Diffusion MRI Techniques Research Articles

Brain Microstructural Changes Associated With Neurocognitive Outcome in Intracranial Germ Cell Tumor Survivors.

BackgroundChildhood intracranial germ cell tumor (GCT) survivors are prone to radiotherapy-related neurotoxicity, which can lead to neurocognitive dysfunctions. Diffusion kurtosis imaging (DKI) is a diffusion MRI technique that is sensitive to brain microstructural changes. This study aimed to investigate the association between DKI metrics versus cognitive and functional outcomes of childhood intracranial GCT survivors.MethodsDKI was performed on childhood intracranial GCT survivors (n = 20) who had received cranial radiotherapy, and age and gender-matched healthy control subjects (n = 14). Neurocognitive assessment was performed using the Hong Kong Wechsler Intelligence Scales, and functional assessment was performed using the Lansky/Karnofsky performance scales (KPS). Survivors and healthy controls were compared using mixed effects model. Multiple regression analyses were performed to determine the effects of microstructural brain changes of the whole brain as well as the association between IQ and Karnofsky scores and the thereof.ResultsThe mean Intelligence Quotient (IQ) of GCT survivors was 91.7 (95% CI 84.5 – 98.8), which was below the age-specific normative expected mean IQ (P = 0.013). The mean KPS score of GCT survivors was 85.5, which was significantly lower than that of controls (P < 0.001). Cognitive impairments were significantly associated with the presence of microstructural changes in white and grey matter, whereas functional impairments were mostly associated with microstructural changes in white matter. There were significant correlations between IQ versus the mean diffusivity (MD) and mean kurtosis (MK) of specific white matter regions. The IQ scores were negatively correlated with the MD of extensive grey matter regions.ConclusionOur study identified vulnerable brain regions whose microstructural changes in white and grey matter were significantly associated with impaired cognitive and physical functioning in survivors of pediatric intracranial GCT.

White Matter Microstructural Changes in Psychotic Spectrum Disorder are Associated With Cognitive Function and Symptoms

White matter (WM) microstructural abnormalities in Psychotic Spectrum Disorders (PSD) can now be assessed using novel diffusion MRI (dMRI) techniques. We applied Diffusion Kurtosis Imaging (DKI) and Neurite Orientation Density and Dispersion Imaging Bingham (NODDI-B) alongside Diffusion Tensor Imaging (DTI) to obtain metrics of WM density, orientation dispersion, and complexity and better describe WM microstructural changes in PSD. We additionally tested whether dMRI metrics are associated with cognition and schizotypal symptoms in patients and controls.

Evaluation of early microstructural changes in the R6/1 mouse model of Huntington's disease by ultra-high field diffusion MR imaging

Diffusion MRI (dMRI) has been able to detect early structural changes related to neurological symptoms present in Huntington's disease (HD). However, there is still a knowledge gap to interpret the biological significance at early neuropathological stages. The purpose of this study is two-fold: (i) establish if the combination of Ultra-High Field Diffusion MRI (UHFD-MRI) techniques can add a more comprehensive analysis of the early microstructural changes observed in HD, and (ii) evaluate if early changes in dMRI microstructural parameters can be linked to cellular biomarkers of neuroinflammation. Ultra-high field magnet (16.7T), diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI) techniques were applied to fixed ex-vivo brains of a preclinical model of HD (R6/1 mice). Fractional anisotropy (FA) was decreased in deep and superficial grey matter (GM) as well as white matter (WM) brain regions with well-known early HD microstructure and connectivity pathology. NODDI parameters associated with the intracellular and extracellular compartment, such as intracellular ventricular fraction (ICVF), orientation dispersion index (ODI), and isotropic volume fractions (IsoVF) were altered in R6/1 mice GM. Further, histological studies in these areas showed that glia cell markers associated with neuroinflammation (GFAP & Iba1) were consistent with the dMRI findings. dMRI can be used to extract non-invasive information of neuropathological events present in the early stages of HD. The combination of multiple imaging techniques represents a better approach to understand the neuropathological process allowing the early diagnosis and neuromonitoring of patients affected by HD.

Diffusion MRI derived free-water imaging measures in patients with schizophrenia and their non-psychotic siblings

Free-water imaging is a diffusion MRI technique that separately models water diffusion hindered by fiber tissue and water that disperses freely in the extracellular space. Studies using this technique have shown that schizophrenia is characterized by a lower level of fractional anisotropy of the tissue compartment (FAt) and higher free-water fractional volume (FW). It is unknown, however, whether such abnormalities are an expression of pre-existing (genetic) risk for schizophrenia or a manifestation of the illness. To investigate the contribution of familial risk factors to white matter abnormalities, we used the free-water imaging technique to assess FAt and FW in a large cohort of 471 participants including 161 patients with schizophrenia, 182 non-psychotic siblings, and 128 healthy controls. In this sample, patients did not show significant differences in FAt as compared to controls, but did exhibit a higher level of FW relative to both controls and siblings in the left uncinate fasciculus, superior corona radiata and fornix / stria terminalis. This increase in FW was found to be related to, though not solely explained by, ventricular enlargement. Siblings did not show significant FW abnormalities. However, siblings did show a higher level of FAt as compared to controls and patients, in line with results of a previous study on the same data using conventional DTI. Taken together, our findings suggest that extracellular free-water accumulation in patients is likely a manifestation of established disease rather than an expression of familial risk for schizophrenia and that super-normal levels of FAt in unaffected siblings may reflect a compensatory process.

Association Between Proteomic Blood Biomarkers and DTI/NODDI Metrics in Adolescent Football Players: A Pilot Study.

While neuroimaging and blood biomarker have been two of the most active areas of research in the neurotrauma community, these fields rarely intersect to delineate subconcussive brain injury. The aim of the study was to examine the association between diffusion MRI techniques [diffusion tensor imaging (DTI) and neurite orientation/dispersion density imaging (NODDI)] and brain-injury blood biomarker levels [tau, neurofilament-light (NfL), glial-fibrillary-acidic-protein (GFAP)] in high-school football players at their baseline, aiming to detect cumulative neuronal damage from prior seasons. Twenty-five football players were enrolled in the study. MRI measures and blood samples were obtained during preseason data collection. The whole-brain, tract-based spatial statistics was conducted for six diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial/radial diffusivity (AD, RD), neurite density index (NDI), and orientation dispersion index (ODI). Five players were ineligible for MRIs, and three serum samples were excluded due to hemolysis, resulting in 17 completed set of diffusion metrics and blood biomarker levels for association analysis. Our permutation-based regression model revealed that serum tau levels were significantly associated with MD and NDI in various axonal tracts; specifically, elevated serum tau levels correlated to elevated MD (p = 0.0044) and reduced NDI (p = 0.016) in the corpus callosum and surrounding white matter tracts (e.g., longitudinal fasciculus). Additionally, there was a negative association between NfL and ODI in the focal area of the longitudinal fasciculus. Our data suggest that high school football players may develop axonal microstructural abnormality in the corpus callosum and surrounding white matter tracts, such as longitudinal fasciculus. A future study is warranted to determine the longitudinal multimodal relationship in response to repetitive exposure to sports-related head impacts.

Nucleus accumbens cytoarchitecture predicts weight gain in children

The prevalence of obesity in children and adolescents worldwide has quadrupled since 1975 and is a key predictor of obesity later in life. Previous work has consistently observed relationships between macroscale measures of reward-related brain regions (e.g., the nucleus accumbens [NAcc]) and unhealthy eating behaviors and outcomes; however, the mechanisms underlying these associations remain unclear. Recent work has highlighted a potential role of neuroinflammation in the NAcc in animal models of diet-induced obesity. Here, we leverage a diffusion MRI technique, restriction spectrum imaging, to probe the microstructure (cellular density) of subcortical brain regions. More specifically, we test the hypothesis that the cell density of reward-related regions is associated with obesity-related metrics and early weight gain. In a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate that cellular density in the NAcc is related to individual differences in waist circumference at baseline and is predictive of increases in waist circumference after 1 y. These findings suggest a neurobiological mechanism for pediatric obesity consistent with rodent work showing that high saturated fat diets increase gliosis and neuroinflammation in reward-related brain regions, which in turn lead to further unhealthy eating and obesity.

