Traditional Diffusion Tensor Imaging Research Articles

Mapping the aging brain: Insights into microstructural changes from free water-corrected fractional anisotropy.

Aging has a significant impact on brain structure, demonstrated by numerous MRI studies using diffusion tensor imaging (DTI). While these studies reveal changes in fractional anisotropy (FA) across different brain regions, they tend to focus on white matter tracts and cognitive regions, often overlooking gray matter and motor areas. Additionally, traditional DTI metrics can be affected by partial volume effects. To address these limitations and gain a better understanding of microstructural changes across the whole brain, we utilized free water-corrected fractional anisotropy (FAt) to examine aging-related microstructural changes in a group of 20 young adults (YA) and 24 older adults (OA). A voxel-wise analysis revealed that YA had higher FAt values predominantly in white matter tracts associated with both motor and non-motor functions. In contrast, OA showed higher levels of FAt primarily in gray matter regions, including both subcortical and cortical motor areas, and occipital and temporal cortices. Complementing these cross-sectional results, correlation analyses within the OA group showed that many of these changes are further exacerbated with increasing age, underscoring the progressive nature of these microstructural alterations. In summary, the distinct patterns of FAt changes in gray versus white matter with aging suggest different underlying mechanisms. While white matter FAt values decrease, likely due to axonal degeneration, the increase in gray matter FAt could reflect either compensatory processes or pathological changes. Including behavioral data in future studies will be crucial for understanding the functional implications of these microstructural gray matter changes and their effects on cognitive and motor functions.

White matter fiber morphology in persisting postconcussive symptoms and posttraumatic headache after pediatric concussion: a fixel-based analysis.

Posttraumatic headache (PTH) represents the most common acute and persistent postconcussive symptom (PCS) in children after concussion, yet there remains a lack of valid and objective biomarkers to facilitate risk stratification and early intervention in this patient population. Fixel-based analysis of diffusion-weighted imaging, which overcomes constraints of traditional diffusion tensor imaging analyses, can improve the sensitivity and specificity of detecting white matter changes postconcussion. The aim of this study was to investigate whole-brain and tract-based differences in white matter morphology, including fiber density (FD) and fiber bundle cross-section (FC) area in children with PCSs and PTH at 2 weeks after concussion. This prospective longitudinal study recruited children aged 5-18 years who presented to the emergency department of a tertiary pediatric hospital with a concussion sustained within the previous 48 hours. Participants underwent diffusion-weighted MRI at 2 weeks postinjury. Whole-brain white matter statistical analysis was performed at the level of each individual fiber population within an image voxel (fixel) to compute FD, FC, and a combined metric (FD and bundle cross-section [FDC]) using connectivity-based fixel enhancement. Tract-based Bayesian analysis was performed to examine FD in 23 major white matter tracts. Comparisons of 1) recovered (n = 27) and symptomatic (n = 16) children, and those with 2) PTH (n = 13) and non-PTH (n = 30; overall mean age 12.99 ± 2.70 years, 74% male) found no fiber-specific white matter microstructural differences in FD, FC, or FDC at 2 weeks postconcussion, when adjusting for age and sex (family-wise error rate corrected p value > 0.05). Tract-based Bayesian analysis showed evidence of no effect of PTH on FD in 10 major white matter tracts, and evidence of no effect of recovery group on FD in 3 white matter tracts (Bayes factor < 1/3). Using whole-brain fixel-wise and tract-based analyses, these findings indicate that fiber-specific properties of white matter microstructure are not different between children with persisting PCSs compared with recovered children 2 weeks after concussion. These data extend the limited research on white matter fiber-specific morphology while overcoming limitations inherent to traditional diffusion models. Further validation of our findings with a large-scale cohort is warranted.

Feasibility of diffusion tensor imaging in cervical spondylotic myelopathy using MUSE sequence

