Evaluation of white matter microstructure in pediatric onset multiple sclerosis with diffusion compartment imaging.

BACKGROUND AND PURPOSE: Pediatric-onset multiple sclerosis (POMS) shows earlier axonal involvement and greater axonal loss than in adults. We aim to characterize the white matter (WM) microstructural changes in POMS using a diffusion compartment imaging (DCI) model and compare it to standard diffusion tensor imaging (DTI). METHODS: Eleven patients (2 males, mean age 18.8 ± 3.9 years) with a diagnosis of relapsing and remitting POMS (mean age at disease onset 13.8 ± 2.9 years, mean duration 5.1 ± 1.9 years) and healthy controls (8 males, mean age 26.4 ± 6.5 years) were recruited and imaged at 3 T. A 90-gradient set Cube and Sphere acquisition and a novel DCI model known as DIstribution of Anisotropic MicrOstructural eNvironments with Diffusion-weighted imaging (DIAMOND) were used to calculate a single anisotropic compartment, an isotropic compartment, and a free diffusion compartment. Lesions and contralateral normal-appearing white matter (NAWM) in patients and whole brain WM for controls were labeled. RESULTS: Eleven patients and 11 controls were recruited. When comparing lesions and contralateral NAWM in patients using DCI, compartmental axial diffusivity, radial diffusivity (cRD), and mean diffusivity (cMD) were higher in lesions. Conversely, compartmental fractional anisotropy (cFA) and heterogeneity index were lower in lesions. An analysis of DTI equivalents showed the same trends. In whole-brain NAWM of patients compared to controls, cRD and cMD were higher and cFA was lower in patients. CONCLUSION: Lesions in POMS can be accurately characterized by a DCI model. Incipient changes in NAWM seen in DCI may not be readily observable by DTI.

Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding.

Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.

Tuber Locations Associated with Infantile Spasms Map to a Common Brain Network.

OBJECTIVE: Approximately 50% of patients with tuberous sclerosis complex develop infantile spasms, a sudden onset epilepsy syndrome associated with poor neurological outcomes. An increased burden of tubers confers an elevated risk of infantile spasms, but it remains unknown whether some tuber locations confer higher risk than others. Here, we test whether tuber location and connectivity are associated with infantile spasms. METHODS: We segmented tubers from 123 children with (n = 74) and without (n = 49) infantile spasms from a prospective observational cohort. We used voxelwise lesion symptom mapping to test for an association between spasms and tuber location. We then used lesion network mapping to test for an association between spasms and connectivity with tuber locations. Finally, we tested the discriminability of identified associations with logistic regression and cross-validation as well as statistical mediation. RESULTS: Tuber locations associated with infantile spasms were heterogenous, and no single location was significantly associated with spasms. However, >95% of tuber locations associated with spasms were functionally connected to the globi pallidi and cerebellar vermis. These connections were specific compared to tubers in patients without spasms. Logistic regression found that globus pallidus connectivity was a stronger predictor of spasms (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.10-3.50, p = 0.02) than tuber burden (OR = 1.65, 95% CI = 0.90-3.04, p = 0.11), with a mean receiver operating characteristic area under the curve of 0.73 (±0.1) during repeated cross-validation. INTERPRETATION: Connectivity between tuber locations and the bilateral globi pallidi is associated with infantile spasms. Our findings lend insight into spasm pathophysiology and may identify patients at risk. ANN NEUROL 2021;89:726-739.

A structural brain network of genetic vulnerability to psychiatric illness.

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.

Myofiber organization in the failing systemic right ventricle.