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain.

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation–diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo‐times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation–diffusion distributions where contributions from different sub‐voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre‐specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation‐specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre‐tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

Cellular and extracellular white matter alterations indicate conversion to psychosis among individuals at clinical high-risk for psychosis

Objectives It is important to find biomarkers associated with transition to illness in individuals at clinical high-risk for psychosis (CHR). Here, we use free-water imaging, an advanced diffusion MRI technique, to identify white matter alterations in the brains of CHR subjects who subsequently develop psychosis (CHR-P) compared to those who do not (CHR-NP). Methods Twenty-four healthy controls (HC) and 30 CHR individuals, 8 of whom converted to schizophrenia after a mean follow-up of 15.16 months, received baseline MRI scans. Maps of fractional anisotropy (FA), FA of cellular tissue (FAT), and extracellular free-water (FW) were extracted using tract-based spatial statistics after which voxel-wise non-parametric group statistics and correlations with symptom severity were performed. Results There were no significant differences between HCs and the combined CHR group. However, prior to conversion, CHR-P showed widespread lower FA compared to CHR-NP (p FWE < 0.05). FA changes in CHR-P were associated with significantly lower FAT and higher FW, compared to CHR-NP. Positive symptoms correlated significantly with diffusion parameters in similar regions as those discriminating CHR-P from CHR-NP. Conclusions Our study suggests that cellular (FAT) and extracellular (FW) white matter alterations are associated with positive symptom severity and indicate an elevated illness risk among CHR individuals.

MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging.

Age-Related Decline in the Topological Efficiency of the Brain Structural Connectome and Cognitive Aging.

Brain disconnection model has been proposed as a possible neural mechanism for cognitive aging. However, the relationship between structural connectivity degeneration and cognitive decline with normal aging remains unclear. In the present study, using diffusion MRI and tractography techniques, we report graph theory-based analyses of the brain structural connectome in a cross-sectional, community-based cohort of 633 cognitively healthy elderly individuals. Comprehensive neuropsychological assessment of the elderly subjects was performed. The association between age, brain structural connectome, and cognition across elderly individuals was examined. We found that the topological efficiency, modularity, and hub integration of the brain structural connectome exhibited a significant decline with normal aging, especially in the frontal, parietal, and superior temporal regions. Importantly, network efficiency was positively correlated with attention and executive function in elderly subjects and had a significant mediation effect on the age-related decline in these cognitive functions. Moreover, nodal efficiency of the brain structural connectome showed good performance for the prediction of attention and executive function in elderly individuals. Together, our findings revealed topological alterations of the brain structural connectome with normal aging, which provides possible structural substrates underlying cognitive aging and sensitive imaging markers for the individual prediction of cognitive functions in elderly subjects.

MR Biomarkers of Degenerative Brain Disorders Derived From Diffusion Imaging.

The incidence of neurodegenerative diseases has shown an increasing trend. These conditions typically cause progressive functional disability. Identification of robust biomarkers of neurodegenerative diseases is a key imperative to facilitate early identification of the pathological features and to foster a better understanding of the pathogenetic mechanisms of individual diseases. Diffusion tensor imaging (DTI) is the most widely used diffusion MRI technique for assessment of neurodegenerative diseases. The DTI parameters are promising biomarkers for evaluation of microstructural changes; however, some limitations of DTI restrict its wider clinical use. New diffusion MRI techniques, such as diffusion kurtosis imaging (DKI), bi‐tensor DTI, and neurite orientation density and dispersion imaging (NODDI) have been demonstrated to provide value addition to DTI for evaluation of neurodegenerative diseases. In this review article, we summarize the key technical aspects and provide an overview of the current state of knowledge regarding the role of DKI, bi‐tensor DTI, and NODDI as biomarkers of microstructural changes in representative neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease.Level of Evidence5Technical Efficacy Stage2 J. MAGN. RESON. IMAGING 2020;52:1620–1636.

Effects of brain network construction on individual differences of topological properties

Advances in diffusion MRI techniques have made it possible to virtually reconstruct white matter tracts, which further allows the modeling of the human brain as a complex network in vivo . Graph theoretical approaches have been applied to characterize topological architectures of whole-brain white matter networks. By using these techniques, many important topological properties for the whole-brain white matter network of human brain have been revealed in many previous works, while the construction method for white matter networks differs across studies. At present, a few studies have assessed the effects of network construction methods on specific white matter network properties and substantial influences of the construction methods were observed. However, the previous research mainly focuses on the effects of the constructed methods on the same subjects for their network properties. Up to now, how the topological properties are affected by construction methods for the individual differences is still unknown, even it is the key for the investigation of white matter networks in a cross-sectional manner, including group comparison, correlational analysis, etc. Therefore, it is necessary but still insufficient to make a systematic evaluation for the network construction methods on the individual differences. In the present work, the influence of different network construction methods on the network properties is desired to be assessed individual differences. Thirty-two methods were adopted to construct the whole-brain white matter networks of 80 healthy adult subjects, which includes two node definitions (low-resolution and high-resolution), four deterministic tractography algorithms (Fiber Assignment by Continuous Tracking, Tensorline Tracking, Interpolated streamline method and 2nd-order Runge-Kutta method) and four weighted edge definitions (binary, FA weighted, fiber-length weighted, and fiber-number weighted). For these white matter networks, individual differences were analyzed by graph theory and hierarchical clustering in three topological parameters including the global efficiency, the local efficiency and the nodal efficiency of the network. The results can be summarized that, firstly, the nodal efficiency with the same nodal definition exhibits convergent individual differences, which indicated that the individual differences of network nodal properties are divergent across network resolutions. Secondly, the individual differences of global and local efficiency of white matter networks constructed by different deterministic fiber tracking algorithms do not differ significantly. However, the constructed networks based on the same deterministic fiber tracking algorithm exhibited the same individual differences with the different node efficiency, which indicated that the effect of fiber tracking algorithm should be emphasized in the comparison and analysis of nodal properties in different studies. Finally, the method clustering analysis for the network global efficiency and local efficiency indicates that the networks with both low-resolution and high-resolution will compose to be the same module with the definition of the same edges, which indicated the high convergent of individual differences for the network efficiencies with different resolutions. It can be concluded that the difference of network construction methods depends on the network properties when it is used to reflexing the individual difference. These findings may provide valuable implications for understanding the intersubject variability of white matter networks and comparing results across different studies.

Extending the frontiers of neuroimaging: an introduction to Diffusion Tensor Imaging Tractography

Summary: The use of Diffusion Tensor Imaging (DTI) tractography has exploded over the past two decades, proving to be a major advancement in the current methods for exploring the architecture of white matter tracts in the living human brain. Despite making up 50% of brain tissue, investigations into the function of white matter have long remained in the background of medical research. Consequently, white matter pathology cited in neuropsychiatric disease remains a heavy burden of human illness. Nonetheless, with novel diffusion MRI techniques, including DTI tractography, we are able to visualise areas of the brain better than ever before, furthering our understanding of white matter beyond the crude depictions offered by conventional MRI. Relevance: It is important that medical students have an understanding of DTI tractography because it has the potential to illuminate mechanisms that underpin cognition and emotion through the creation of connectional maps. Furthermore, advances in MR technology should not be restricted to the territory of academics. Instead, both medical students and clinicians should be able to draw upon the current research methods to improve the care and understanding of patients with white matter pathology. Take Home Messages: 1. Diffusion Tensor Imaging is a diffusion MRI technique that depicts the movement of water molecules along major white matter pathways in the living brain. 2. Using information from DTI, tractography software virtually reconstructs white matter tracts and can be used to make dissections of major fasciculi in vivo. 3. DTI tractography remains the only non-invasive method to study white matter tracts in the living human brain. 4. Results from tractography are increasingly being used in neuropsychiatric research. 5. Despite its potential, DTI tractography still faces a number of challenges. The most prominent of these being its inability to accurately map crossing fibres within a voxel.

Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) in rats at 9.4 Tesla.

PurposeNeurite Orientation Dispersion and Density Imaging (NODDI) is a diffusion MRI (dMRI) technique used to characterize tissue microstructure by compartmental modelling of neural water fractions. Intra-neurite, extra-neurite, and cerebral spinal fluid volume fractions are measured. The purpose of this study was to determine the reproducibility of NODDI in the rat brain at 9.4 Tesla.MethodsEight data sets were successfully acquired on adult male Sprague Dawley rats. Each rat was scanned twice on a 9.4T Agilent MRI with a 7 ± 1 day separation between scans. A multi-shell diffusion protocol was implemented consisting of 108 total directions varied over two shells (b-values of 1000 s/mm2 and 2000 s/mm2). Three techniques were used to analyze the NODDI scalar maps: mean region of interest (ROI) analysis, whole brain voxel-wise analysis, and targeted ROI analyses (voxel-wise within a given ROI). The coefficient of variation (CV) was used to assess the reproducibility of NODDI and provide insight into necessary sample sizes and minimum detectable effect size.ResultsCV maps for orientation dispersion index (ODI) and neurite density index (NDI) showed high reproducibility both between and within subjects. Furthermore, it was found that small biological changes (<5%) may be detected with feasible sample sizes (n < 6–10). In contrast, isotropic volume fraction (IsoVF) was found to have low reproducibility, requiring very large sample sizes (n > 50) for biological changes to be detected.ConclusionsThe ODI and NDI measured by NODDI in the rat brain at 9.4T are highly reproducible and may be sensitive to subtle changes in tissue microstructure.

MK-curve - Characterizing the relation between mean kurtosis and alterations in the diffusion MRI signal

Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.

Application of neurite orientation dispersion and density imaging to characterize brain microstructural abnormalities in type-2 diabetics with mild cognitive impairment.

Diffusion-tensor-imaging (DTI) is sensitive in detecting white matter changes in type-2 diabetes mellitus (T2DM). However, DTI indices can be affected by either neurite density or spatial variation. A novel diffusion MRI technique, termed neurite orientation dispersion and density imaging (NODDI), can provide distinct indices of fiber density and dispersion. To characterize brain microstructural alterations in T2DM patients with mild cognitive impairment (MCI) using the NODDI model. Cross-sectional. Twenty T2DM patients with (DM-MCI group), 18 age- and gender-matched T2DM patients with normal cognition (DM-NC group), and 28 euglycemic healthy controls (HC). 3T/NODDI. Diffusion data were analyzed using tract-based-spatial-statistics (TBSS) analysis in white matter and voxel-based analysis in both white and gray matter. NODDI indices, including intracellular volume fraction (Vic) and orientation dispersion index (ODI), were estimated from multiple regions and compared among these groups. Differences between groups were compared by Student's t-test, Pearson chi-square test, or analysis of variance when appropriate. Correlation analyses were performed to investigate the relationship between NODDI variables and clinical measurements. Whole-brain TBSS revealed that 2.29% and 2.02% of the white matter regions exhibited decreased fractional anisotropy and Vic, respectively, between the DM-NC and HC, while considerably larger white matter areas showed decreased fractional anisotropy (38.38%) and Vic (34.64%) between the DM-MCI and HC (Student's t-test, P < 0.05). However, the angular variation of neurites, characterized by ODI, exhibited very little (0.1%, P < 0.05) or no difference (P > 0.05) between either the DM-MCI or DM-NC groups and HC. Decreased Vic values in the genu of the corpus callosum (R = 0.580, 0.551 and 0.586, P < 0.01) and thalamus (R = 0.570, 0.616, and 0.595, P < 0.05) correlated with glycosylated hemoglobin A1c level, disease duration, and neuropsychological scores, respectively. T2DM patients with cognitive decline had reduced Vic, which indicated decreased density of axons and dendrites. NODDI might be able to help probe microstructural changes in white and gray matter and provide information on diabetic encephalopathy, including those with cognitive impairment. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:889-898.

Nutritional Intervention for Developmental Brain Damage: Effects of Lactoferrin Supplementation in Hypocaloric Induced Intrauterine Growth Restriction Rat Pups.

Introduction: Intrauterine Growth Restriction (IUGR) refers to an impaired development of the fetus and hence results in adverse neurodevelopmental and psychiatric consequences later in life. Lactoferrin (Lf) is a glycoprotein present in milk that has already shown neuroprotective effects through its anti-inflammatory and antioxidant properties on impaired developing brains. The aim of this study was to characterize a rat model of IUGR and assess the neuroprotective effect of a nutritional supplementation with bovine Lf during pregnancy and lactation on this model.Methods: A model of 50% gestational caloric restriction (CR) was used. Three groups were designed, and pregnant rats had either ad libitum access to food (control group, CTL) or 50% of the controls' intake (restricted group, IUGR). The diet was isocaloric and supplemented with bovine Lf for the caloric restricted dams (restricted-Lf, IUGR_Lf). At postnatal day 7 and 21, advanced ex-vivo diffusion MRI techniques at 9.4T were used to investigate brain cortical and white matter microstructure. Further, genes and proteins involved in structure (synaptophysin, MBP), microglia (Iba-1), metabolism (MCT2, βCaMKII) and apoptosis (Bcl-2) were analyzed in the cortex and striatum. In the cortex, the number of parvalbumin immunoreactive interneurons and their perineuronal nets were quantified. Behavioral tests were performed at P31.Results: Effects of the CR were significant in the cortex and striatum with reduction of synaptophysin (marker of synaptogenesis) at P7 and MBP (marker of myelin) at P21 in the cortex. Indeed, MCT2 (energy metabolism), Bcl-2 (anti-apoptotic protein) and βCaMKII (synapse activity) expressions were reduced in IUGR groups at P7. In the striatum NG2 (marker of oligodendrocyte precursor cells) and Bcl-2 at P7 as well as βCaMKII at P21 were decreased following IUGR and restored by Lf. Cortical microstructure was impaired following CR with partial effect of Lf. Lf prevented oxidative stress induced parvalbumin interneurons impairments whereas striatum and external capsule showed alterations in microstructure depicted by diffusion MRI, which were also partially reversed by Lf.Discussion and Conclusion: The model of 50% caloric restriction induced mild impairment partially reversed by nutritional intervention using Lf during pregnancy and lactation.

Structural, Microstructural, and Metabolic Alterations in Primary Progressive Aphasia Variants.

Neuroimaging studies have described the brain alterations in primary progressive aphasia (PPA) variants (semantic, logopenic, nonfluent/agrammatic). However, few studies combined T1, FDG-PET, and diffusion MRI techniques to study atrophy, hypometabolism, and tract alterations across the three PPA main variants. We therefore explored a large early-stage cohort of semantic, logopenic and nonfluent/agrammatic variants (N = 86) and of 23 matched healthy controls with anatomical MRI (cortical thickness), FDG PET (metabolism) and diffusion MRI (white matter tracts analyses), aiming at identifying cortical and sub-cortical brain alterations, and confronting these alterations across imaging modalities and aphasia variants. In the semantic variant, there was cortical thinning and hypometabolism in anterior temporal cortices, with left-hemisphere predominance, extending toward posterior temporal regions, and affecting tracts projecting to the anterior temporal lobes (inferior longitudinal fasciculus, uncinate fasciculus) and tracts projecting to or running nearby posterior temporal cortices: (superior longitudinal fasciculus, inferior frontal-occipital fasciculus). In the logopenic variant metabolic alterations were more extensive than atrophy affecting mainly the left temporal-parietal junction and extending toward more anterior temporal cortices. Metabolic and tract data were coherent given the alterations of the left superior and inferior longitudinal fasciculus and the left inferior frontal-occipital fasciculus. In the nonfluent/agrammatic variant cortical thinning and hypometabolism were located in the left frontal cortex but Broca's area was only affected on metabolic measures. Metabolic and tract alterations were coherent as reflected by damage to the left uncinate fasciculus connecting with Broca's area. Our findings provide a full-blown statistically robust picture of brain alterations in early-stage variants of primary progressive aphasia which has implications for diagnosis, classification and future therapeutic strategies. They demonstrate that in logopenic and semantic variants patterns of brain damage display a non-negligible overlap in temporal regions whereas they are substantially distinct in the nonfluent/agrammatic variant (frontal regions). These results also indicate that frontal networks (combinatorial syntax/phonology) and temporal networks (lexical/semantic representations) constitute distinct anatomo-functional entities with differential vulnerability to degenerative processes in aphasia variants. Finally, the identification of the specific damage patterns could open an avenue for trans-cranial stimulation approaches by indicating the appropriate target-entry into the damaged language system.

