Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome

Peer reviewe

Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

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.Peer reviewe

Isotropic non-white matter partial volume effects in constrained spherical deconvolution

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 DVV 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 G M interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (similar to 30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD

Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate–thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left–right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate

Oral symptoms and oral health-related quality of life in patients with chronic kidney disease from predialysis to posttransplantation

ObjectiveThis prospective follow-up cohort study analyzed chronic kidney disease (CKD) patients' oral symptoms, health habits, and oral health-related quality of life (OHRQoL), from predialysis to posttransplantation. A simplified questionnaire method (Oral Health Quality Score, OHQS), based on these and clinical findings, was constructed and tested for identifying patients in need for referral to a dentist.Material and methodsFifty-three CKD patients were followed up for a mean of 10.3years. Clinical oral, radiological, and salivary examination was performed at baseline and posttransplantation. Total Dental Index (TDI) indicating inflammation was calculated. The patients filled out a questionnaire on symptoms, oral hygiene and health care habits, smoking, alcohol use, and medication. General health-related quality of life was assessed with the 15-dimensional (15D) instrument at posttransplantation. Descriptive and analytical methods were used in statistics.ResultsOHQS significantly correlated with high TDI (p=0.017), number of teeth (p=0.031), and unstimulated salivary flow rate (p=0.001) in transplanted patients. Number of daily medications showed a negative correlation with the OHQS (r=-0.30; p=0.028). The prevalence of oral symptoms was slightly, but not significantly, more common posttransplantation compared with predialysis stage.ConclusionOHQS identified patients with high oral inflammatory score thus confirming our study hypothesis.Clinical relevanceUse of OHQS and measuring salivary flow indicate patients at risk for oral diseases. These markers might be easy to use chair-side also by auxiliary personnel.Peer reviewe

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables. Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.Peer reviewe

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients. Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.Peer reviewe

Reelin Associated With Restricted and Stereotyped Behavior Based on Principal Component Analysis on Autism Diagnostic Interview-Revised

Tämä artikkeli ei ole avattavissa lehden sivuilta, koska linkit ja DOI vievät väärään artikkeliin samoin PDF sen ohessa. Kustantajalle ilmoitettu ja pyydetty korjausta.Abstract Background: Twin and family studies have indicated a strong genetic component in autism spectrum disorders, and genetic studies have revealed highly heterogeneous risk factors. The range and severity of the symptom presentation also vary in the spectrum. Thus, symptom-based phenotypes are putatively more closely related to the underlying biology of autism than the end-state diagnosis. Methods: We performed principal component analysis on Autism Diagnostic Interview-Revised algorithm for 117 Finnish families and 594 families from the Autism Genetic Research Exchange (AGRE). The resulting continuous component scores were used as quantitative phenotypes in family-based association analysis. In addition, K-means clustering was performed to cluster and visualize the results of the PCA. Unaffected siblings were included in the study. Results: The components were interpreted as Social Component (SC), communication component and Restricted and Stereotyped Behavior Component (RSBC). K-means clustering showed that, especially in SC, the range of the symptom severity was increased by the siblings. The association of neuroligin 1 with SC was increased, compared to a previous study where only the end-state diagnosis was used. In RSBC, the range of the symptom severity of siblings overlapped greatly with that of patients, which could explain why no association of reelin was found in previous studies in which only the end-state diagnosis was used, but a significant association of reelin with RSBC was now found in the Finnish families (Bonferroni-corrected p=0.029 for rs362644). Although, the Finnish sample is isolated and genetically very homogeneous, compared to the heterogeneous background of AGRE families, many single-nucleotide polymorphisms in reelin, showed modest association with RSBC in the AGRE sample, too. Conclusions: This study demonstrates how the quantitative phenotypes can affect the association analyses, and yields further support to the use of siblings in the study of complex neuropsychiatric disorders.Peer reviewe

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables.Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits