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

Investigating the Role of Intravoxel Incoherent Motion Diffusion-Weighted Imaging in Evaluating Multiple Sclerosis Lesions

Background: Multiple sclerosis (MS) is a chronic and heterogeneous disease characterized by demyelination and axonal loss and damage. Magnetic resonance imaging (MRI) has been employed to distinguish these changes in various types of MS lesions. Objectives: We aimed to evaluate intravoxel incoherent motion (IVIM) diffusion and perfusion MRI metrics across different brain regions in healthy individuals and various types of MS lesions, including enhanced, non-enhanced, and black hole lesions. Methods: A prospective study included 237 patients with MS (65 males and 172 females) and 29 healthy control participants (25 males and 4 females). The field strength was 1.5 Tesla. The imaging sequences included three-dimensional (3D) T1, 3D fluid-attenuated inversion recovery, two-dimensional (2D) T1, T2-weighted imaging, and 2D diffusion-weighted imaging (DWI) sequences. IVIM-derived parameters-apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f)-were quantified for commonly observed lesion types (2506 lesions from 224 patients with MS, excluding 13 patients due to MRI artifacts or not meeting the diagnostic criteria for RR-MS) and for corresponding brain regions in 29 healthy control participants. A one-way analysis of variance, followed by post-hoc analysis (Tukey's test), was performed to compare mean values between the healthy and MS groups. Receiver operating characteristic curve analyses, including area under the curve, sensitivity, and specificity, were conducted to determine the cutoff values of IVIM parameters for distinguishing between the groups. A p-value of ≤0.05 and 95% confidence intervals were used to report statistical significance and precision, respectively. Results: All IVIM parametric maps in this study discriminated among most MS lesion types. ADC, D, and D* values for MS black hole lesions were significantly higher (p < 0.0001) than those for other MS lesions and healthy controls. ADC, D, and D* maps demonstrated high sensitivity and specificity, whereas f maps exhibited low sensitivity but high specificity. Conclusions: IVIM parameters provide valuable diagnostic and clinical insights by demonstrating high sensitivity and specificity in evaluating different categories of MS lesions.

Regional associations of white matter integrity and neurological, post-traumatic stress disorder and autonomic symptoms in Veterans with and without history of loss of consciousness in mild TBI

Background

Posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) share overlapping symptom presentations and are highly comorbid conditions among Veteran populations. Despite elevated presentations of PTSD after mTBI, mechanisms linking the two are unclear, although both have been associated with alterations in white matter and disruptions in autonomic regulation. The present study aimed to determine if there is regional variability in white matter correlates of symptom severity and autonomic functioning in a mixed sample of Veterans with and without PTSD and/or mTBI (N = 77).

Methods

Diffusion-weighted images were processed to extract fractional anisotropy (FA) values for major white matter structures. The PTSD Checklist-Military version (PCL-M) and Neurobehavioral Symptom Inventory (NSI) were used to determine symptom domains within PTSD and mTBI. Autonomic function was assessed using continuous blood pressure and respiratory sinus arrythmia during a static, standing angle positional test. Mixed-effect models were used to assess the regional specificity of associations between symptom severity and white matter, with FA, global symptom severity (score), and white matter tract (tract) as predictors. Additional interaction terms of symptom domain (i.e., NSI and PCL-M subscales) and loss of consciousness (LoC) were added to evaluate potential moderating effects. A parallel analysis was conducted to explore concordance with autonomic functioning.

Results

Results from the two-way Score × Tract interaction suggested that global symptom severity was associated with FA in the cingulum angular bundle (positive) and uncinate fasciculus (negative) only, without variability by symptom domain. We also found regional specificity in the relationship between FA and autonomic function, such that FA was positively associated with autonomic function in all tracts except the cingulum angular bundle. History of LoC moderated the association for both global symptom severity and autonomic function.

Conclusions

Our findings are consistent with previous literature suggesting that there is significant overlap in the symptom presentation in TBI and PTSD, and white matter variability associated with LoC in mTBI may be associated with increased PTSD-spectra symptoms. Further research on treatment response in patients with both mTBI history and PTSD incorporating imaging and autonomic assessment may be valuable in understanding the role of brain injury in treatment outcomes and inform treatment design.

Implications of fitting a two-compartment model in single-shell diffusion MRI

It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment (f) is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes infhas not been validated. In this work we put aside the biological interpretation offand study the sensitivity implications of fitting single-shell data to a two-compartment model. We identify a nonlinear transformation between the one-compartment model (diffusion tensor imaging, DTI) and a two-compartment model in which the mean diffusivities of both compartments are effectively fixed. While the analytic relationship implies that fitting this two-compartment model does not offer any more information than DTI, it explains why metrics derived from a two-compartment model can exhibit enhanced sensitivity over DTI to certain types of WM processes, such as age-related WM differences. The sensitivity enhancement should not be viewed as a substitute for acquiring multi-shell data. Rather, the results of this study provide insight into the consequences of choosing a two-compartment model when only single-shell data is available.

Improved prediction of glioma-related aphasia by diffusion MRI metrics, machine learning, and automated fiber bundle segmentation

White matter impairments caused by gliomas can lead to functional disorders. In this study, we predicted aphasia in patients with gliomas infiltrating the language network using machine learning methods. We included 78 patients with left-hemispheric perisylvian gliomas. Aphasia was graded preoperatively using the Aachen aphasia test (AAT). Subsequently, we created bundle segmentations based on automatically generated tract orientation mappings using TractSeg. To prepare the input for the support vector machine (SVM), we first preselected aphasia-related fiber bundles based on the associations between relative tract volumes and AAT subtests. In addition, diffusion magnetic resonance imaging (dMRI)-based metrics [axial diffusivity (AD), apparent diffusion coefficient (ADC), fractional anisotropy (FA), and radial diffusivity (RD)] were extracted within the fiber bundles' masks with their mean, standard deviation, kurtosis, and skewness values. Our model consisted of random forest-based feature selection followed by an SVM. The best model performance achieved 81% accuracy (specificity = 85%, sensitivity = 73%, and AUC = 85%) using dMRI-based features, demographics, tumor WHO grade, tumor location, and relative tract volumes. The most effective features resulted from the arcuate fasciculus (AF), middle longitudinal fasciculus (MLF), and inferior fronto-occipital fasciculus (IFOF). The most effective dMRI-based metrics were FA, ADC, and AD. We achieved a prediction of aphasia using dMRI-based features and demonstrated that AF, IFOF, and MLF were the most important fiber bundles for predicting aphasia in this cohort.

The Prediction of Neurological Prognosis for Cervical Spondylotic Myelopathy Using Diffusion Tensor Imaging

Objective: Although cervical spondylotic myelopathy (CSM) can be easily diagnosed using magnetic resonance imaging (MRI), prediction of surgical effect using preoperative radiological examinations remains difficult. In previous studies, it was reported that diffusion tensor imaging (DTI) may be used for the prediction of surgical effect; however, these studies did not consider the influences of spinal cord compression even though the values of DTI indexes can be distorted by compressive lesions in patients with CSM. Therefore, it is uncertain whether preoperative DTI indexes can actually predict the surgical effect. The aim of this study was to investigate DTI metrics that are hardly affected by spinal cord compression and can accurately predict neurological status after decompressive surgery.Methods: Twenty-one patients with CSM who underwent surgery and 10 healthy volunteers were enrolled in this study. The subjects underwent cervical MRI, and values of DTI indexes including axial diffusivity (AD), radial diffusivity (RD), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) were recorded at each intervertebral level. Further, the Japanese Orthopaedic Association (JOA) score of each patient with CSM was recorded before and after surgery for neurological status evaluation. Preoperative and postoperative values of DTI indexes were compared, and correlations between preoperative DTI parameters and postoperative neurological recovery were assessed.Results: After surgery, the lesion-adjacent (LA) ratios of RD and ADC increased (p = 0.04 and p = 0.062, respectively), while the LA ratio of FA decreased (p = 0.075). In contrast, the LA ratio of AD hardly changed. A negative correlation was observed between preoperative LA ratio of AD and JOA recovery rate 6 months after surgery (r = -0.379, p = 0.091). Based on preoperative LA ratio of AD, the patients were divided into a low AD group and a high AD group, and JOA recovery rate 6 months after surgery was found to be higher in the low AD group than in the high AD group (p = 0.024).Conclusion: In patients with CSM, preoperative LA ratio of AD is seldom affected by spinal cord compression, and it negatively correlates with JOA recovery rate 6 months after surgery.

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

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.

Structural brain connectivity correlates with outcome in mild traumatic brain injury

We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.

Hyperarousal symptoms and decreased right hemispheric frontolimbic white matter integrity predict poorer sleep quality in combat-exposed veterans

OBJECTIVE

Disrupted sleep is common following combat deployment. Contributors to risk include posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI); however, the mechanisms linking PTSD, mTBI, and sleep are unclear. Both PTSD and mTBI affect frontolimbic white matter tracts, such as the uncinate fasciculus. The current study examined the relationship between PTSD symptom presentation, lateralized uncinate fasciculus integrity, and sleep quality.

METHOD

Participants include 42 combat veterans with and without PTSD and mTBI. Freesurfer and Tracula were used to establish specific white matter ROI integrity via 3-T MRI. The Pittsburgh Sleep Quality Index and PTSD Checklist were used to assess sleep quality and PTSD symptoms.

RESULTS

Decreased fractional anisotropy in the right uncinate fasciculus (β = -1.11, SE = 0.47, p < .05) and increased hyperarousal symptom severity (β = 3.50, SE = 0.86, p < .001) were associated with poorer sleep quality.

CONCLUSION

Both right uncinate integrity and hyperarousal symptom severity are associated withsleep quality in combat veterans. The right uncinate is a key regulator of limbic behavior and sympathetic nervous system reactivity, a core component of hyperarousal. Damage to this pathway may be one mechanism by which mTBI and/or PTSD could create vulnerability for sleep problems following combat deployment.

Cortical Surfaces Integration with Tractography for Structural Connectivity Analysis

Background: Mapping diffusion MRI tractography streamlines to the cortical surface facilitates the integration of white matter features onto gray matter, especially for connectivity analysis. Method: In this work, we present methods that combine cortical surface meshes with tractography reconstruction to improve endpoint precision and coverage. This cortical mapping also enables the study of structural measures from tractography along the cortex and subcortical structures. In addition to structural connectivity analysis, novel adaptive and dynamic surface seeding methods are proposed. These improvements are made by incorporating cortical maps such as endpoint density. Results: The proposed dynamic surface seeding increases the cortical coverage and reduces endpoint location biases. Our results suggest that the use of cortical and subcortical meshes together with a proper seeding strategy can reduce the variability in structural connectivity analysis. Conclusion: The proposed adaptive and dynamic seeding utilize cortical maps to better distribute tractography interconnections, thus increasing cortical coverage and reducing endpoint bias. This also facilitates the analysis of white matter & diffusion MRI features along the cortex, combined with cortical measures or functional activation. Impact statement This research presents an overview of surface mapping methods for tractography to reduce structural connectivity variability. The proposed adaptive and dynamic seeding utilize cortical maps to better distribute tractography interconnections, thus increasing cortical coverage and reducing end-point bias. This also facilitates the analysis of white matter and diffusion magnetic resonance imaging features along the cortex, combined with cortical measures or functional activation.

Detecting Corticospinal Tract Impairment in Tumor Patients With Fiber Density and Tensor-Based Metrics

Tumors infiltrating the motor system lead to significant disability, often caused by corticospinal tract injury. The delineation of the healthy-pathological white matter (WM) interface area, for which diffusion magnetic resonance imaging (dMRI) has shown promising potential, may improve treatment outcome. However, up to 90% of white matter (WM) voxels include multiple fiber populations, which cannot be correctly described with traditional metrics such as fractional anisotropy (FA) or apparent diffusion coefficient (ADC). Here, we used a novel fixel-based along-tract analysis consisting of constrained spherical deconvolution (CSD)-based probabilistic tractography and fixel-based apparent fiber density (FD), capable of identifying fiber orientation specific microstructural metrics. We addressed this novel methodology's capability to detect corticospinal tract impairment. We measured and compared tractogram-related FD and traditional microstructural metrics bihemispherically in 65 patients with WHO grade III and IV gliomas infiltrating the motor system. The cortical tractogram seeds were based on motor maps derived by transcranial magnetic stimulation. We extracted 100 equally distributed cross-sections along each streamline of corticospinal tract (CST) for along-tract statistical analysis. Cross-sections were then analyzed to detect differences between healthy and pathological hemispheres. All metrics showed significant differences between healthy and pathologic hemispheres over the entire tract and between peritumoral segments. Peritumoral values were lower for FA and FD, but higher for ADC within the entire cohort. FD was more specific to tumor-induced changes in CST than ADC or FA, whereas ADC and FA showed higher sensitivity. The bihemispheric along-tract analysis provides an approach to detect subject-specific structural changes in healthy and pathological WM. In the current clinical dataset, the more complex FD metrics did not outperform FA and ADC in terms of describing corticospinal tract impairment.

Alterations in Microstructure and Local Fiber Orientation of White Matter Are Associated with Outcome after Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) can have long-lasting consequences. We investigated white matter (WM) alterations at 6-12 months following mTBI using diffusion tensor imaging (DTI) and assessed if the alterations associate with outcome. Eighty-five patients with mTBI underwent diffusion-weighted magnetic resonance imaging (MRI) on average 8 months post-injury and patients' outcome was assessed at the time of imaging using the Glasgow Outcome Scale-Extended (GOS-E). Additionally, 30 age-matched patients with extracranial orthopedic injuries were used as control subjects. Voxel-wise analysis of the data was performed using a tract-based spatial statistics (TBSS) approach and differences in microstructural metrics between groups were investigated. Further, the susceptibility of the abnormalities to specific fiber orientations was investigated by analyzing the first eigenvector of the diffusion tensor in the voxels with significant differences. We found significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD) and radial diffusivity (RD) in patients with mTBI compared with control subjects, whereas no significant differences were observed in axial diffusivity (AD) between the groups. The differences were present bilaterally in several WM regions and correlated with outcome. Moreover, multiple clusters were found in the principal fiber orientations of the significant voxels in anisotropy, and similar orientation patterns were found for the diffusivity metrics. These directional clusters correlated with patients' functional outcome. Our study showed that mTBI is associated with WM changes at the chronic stage and these alterations occur in several WM regions. In addition, several significant clusters of WM alterations in specific fiber orientations were found and these clusters were associated with outcome.

Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy

Breast cancer is the leading cancer among women worldwide, and a high number of breast cancer patients are struggling with psychological and cognitive disorders. In this study, we aim to use machine learning models to discriminate between chemo-brain participants and healthy controls (HCs) using connectomes (connectivity matrices) and topological coefficients. Nineteen female post-chemotherapy breast cancer (BC) survivors and 20 female HCs were recruited for this study. Participants in both groups received resting-state functional magnetic resonance imaging (rs-fMRI) and generalized q-sampling imaging (GQI). Logistic regression (LR), decision tree classifier (CART), and xgboost (XGB) were the models we adopted for classification. In connectome analysis, LR achieved an accuracy of 79.49% with the functional connectomes and an accuracy of 71.05% with the structural connectomes. In the topological coefficient analysis, accuracies of 87.18%, 82.05%, and 83.78% were obtained by the functional global efficiency with CART, the functional global efficiency with XGB, and the structural transitivity with CART, respectively. The areas under the curves (AUCs) were 0.93, 0.94, 0.87, 0.88, and 0.84, respectively. Our study showed the discriminating ability of functional connectomes, structural connectomes, and global efficiency. We hope our findings can contribute to an understanding of the chemo brain and the establishment of a clinical system for tracking chemo brain.

Quantitative analyses of high-angular resolution diffusion imaging (HARDI)-derived long association fibers in children with sensorineural hearing loss

Sensorineural hearing loss (SNHL) is the most common developmental sensory disorder due to a loss of function within the inner ear or its connections to the brain. While successful intervention for auditory deprivation with hearing amplification and cochlear implants during a sensitive early developmental period can improve spoken-language outcomes, SNHL patients can suffer several cognitive dysfunctions including executive function deficits, visual cognitive impairment, and abnormal visual dominance in speaking perception even after successful intervention. To evaluate whether long association fibers are involved in the pathogenesis of impairment on the extra-auditory cognitive process in SNHL participants, we quantitatively analyzed high-angular resolution diffusion imaging (HARDI) tractography-derived fibers in participants with SNHL. After excluding cases with congenital disorders, perinatal brain damage, or premature birth, we enrolled 17 participants with SNHL aged under 10 years old. Callosal pathways (CP) and six types of cortico-cortical association fibers (arcuate fasciculus [AF], inferior longitudinal fasciculus [ILF], inferior fronto-occipital fasciculus [IFOF], uncinate fasciculus [UF], cingulum fasciculus [CF], and fornix [Fx]) in both hemispheres were identified and visualized. The ILF and IFOF were partly undetected in three profound SNHL participants. Compared to age- and gender-matched neurotypical controls (NC), decreased volumes, increased lengths, and high apparent diffusion coefficient (ADC) values without difference in fractional anisotropy (FA) values were identified in multiple types of fibers in the SNHL group. The impairment of long association fibers in SNHL may partly be related to the association of cognitive dysfunction with SNHL.

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data

PURPOSE

Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated.

METHODS

This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters.

RESULTS

The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort.

CONCLUSION

The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Decreased Value of Highly Accurate Fractional Anisotropy Using 3-Tesla ZOOM Diffusion Tensor Imaging After Decompressive Surgery in Patients with Cervical Spondylotic Myelopathy: Aligned Fibers Effect

Background

Diffusion tensor imaging (DTI) is widely used; however, most of the prior studies have resulted in presurgical decreased fractional anisotropy (FA) values in patients with cervical spondylotic myelopathy (CSM). We used ZOOM DTI and could acquire highly accurate FA values during perioperative periods, which indicated different insights than preceding studies. The objective of this study was to assess the perioperative FA change in patients with CSM and determine the prognostic factor.

Methods

Twenty-eight patients with CSM and healthy control subjects were enrolled in this study. Twenty patients (71%) had intracordal high intensity before surgery. All patients underwent decompressive surgery. ZOOM DTI and the Japanese Orthopaedic Association (JOA) assessment were performed before and after surgery. The region of interest was manually contoured to omit the surrounding cerebrospinal fluid. The axial plane of the most stenotic cervical level was assessed.

Results

FA values before surgery and at 1 week after surgery, and FA values at 1 week after surgery and at 6 months after surgery differed significantly as determined. The FA values of patients with intracordal high intensity significantly decreased after surgery and significantly increased from 1 week to 6 months, whereas those of patients without intracordal high intensity did not significantly change. JOA scores at 6 months after surgery (13.1) improved significantly compared with JOA scores before surgery (10.8). Only FA values at 1 week after surgery had a significant positive relationship with JOA scores presurgery and at 6 months after surgery.

Conclusions

The presurgical FA value in patients with CSM did not differ from that of normal control subjects, but significantly decreased after surgery, and significantly increased 6 months after surgery. We concluded that the postsurgical FA value approximates the proper state of the damaged cord and the presurgical FA value includes a masked effect as an aligned fiber effect because of compression by degenerative construction. Only the FA value at 1 week had a significant positive relationship with the JOA score presugery and at 6 months, which established that the postsurgical FA value may be a more accurate prognostic factor than the presurgical FA value.

Modelling white matter with spherical deconvolution: How and why?

Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non-invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.

Probing Brain Micro-architecture by Orientation Distribution Invariant Identification of Diffusion Compartments

Precise quantification of brain tissue micro-architecture using diffusion MRI is hampered by the conflation of diffusion-attenuated signals from micro-environments that can be orientationally heterogeneous due to complex fiber configurations, such as crossing, fanning, and bending, and compartmentally heterogeneous due to variability in tissue organization. In this paper, we introduce a method, called Spherical Mean Spectrum Imaging (SMSI), for quantification of tissue microstructure. SMSI does not assume a fixed number of compartments, but characterizes the signal as a spectrum of fine- to coarse-scale diffusion processes. Using SMSI, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy (μFA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that SMSI is fast, accurate, and can overcome biases in state-of-the-art microstructure models. We demonstrate its application in probing microstructural changes in the baby brain during the first two years of life.

Highly Accurate Analysis of the Cervical Neural Tract of the Elderly Using ZOOM DTI

Background/Aims

To investigate the fractional anisotropy (FA) values of the cervical spinal cord in elderly individuals using zonally magnified oblique multislice (ZOOM) diffusion tensor imaging (DTI).

Methods

Fourteen healthy elderly volunteers (group E) and 10 young volunteers (group Y) were enrolled. We assessed the FA, apparent diffusion coefficient (ADC), and λ₁–λ₃ values using 3-T magnetic resonance imaging. The region of interest was contoured entirely inside the spinal cord, with no gray/white matter distinction, in order to avoid including the cerebrospinal fluid.

Results

As lower cervical levels were approached, the FA values gradually decreased, while the ADC values increased. The mean FA values at each cervical level were as follows in groups E and Y: 0.71 and 0.70 at the C2/3 level, 0.66 and 0.66 at the C3/4 level, 0.63 and 0.62 at the C4/5 level, 0.57 and 0.57 at the C5/6 level, and 0.58 and 0.57 at the C6/7 level, respectively. The mean ADC values in groups E and Y were 1.06 and 0.99 at the C2/3 level, 1.05 and 1.06 at the C3/4 level, 1.14 and 1.06 at the C4/5 level, 1.18 and 1.21 at the C5/6 level, and 1.39 and 1.46 at the C6/7 level, respectively. There were no significant differences between the elderly and young participants.

Conclusion

In both asymptomatic elderly and young individuals, the FA values gradually decreased and the ADC values increased moving towards lower cervical levels. Age did not affect the FA values, even though mild cord compression was evident due to spondylotic changes. ZOOM DTI has the potential to provide more information than conventional DTI.

The Reliability of Reduced Field-of-view DTI for Highly Accurate Quantitative Assessment of Cervical Spinal Cord Tracts

Purpose:

To compare the accuracy of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values between reduced FOV or so-called zonally oblique multislice (ZOOM) and conventional diffusion tensor imaging (DTI) in the cervical spinal cord.

Methods:

Both ZOOM and conventional DTI were performed on 10 healthy volunteers. Intraclass correlation coefficient (ICC) was used to evaluate the reliability of the measurements obtained. Four radiologists evaluated the FA and ADC values at each cervical cord level and classified the visibility by 4 ranks. The geometric distortion ratios of the long axis and short axis were compared between ZOOM and conventional DTI. The imaging parameters were as follows: b-value = 600 s/mm²; TR = 4500 ms; TE = 81 ms; FOV = 70 × 47 mm² / 200 × 200 mm²; matrix = 80 × 51 / 128 × 126 (ZOOM and conventional DTI, respectively). The region of interest was carefully drawn inside the spinal cord margin to exclude the spinal cord component, without excluding the white matter fiber tracts.

Results:

The average FA value decreased in both ZOOM and conventional DTI in lower spinal cord levels; in contrast, the ADC value increased in lower spinal cord levels. Zonally oblique multislice DTI was superior to conventional DTI with regard to inter-rater and intra-rater reliability; further, visibility was better and the standard deviation was smaller in ZOOM DTI. On both the long and short axis, the geometric distortion ratio was lower in ZOOM DTI at all cervical spinal cord levels compared with the conventional DTI. There was a significant difference in the distortion ratios of the long and short axis between ZOOM and conventional DTI.

Conclusion:

Conventional DTI is unreliable owing to its susceptibility to the surrounding magnetic field. ZOOM DTI is reliable for performing highly accurate evaluations.

Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls. Mapping was also performed by deterministic single-shell, single tissue (SSST)-CSD tracking and probabilistic SSST-CSD tracking for comparison. A support vector machine was trained to predict diagnosis based on a linear combination of graph metrics. We showed that probabilistic MSMT-CSD could detect significantly reduced global strength, efficiency, clustering, and small-worldness, and increased global path length in patients with PD relative to healthy controls; by contrast, probabilistic SSST-CSD only detected the difference in global strength and small-worldness. In patients with PD, probabilistic MSMT-CSD also detected a significant reduction in local efficiency and detected clustering in the motor, frontal temporoparietal associative, limbic, basal ganglia, and thalamic areas. The network-based statistic identified a subnetwork of reduced connectivity by MSMT-CSD and probabilistic SSST-CSD in patients with PD, involving key components of the cortico–basal ganglia–thalamocortical network. Finally, probabilistic MSMT-CSD had superior diagnostic accuracy compared with conventional probabilistic SSST-CSD and deterministic SSST-CSD tracking. In conclusion, probabilistic MSMT-CSD detected a greater extent of connectome pathology in patients with PD, including those with cortico–basal ganglia–thalamocortical network disruptions. Connectome analysis based on probabilistic MSMT-CSD may be useful when evaluating the extent of white matter connectivity disruptions in PD.

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Connectomes mapped in Parkinson's disease (PD) using multi-shell tractography.

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Multi-shell tractography provided improved sensitivity to connectome pathology.

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Machine learning accurately predicted PD diagnosis based on connectome.

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Connectome pathology in PD was localized to basal ganglia-thalamocortical circuits.

Delineation of the visual pathway in paediatric optic pathway glioma patients using probabilistic tractography, and correlations with visual acuity

Background

Radiological biomarkers which correlate with visual function are needed to improve the clinical management of optic pathway glioma (OPG) patients. Currently, these are not available using conventional magnetic resonance imaging (MRI) sequences. The aim of this study was to determine whether diffusion MRI could be used to delineate the entire optic pathway in OPG patients, and provide imaging biomarkers within this pathway which correlate with a patient's visual acuity (VA).

Methods

Multi-shell diffusion MRI data were acquired in a cohort of paediatric OPG patients, along with VA measurements in each eye. Diffusion MRI data were processed using constrained spherical deconvolution and probabilistic fibre tractography, to delineate the white matter bundles forming the optic pathway in each patient. Median fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were measured in the optic nerves, tracts, and radiations, and correlated against each patient's VA.

Results

In the optic nerves, median FA significantly correlated with VA (R²_adj = 0.31, p = 0.0082), with lower FA associated with poorer vision. In the optic radiations, both lower FA and higher ADC were significantly associated with poorer vision (R²_adj = 0.52, p = 0.00075 and R²_adj = 0.50, p = 0.0012 respectively). No significant correlations between VA and either FA or ADC were found in the optic tracts.

Conclusions

Multi-shell diffusion MRI provides in vivo delineation of the optic pathway in OPG patients, despite the presence of tumour invasion. This technique provides imaging biomarkers which are sensitive to microstructural damage to the underlying white matter in this pathway, which is not always visible on conventional MRI.

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Diffusion MRI can delineate the entire visual pathway in optic pathway glioma patients.

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Decreased FA in the optic nerves and radiations is associated with poorer vision.

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This provides sub-clinical biomarkers of structural damage to the visual pathway.

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These biomarkers correlate strongly with a patient's visual acuity.

The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density

PURPOSE

To investigate whether diffusion MRI can be used to study cortical segregation based on a contrast related to neurite density, thus providing a complementary tool to myelin-based MRI techniques used for myeloarchitecture.

METHODS

Several myelin-sensitive MRI methods (e.g., based on T₁ , T₂ , and T2*) have been proposed to parcellate cortical areas based on their myeloarchitecture. Recent improvements in hardware, acquisition, and analysis methods have opened the possibility of achieving a more robust characterization of cortical microstructure using diffusion MRI. High-quality diffusion MRI data from the Human Connectome Project was combined with recent advances in fiber orientation modeling. The orientational average of the fiber orientation distribution was used as a summary parameter, which was displayed as inflated brain surface views.

RESULTS

Diffusion MRI identifies cortical patterns consistent with those previously seen by MRI methods used for studying myeloarchitecture, which have shown patterns of high myelination in the sensorimotor strip, visual cortex, and auditory areas and low myelination in frontal and anterior temporal areas.

CONCLUSION

In vivo human diffusion MRI provides a useful complementary noninvasive approach to myelin-based methods used to study whole-brain cortical parcellation, by exploiting a contrast based on tissue microstructure related to neurite density, rather than myelin itself. Magn Reson Med 79:2738-2744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways

Conventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). High b-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of "microstructure-informed" whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm^-2 at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-exponential tensor evaluation becomes too high due to decreased anisotropy of low b-value diffusion in these areas. Benefits can be expected in assessment of the residual axonal integrity in tissues affected by various pathological conditions, in surgical planning, and in evaluation of cortical connectivity, in particular, between Brodmann's areas.

Improved tractography using asymmetric fibre orientation distributions

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and -x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.

Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI

The question of whether there are biological differences between male and female brains is a fraught one, and political positions and prior expectations seem to have a strong influence on the interpretation of scientific data in this field. This question is relevant to issues of gender differences in the prevalence of psychiatric conditions, including autism, attention deficit hyperactivity disorder (ADHD), Tourette's syndrome, schizophrenia, dyslexia, depression, and eating disorders. Understanding how gender influences vulnerability to these conditions is significant. Diffusion magnetic resonance imaging (dMRI) provides a non-invasive method to investigate brain microstructure and the integrity of anatomical connectivity. Generalized q-sampling imaging (GQI) has been proposed to characterize complicated fiber patterns and distinguish fiber orientations, providing an opportunity for more accurate, higher-order descriptions through the water diffusion process. Therefore, we aimed to investigate differences in the brain's structural network between teenage males and females using GQI. This study included 59 (i.e., 33 males and 26 females) age- and education-matched subjects (age range: 13 to 14 years). The structural connectome was obtained by graph theoretical and network-based statistical (NBS) analyses. Our findings show that teenage male brains exhibit better intrahemispheric communication, and teenage female brains exhibit better interhemispheric communication. Our results also suggest that the network organization of teenage male brains is more local, more segregated, and more similar to small-world networks than teenage female brains. We conclude that the use of an MRI study with a GQI-based structural connectomic approach like ours presents novel insights into network-based systems of the brain and provides a new piece of the puzzle regarding gender differences.

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The GQI-based structural connectomic study provides a new piece of the puzzle regarding gender differences.

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Male brains exhibit better intrahemispheric communication, and female exhibit better interhemispheric communication.

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The network organization of teenage male brains is more local and more segregated than teenage female brains.

Fornix Under Water? Ventricular Enlargement Biases Forniceal Diffusion Magnetic Resonance Imaging Indices in Anorexia Nervosa

BACKGROUND

Acute anorexia nervosa (AN) is characterized by reduced brain mass and corresponding increased sulcal and ventricular cerebrospinal fluid. Recent studies of white matter using diffusion tensor imaging consistently identified alterations in the fornix, such as reduced fractional anisotropy (FA). However, because the fornix penetrates the ventricles, it is prone to cerebrospinal fluid-induced partial volume effects that interfere with a valid assessment of FA. We investigated the hypothesis that in the acute stage of AN, FA of the fornix is markedly affected by ventricular volumes.

METHODS

First, using diffusion tensor imaging data we established the inverse associations between forniceal FA and volumes of the third and lateral ventricles in a prestudy with 32 healthy subjects to demonstrate the strength of ventricular influence on forniceal FA independent of AN. Second, we investigated a sample of 25 acute AN patients and 25 healthy control subjects.

RESULTS

Using ventricular volumes as covariates markedly reduced the group effect of forniceal FA, even with tract-based spatial statistics focusing only on the center of the fornix. In addition, after correcting for free water on voxel level, the group differences in forniceal FA between AN patients and controls disappeared completely.

CONCLUSIONS

It is unlikely that microstructural changes affecting FA occurred in the fornix of AN patients. Previously identified alterations in acute AN may have been biased by partial volume effects and the proposed central role of this structure in the pathophysiology may need to be reconsidered. Future studies on white matter alterations in AN should carefully deal with partial volume effects.

Convexity-constrained and nonnegativity-constrained spherical factorization in diffusion-weighted imaging

Diffusion-weighted imaging (DWI) facilitates probing neural tissue structure non-invasively by measuring its hindrance to water diffusion. Analysis of DWI is typically based on generative signal models for given tissue geometry and microstructural properties. In this work, we generalize multi-tissue spherical deconvolution to a blind source separation problem under convexity and nonnegativity constraints. This spherical factorization approach decomposes multi-shell DWI data, represented in the basis of spherical harmonics, into tissue-specific orientation distribution functions and corresponding response functions, without assuming the latter as known thus fully unsupervised. In healthy human brain data, the resulting components are associated with white matter fibres, grey matter, and cerebrospinal fluid. The factorization results are on par with state-of-the-art supervised methods, as demonstrated also in Monte-Carlo simulations evaluating accuracy and precision of the estimated response functions and orientation distribution functions of each component. In animal data and in the presence of oedema, the proposed factorization is able to recover unseen tissue structure, solely relying on DWI. As such, our method broadens the applicability of spherical deconvolution techniques to exploratory analysis of tissue structure in data where priors are uncertain or hard to define.