Angular resolution enhancement technique for diffusion-weighted imaging (DWI) using predicted diffusion gradient directions

Anisotropic diffusion MRI techniques using single-shell or multi-shell acquisitions have been proposed as a means to overcome some limitations imposed by diffusion tensor imaging (DTI), especially in complex models of fibre orientation distribution in voxels. A long acquisition time for the angular resolution of diffusion MRI is a major obstacle to practical clinical implementations. In this paper, we propose a novel method to improve angular resolution of diffusion MRI acquisition using given diffusion gradient (DG) directions. First, we define a local diffusion pattern map of diffusion MR signals on a single shell in given DG directions. Using the local diffusion pattern map, we design a prediction scheme to determine the best DG direction to be synthesized within a nearest neighborhood DG directions group. Second, the local diffusion pattern map and the spherical distance on the shell are combined to determine a synthesized diffusion signal in the new DG direction. Using the synthesized and measured diffusion signals on a single sphere, we estimate a spin orientation distribution function (SDF) with human brain data. Although the proposed method is applied to SDF, a basic idea is to increase the angular resolution using the measured diffusion signals in various DG directions. The method can be applicable to different acquired multi-shell data or diffusion spectroscopic imaging (DSI) data. We validate the proposed method by comparing the recovered SDFs using the angular resolution enhanced diffusion signals with the recovered SDF using the measured diffusion data. The developed method provides an enhanced SDF resolution and improved multiple fiber structure by incorporating synthesized signals. The proposed method was also applied neurite orientation dispersion and density imaging (NODDI) using multi-shell acquisitions.

Chronic differences in white matter integrity following sport-related concussion as measured by diffusion MRI: 6-Month follow-up.

Recent studies demonstrated evidence of physiological changes in the brain following sport-related concussion (SRC) that persisted beyond the point at which athletes achieved full symptom recovery. Diffusion MRI techniques have been used to study brain white matter (WM) changes following SRC; however, longitudinal studies that follow injured athletes from the acute to chronic stages of injury are sparse. The current study explores potential persisting effects of the injury, which serves as a follow-up to our previous work that reported WM changes in the acute and subacute phase of SRC recovery. Concussed high school and collegiate football players (n = 17) and well-matched teammate controls (n = 20) were followed up at 6 months postinjury with diffusion tensor (DTI) and diffusion kurtosis imaging (DKI) as well as measures of self-reported symptoms, cognitive functioning, and balance. Results of tract-based spatial statistics (TBSS) analyses revealed continued widespread decreased mean and axial diffusivity compared to control subjects in 6-month follow-up scans. On the other hand, kurtosis metrics, which were significantly higher in concussed athletes in the acute phase, had normalized. WM tract regions-of-interest (ROIs) were created from significant clusters in the TBSS analysis, and linear mixed effects (LME) analyses were used to look at longitudinal changes in these ROIs over time. LME analyses revealed few time × group interactions indicating findings were relatively stable over time. In addition, acute concussion symptoms predicted diffusivity measures at 6 months postinjury. Findings indicate that DTI and DKI may be useful tools in assessing concussion severity, recovery, and possible long-term effects of concussion.