BACKGROUND: The right ventricle (RV) often fails when functioning as the systemic ventricle, but the cause is not understood. We tested the hypothesis that myofiber organization is abnormal in the failing systemic right ventricle. METHODS: We used diffusion-weighted cardiovascular magnetic resonance imaging to examine 3 failing hearts explanted from young patients with a systemic RV and one structurally normal heart with postnatally acquired RV hypertrophy for comparison. Diffusion compartment imaging was computed to separate the free diffusive component representing free water from an anisotropic component characterizing the orientation and diffusion characteristics of myofibers. The orientation of each anisotropic compartment was displayed in glyph format and used for qualitative description of myofibers and for construction of tractograms. The helix angle was calculated across the ventricular walls in 5 locations and displayed graphically. Scalar parameters (fractional anisotropy and mean diffusivity) were compared among specimens. RESULTS: The hypertrophied systemic RV has an inner layer, comprising about 2/3 of the wall, composed of hypertrophied trabeculae and an epicardial layer of circumferential myofibers. Myofibers within smaller trabeculae are aligned and organized with parallel fibers while larger, composite bundles show marked disarray, largely between component trabeculae. We observed a narrow range of helix angles in the outer, compact part of the wall consistent with aligned, approximately circumferential fibers. However, there was marked variation of helix angle in the inner, trabecular part of the wall consistent with marked variation in fiber orientation. The apical whorl was disrupted or incomplete and we observed myocardial whorls or vortices at other locations. Fractional anisotropy was lower in abnormal hearts while mean diffusivity was more variable, being higher in 2 but lower in 1 heart, compared to the structurally normal heart. CONCLUSIONS: Myofiber organization is abnormal in the failing systemic RV and might be an important substrate for heart failure and arrhythmia. It is unclear if myofiber disorganization is due to hemodynamic factors, developmental problems, or both.

Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results.

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 â€‹mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.

Diffusion Tensor Imaging Abnormalities in the Uncinate Fasciculus and Inferior Longitudinal Fasciculus in Phelan-McDermid Syndrome.

BACKGROUND: This cohort study utilized diffusion tensor imaging tractography to compare the uncinate fasciculus and inferior longitudinal fasciculus in children with Phelan-McDermid syndrome with age-matched controls and investigated trends between autism spectrum diagnosis and the integrity of the uncinate fasciculus and inferior longitudinal fasciculus white matter tracts. METHODS: This research was conducted under a longitudinal study that aims to map the genotype, phenotype, and natural history of Phelan-McDermid syndrome and identify biomarkers using neuroimaging (ClinicalTrial NCT02461420). Patients were aged three to 21 years and underwent longitudinal neuropsychologic assessment over 24 months. MRI processing and analyses were completed using previously validated image analysis software distributed as the Computational Radiology Kit (http://crl.med.harvard.edu/). Whole-brain connectivity was generated for each subject using a stochastic streamline tractography algorithm, and automatically defined regions of interest were used to map the uncinate fasciculus and inferior longitudinal fasciculus. RESULTS: There were 10 participants (50% male; mean age 11.17 years) with Phelan-McDermid syndrome (n = 8 with autism). Age-matched controls, enrolled in a separate longitudinal study (NIH R01 NS079788), underwent the same neuroimaging protocol. There was a statistically significant decrease in the uncinate fasciculus fractional anisotropy measure and a statistically significant increase in uncinate fasciculus mean diffusivity measure, in the patient group versus controls in both right and left tracts (P ≤ 0.024). CONCLUSION: Because the uncinate fasciculus plays a critical role in social and emotional interaction, this tract may underlie some deficits seen in the Phelan-McDermid syndrome population. These findings need to be replicated in a larger cohort.

Lesion-Constrained Electrical Source Imaging: A Novel Approach in Epilepsy Surgery for Tuberous Sclerosis Complex.

PURPOSE: Electrical source imaging may yield ambiguous results in multilesional epilepsy. The aim of this study was to test the clinical utility of lesion-constrained electrical source imaging in epilepsy surgery in children with tuberous sclerosis complex. METHODS: Lesion-constrained electrical source imaging is a novel method based on a proposed head model in which the source solution is constrained to lesions. Using a goodness of fit analysis, we rank-ordered individual tubers by their ability to approximate interictal and ictal EEG data. The overlap with the surgical resection cavity was determined qualitatively, and placed findings in the context of epilepsy surgical outcome, and compared with the low-resolution brain electromagnetic tomography solution. RESULTS: Low-resolution brain electromagnetic tomography predicted the surgical cavity in only one patient with good outcome (true positive) and localized to outside of the cavity in two patients with a good outcome (false negative). In one patient with a poor outcome, the interictal low-resolution brain electromagnetic tomography solution overlapped with the cavity (false positive). Lesion-constrained electrical source imaging of ictal EEG data identified tubers concordant with the resection zone in three patients with a good surgical outcome (true positive) and appropriately discordant in three other patients with a poor outcome (true negative). CONCLUSIONS: Lesion-constrained electrical source imaging on low-resolution EEG data provides complementary information in the presurgical workup for patients with tuberous sclerosis complex, although further validation is required. In the appropriate clinical context, the yield of source localization on low-resolution EEG data may be increased by reduction of the solution space.

The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex.

Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.

Early white matter development is abnormal in tuberous sclerosis complex patients who develop autism spectrum disorder.

BACKGROUND: Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. METHODS: TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. RESULTS: Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. CONCLUSIONS: Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.

Extra-axonal restricted diffusion as an in-vivo marker of reactive microglia.

Reactive microgliosis is an important pathological component of neuroinflammation and has been implicated in a wide range of brain diseases including brain tumors, multiple sclerosis, Parkinson's disease, Alzheimer's disease, and schizophrenia. Mapping reactive microglia in-vivo is often performed with PET scanning whose resolution, cost, and availability prevent its widespread use. The advent of diffusion compartment imaging (DCI) to probe tissue microstructure in vivo holds promise to map reactive microglia using MRI scanners. But this potential has never been demonstrated. In this paper, we performed longitudinal DCI in rats that underwent dorsal root axotomy triggering Wallerian degeneration of axons-a pathological process which reliably activates microglia. After the last DCI at 51 days, rats were sacrificed and histology with Iba-1 immunostaining for microglia was performed. The fraction of extra-axonal restricted diffusion from DCI was found to follow the expected temporal dynamics of reactive microgliosis. Furthermore, a strong and significant correlation between this parameter and histological measurement of microglial density was observed. These findings strongly suggest that extra-axonal restricted diffusion is an in-vivo marker of reactive microglia. They pave the way for MRI-based microglial mapping which may be important to characterize the pathogenesis of neurological and psychiatric diseases.

Histological and diffusion-weighted magnetic resonance imaging data from normal and degenerated optic nerve and chiasm of the rat.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to infer microstructural characteristics of tissue, particularly in cerebral white matter. Histological validation of the metrics derived from dMRI methods are needed to fully characterize their ability to capture biologically-relevant histological features non-invasively. The data described here were used to correlate metrics derived from dMRI and quantitative histology in an animal model of axonal degeneration ("Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration" [1]). Unilateral retinal ischemia/reperfusion was induced in 10 rats, by the elevation of pressure of the anterior chamber of the eye for 90 min. Five rats were used as controls. After five weeks, injured animals were intracardially perfused to analyze the optic nerves and chiasm with dMRI and histology. This resulted in 15 brain scans, each with 80 diffusion-sensitizing gradient directions with b = 2000 and 2500 s/mm2 and 20 non-diffusion-weighted images (b = 0 s/mm2), with isometric voxel resolution of 125 µm3. Histological sections were obtained after dMRI. Optical microscopy photomicrographs of the optic nerves (stained with toluidine blue) are available, as well as their corresponding automatic segmentations of axons and myelin.

Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study.

BACKGROUND: Multi-site MRI studies are often necessary for recruiting sufficiently sized samples when studying rare conditions. However, they require pooling data from multiple scanners into a single data set, and therefore it is critical to evaluate the variability of quantitative MRI measures within and across scanners used in multi-site studies. The aim of this study was to evaluate the reproducibility of structural and diffusion weighted (DW) MRI measurements acquired on seven scanners at five medical centers as part of the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN) multisite study. METHODS: The American College of Radiology (ACR) phantom was imaged monthly to measure reproducibility of signal intensity and uniformity within and across seven 3T scanners from General Electric, Philips, and Siemens vendors. One healthy adult male volunteer was imaged repeatedly on all seven scanners under the TACERN structural and DW protocol (5 b = 0 s/mm2 and 30 b = 1000 s/mm2) over a period of 5 years (age 22-27 years). Reproducibility of inter- and intra-scanner brain segmentation volumes and diffusion tensor imaging metrics fractional anisotropy (FA) and mean diffusivity (MD) within white matter regions was quantified with coefficient of variation. RESULTS: The American College of Radiology Phantom signal intensity and uniformity were similar across scanners and changed little over time, with a mean intra-scanner coefficient of variation of 3.6 and 1.8%, respectively. The mean inter- and intra-scanner coefficients of variation of brain structure volumes derived from T1-weighted (T1w) images of the human phantom were 3.3 and 1.1%, respectively. The mean inter- and intra-scanner coefficients of variation of FA in white matter regions were 4.5 and 2.5%, while the mean inter- and intra-scanner coefficients of variation of MD in white matter regions were 5.4 and 1.5%. CONCLUSION: Our results suggest that volumetric and diffusion tensor imaging (DTI) measurements are highly reproducible between and within scanners and provide typical variation amplitudes that can be used as references to interpret future findings in the TACERN network.

Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration.

Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter. Several multiple fiber models have been proposed that seem to overcome the limitations of the tensor, with few providing per-bundle dMRI-derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation. To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures. We correlated and compared histology to per-bundle descriptors derived from three methodologies for dMRI analysis (constrained spherical deconvolution and two multi-tensor representations). We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density and fractional anisotropy (derived from dMRI). The multi-fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions. Our proposed framework is useful to validate other current and future dMRI methods.

White matter mean diffusivity correlates with myelination in tuberous sclerosis complex.

OBJECTIVE: Diffusion tensor imaging (DTI) of the white matter is a biomarker for neurological disease burden in tuberous sclerosis complex (TSC). To clarify the basis of abnormal diffusion in TSC, we correlated ex vivo high-resolution diffusion imaging with histopathology in four tissue types: cortex, tuber, perituber, and white matter. METHODS: Surgical specimens of three children with TSC were scanned in a 3T or 7T MRI with a structural image isotropic resolution of 137-300 micron, and diffusion image isotropic resolution of 270-1,000 micron. We stained for myelin (luxol fast blue, LFB), gliosis (glial fibrillary acidic protein, GFAP), and neurons (NeuN) and registered the digitized histopathology slides (0.686 micron resolution) to MRI for visual comparison. We then performed colocalization analysis in four tissue types in each specimen. Finally, we applied a linear mixed model (LMM) for pooled analysis across the three specimens. RESULTS: In white matter and perituber regions, LFB optical density measures correlated with fractional anisotropy (FA) and inversely with mean diffusivity (MD). In white matter only, GFAP correlated with MD, and inversely with FA. In tubers and in the cortex, there was little variation in mean LFB and GFAP signal intensity, and no correlation with MRI metrics. Neuronal density correlated with MD. In the analysis of the combined specimens, the most robust correlation was between white matter MD and LFB metrics. INTERPRETATION: In TSC, diffusion imaging abnormalities in microscopic tissue types correspond to specific histopathological markers. Across all specimens, white matter diffusivity correlates with myelination.

Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition.

PURPOSE: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI. METHODS: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. RESULTS: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion. CONCLUSION: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

Longitudinal Effects of Everolimus on White Matter Diffusion in Tuberous Sclerosis Complex.

OBJECTIVE: We studied the longitudinal effects of everolimus, an inhibitor of the mammalian target of rapamycin (mTOR), on callosal white matter diffusion tensor imaging (DTI) in patients with tuberous sclerosis complex (TSC). METHODS: Serial imaging data spanning nine years were used from the open label, Phase I/II trial (NCT00411619) and open-ended extension phase of everolimus for the treatment of subependymal giant cell astrocytoma associated with TSC. From 28 patients treated with everolimus and 25 untreated control patients, 481 MRI scans were available. Rigorous quality control resulted in omission of all scans with diffusion weighted imaging data in less than 15 directions or more than eight artifacted volumes, and all postsurgical scans. We applied a linear mixed-effects model to the remaining 125 scans (17 treated, 24 controls) for longitudinal analysis of each DTI metric of manually drawn callosal regions of interest. RESULTS: On a population level, mTOR inhibition was associated with a decrease in mean diffusivity. In addition, in treated patients only, a decrease of radial diffusivity was observed; in untreated patients only, an increase of axial diffusivity was seen. In patients below age 10, effect-sizes were consistently greater, and longer treatment was associated with greater rate of diffusion change. There was no correlation between DTI metrics and reduction of subependymal giant cell astrocytoma volume, or everolimus serum levels. CONCLUSIONS: Effects from mTOR overactivity on white matter microstructural integrity in TSC were modified through pharmacologic inhibition of mTOR. These changes sustained over time, were greater with longer treatment and in younger patients during a time of rapid white matter maturation.

Volumetric Analysis of the Basal Ganglia and Cerebellar Structures in Patients with Phelan-McDermid Syndrome.

OBJECTIVE: Phelan-McDermid syndrome is caused by haploinsufficiency of SHANK3 on terminal chromosome 22. Knowledge about altered neuroanatomic circuitry in Phelan-McDermid syndrome comes from mouse models showing striatal hypertrophy in the basal ganglia, and from humans with evidence of cerebellar atrophy. To date, no studies have performed volumetric analysis on Phelan-McDermid syndrome patients. METHODS: We performed volumetric analysis of baseline brain MRIs of Phelan-McDermid syndrome patients (ages three to 21 years) enrolled in a prospective natural history study (ClinicalTrials.gov NCT02461420). Using MRI segmentations carried out with PSTAPLE algorithm, we measured relative volumes (volume of the structure divided by the volume of the brain parenchyma) of basal ganglia and cerebellar structures. We compared these measurements to those of age- and sex-matched healthy controls part of another study. Among the patients, we performed linear regression of each relative volume using Repetitive Behavior Scale-Revised total score and Aberrant Behavior Checklist stereotypy score. Eleven patients with Phelan-McDermid syndrome (six females, five males) and 11 healthy controls were in this analysis. RESULTS: At time of MRI, the mean age of the patients and controls was 9.24 (5.29) years and 9.00 (4.49) years, respectively (P = 0.66). Compared to controls, patients had decreased caudate (P ≤ 0.013), putamen (P ≤ 0.026), and left pallidum (P = 0.033) relative volumes. Relative volume of cerebellar vermal lobules I to V (beta coefficient = -17119, P = 0.017) decreased with increasing Repetitive Behavior Scale-Revised total score. CONCLUSIONS: The volumes of the striatum and left pallidum are decreased in individuals with Phelan-McDermid syndrome. Cerebellar vermis volume may predict repetitive behavior severity in Phelan-McDermid syndrome. These findings warrant further investigation in larger samples.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

Corpus Callosum White Matter Diffusivity Reflects Cumulative Neurological Comorbidity in Tuberous Sclerosis Complex.

INTRODUCTION: Neurological manifestations in Tuberous Sclerosis Complex (TSC) are highly variable. Diffusion tensor imaging (DTI) may reflect the neurological disease burden. We analyzed the association of autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy with callosal DTI metrics in subjects with and without TSC. METHODS: 186 children underwent 3T MRI DTI: 51 with TSC (19 with concurrent ASD), 46 with non-syndromic ASD and 89 healthy controls (HC). Subgroups were based on presence of TSC, ASD, ID, and epilepsy. Density-weighted DTI metrics obtained from tractography of the corpus callosum were fitted using a 2-parameter growth model. We estimated distributions using bootstrapping and calculated half-life and asymptote of the fitted curves. RESULTS: TSC was associated with a lower callosal fractional anisotropy (FA) than ASD, and ASD with a lower FA than HC. ID, epilepsy and ASD diagnosis were each associated with lower FA values, demonstrating additive effects. In TSC, the largest change in FA was related to a comorbid diagnosis of ASD. Mean diffusivity (MD) showed an inverse relationship to FA. Some subgroups were too small for reliable data fitting. CONCLUSIONS: Using a cross-disorder approach, this study demonstrates cumulative abnormality of callosal white matter diffusion with increasing neurological comorbidity.