A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals

Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy.

High angular resolution diffusion-weighted imaging in mild traumatic brain injury

We sought to investigate white matter abnormalities in mild traumatic brain injury (mTBI) using diffusion-weighted magnetic resonance imaging (DW-MRI). We applied a global approach based on tract-based spatial statistics skeleton as well as constrained spherical deconvolution tractography. DW-MRI was performed on 102 patients with mTBI within two months post-injury and 30 control subjects. A robust global approach considering only the voxels with a single-fiber configuration was used in addition to global analysis of the tract skeleton and probabilistic whole-brain tractography. In addition, we assessed whether the microstructural parameters correlated with age, time from injury, patient's outcome and white matter MRI hyperintensities. We found that whole-brain global approach restricted to single-fiber voxels showed significantly decreased fractional anisotropy (FA) (p = 0.002) and increased radial diffusivity (p = 0.011) in patients with mTBI compared with controls. The results restricted to single-fiber voxels were more significant and reproducible than those with the complete tract skeleton or the whole-brain tractography. FA correlated with patient outcomes, white matter hyperintensities and age. No correlation was observed between FA and time of scan post-injury. In conclusion, the global approach could be a promising imaging biomarker to detect white matter abnormalities following traumatic brain injury.

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity

In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model the diffusion signal as the contribution of three tissue compartments. NODDI microstructural indices, such as intra-cellular volume fraction (ICVF) and orientation dispersion index (ODI) are directly related to neuronal density and orientation dispersion, respectively. One way of examining the neurophysiological role of these microstructural indices across neuronal fibres is to look into how they relate to brain function. Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional connectivity measured with simultaneous EEG-fMRI. Our results reveal distinct structural fingerprints for each microstructural index that also reflect their inter-relationships.

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data

Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractography-based grey matter parcellation can be investigated as well.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0058-4) contains supplementary material, which is available to authorized users.

DNA-gadolinium-gold nanoparticles for in vivo T1 MR imaging of transplanted human neural stem cells

The unambiguous imaging of transplanted cells remains a major challenge to understand their biological function and therapeutic efficacy. In vivo imaging of implanted cells is reliant on tagging these to differentiate them from host tissue, such as the brain. We here characterize a gold nanoparticle conjugate that is functionalized with modified deoxythymidine oligonucleotides bearing Gd(III) chelates and a red fluorescent Cy3 moiety to visualize in vivo transplanted human neural stem cells. This DNA-Gd@Au nanoparticle (DNA-Gd@AuNP) exhibits an improved T1 relaxivity and excellent cell uptake. No significant effects of cell uptake have been found on essential cell functions. Although T1 relaxivity is attenuated within cells, it is sufficiently preserved to afford the in vivo detection of transplanted cells using an optimized voxel size. In vivo MR images were corroborated by a post-mortem histological verification of DNA-Gd@AuNPs in transplanted cells. With 70% of cells being correctly identified using the DNA-Gd-AuNPs indicates an overall reliable detection. Less than 1% of cells were false positive for DNA-Gd@AuNPs, but a significant number of 30% false negatives reveals a dramatic underestimation of transplanted cells using this approach. DNA-Gd@AuNPs therefore offer new opportunities to visualize transplanted cells unequivocally using T1 contrast and use cellular MRI as a tool to derive biologically relevant information that allows us to understand how the survival and location of implanted cells determines therapeutic efficacy.

Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution

We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first method we use the enhancement PDE for contextual regularization of a fiber orientation distribution (FOD) that is obtained on individual voxels from high angular resolution diffusion imaging (HARDI) data via constrained spherical deconvolution (CSD). Thereby we improve the FOD as input for subsequent tractography. Secondly, we introduce the fiber to bundle coherence (FBC), a measure for quantification of fiber alignment. The FBC is computed from a tractography result using the same PDE framework and provides a criterion for removing the spurious fibers. We validate the proposed combination of CSD and enhancement on phantom data and on human data, acquired with different scanning protocols. On the phantom data we find that PDE enhancements improve both local metrics and global metrics of tractography results, compared to CSD without enhancements. On the human data we show that the enhancements allow for a better reconstruction of crossing fiber bundles and they reduce the variability of the tractography output with respect to the acquisition parameters. Finally, we show that both the enhancement of the FODs and the use of the FBC measure on the tractography improve the stability with respect to different stochastic realizations of probabilistic tractography. This is shown in a clinical application: the reconstruction of the optic radiation for epilepsy surgery planning.

Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study

In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22-71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases.

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

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.