The most frequent type of spinal cord injury is cervical spondylotic myelopathy (CSM). Conventional structural magnetic resonance imaging (MRI) is the gold diagnosis standard for CSM. Diffusion tensor imaging (DTI) could reflect microstructural changes in the spinal cord by tracing water molecular diffusion in early stages of CSM. However, due to the complex local anatomical structure and small field of view of the spinal cord, the imaging effect of traditional DTI imaging on the spinal cord is limited. MUSE (MUltiplexed Sensitivity-Encoding) -DTI is a novel diffusion-weighted imaging (DWI) sequence that achieves higher signal intensity through multiple excitation acquisition. MUSE sequence may improve the quality of spinal cord DTI imaging. Prospective study. This study aimed to investigate the clinical diagnosis value of a novel protocol of MUSE-DTI in patients with cervical spondylotic myelopathy (CSM). From August 2021 to March 2022, a total of 60 subjects (22-71 years) were enrolled, including 51 CSM patients (22 males, 29 females) and 9 healthy subjects (4 males and 5 females). Each subject underwent a MUSE-DTI examination and a clinical Japanese Orthopedic Association (JOA) scale. We measured values of FA (Fractional Anisotropy), MD (Mean Diffusivity), AD (Axial Diffusivity), and RD (Radial Diffusivity), and collected the clinical JOA scores of each subject before the MR examination. A 3.0T MR scanner (Signa Architect, GE Healthcare) performed the MUSE-DTI sequence on each subject. The cervical canal stenosis of subjects was classified from grade 0 to grade Ⅲ according to the method of an MRI grading system. FA, MD, AD, and RD maps were generated by postprocessing MUSE-DTI data on the GE workstation. Regions of interest (ROIs) were manually drawn at the C2 vertebral body level and C2/3-C6/7 intervertebral disc levels by covering the whole spinal cord. The clinical severity of myelopathy of subjects was assessed by the clinical Japanese Orthopedic Association scale (JOA). MUSE-DTI can acquire a high-resolution diffusion image compared to traditional DTI. The FAMCL values showed a decreasing trend from grade 0 to grade Ⅲ, while the MDMCL, ADMCL, and RDMCL values showed an overall increasing trend. Significant differences in MDMCL, ADMCL, and RDMCL values were found between adjacent groups among grades Ⅰ-Ⅲ (p<.05). The ADC2 values in CSM patients (grade I-Ⅲ) were significantly lower than in healthy individuals (grade 0) (p=.019). The clinical JOA score has a significant correlation with FAMCL (p=.035), MDMCL (p<.001), ADMCL (p<.001), and RDMCL (p<.001) values. MUSE-DTI displayed a better image quality compared to traditional DTI. MUSE-DTI parameters displayed a grade-dependent trend. All the MUSE-DTI parameters at MCL were correlated with the clinical JOA scores. The ADC2 values can reflect the secondary damage of distal spinal cord. Therefore, MUSE-DTI could be a reliable biomarker for clinical auxiliary diagnosis of spinal cord injury severity in cervical spondylotic myelopathy.

Neurite‐based white matter alterations in MAPT mutation carriers: A multi‐shell diffusion MRI study in the ALLFTD consortium

AbstractBackgroundWe aimed to assess axonal integrity in MAPT mutation carriers and early white matter (WM) neurodegeneration in asymptomatic carriers using traditional diffusion tensor imaging (DTI) measurements, i.e., fractional anisotropy (FA) and mean diffusivity (MD), and neurite orientation dispersion and density imaging (NODDI).MethodWe included available multi‐shell diffusion MRI data from 21 MAPT mutation carriers (16 asymptomatic; 5 symptomatic) and 31 non‐carrier family members as controls from the ALLFTD consortium. We used linear mixed‐effect models to assess WM abnormalities measured with FA and MD, neurite density index (NDI) and orientation dispersion index (ODI), combined across hemispheres. Separate models were conducted in all MAPT mutation carriers relative to controls while accounting for relatedness and age, then in symptomatic and asymptomatic carriers relative to controls. ComBat was used to harmonize data across sites.ResultIn models contrasting all MAPT carriers versus controls, WM alterations involved the cingulum bundle (FA, ODI), fronto‐temporal, entorhinal (FA, MD, NDI), and the inferior temporal WM (FA) after correcting for multiple comparisons (Fig. 1). In models contrasting symptomatic and asymptomatic MAPT carriers versus controls, symptomatic carriers had widespread involvement of the fronto‐temporal WM including the cingulum (FA, ODI), entorhinal (MD, FA, NDI), amygdala (MD, NDI) and inferior temporal (FA) areas after correcting for multiple comparisons. Asymptomatic carriers showed higher entorhinal MD and lower inferior temporal and cingulum FA relative to controls, and a more widespread pattern of higher ODI involved the medial temporal, superior temporal and occipital WM as well as long association and projection fibers including the internal capsule, cingulate bundle and fronto‐occipital fasciculi, although they were not statistically significant after correction for multiple comparisons.ConclusionTraditional DTI and novel NODDI measurements revealed some extent of overlap as well as divergences in patterns of WM abnormalities, indicating axonal loss is a feature of neurodegeneration in MAPT mutation carriers. Our results suggest that traditional DTI and novel NODDI measurements may have different sensitivities to WM alterations in MAPT mutation carriers, and that axon‐based metrics, particularly ODI measurements, may be more sensitive to the early WM involvement during the asymptomatic stage of the disease.

A fixel-based analysis of white matter reductions early detects Parkinson disease with mild cognitive impairment

BackgroundWhite matter (WM) tract alterations are early signs of cognitive impairment in Parkinson disease (PD) patients. Fixel-based analysis (FBA) has advantages over traditional diffusion tensor imaging in managing complex and crossing fibers. We used FBA to measure fiber-specific changes in patients with PD mild cognitive impairment (PD-MCI) and PD normal cognition (PD-NC). MethodsSeventy-one patients with PD without dementia were included: 39 PD-MCI and 32 PD-NC. All underwent diffusion-weighted imaging, clinical examinations, and tests to evaluate their cognitive function globally and in five cognitive domains. FBA was used to investigate fiber-tract alterations and compare PD-MCI with PD-NC subjects. Correlations with each cognitive test were analyzed. ResultsPatients with PD-MCI were significantly older (p = 0.044), had a higher male-to-female ratio (p = 0.006) and total Unified Parkinson's Disease Rating Scale score (p = 0.001). All fixel-based metrics were significantly reduced within the body of the corpus callosum and superior corona radiata in PD-MCI patients (family-wise error-corrected P value < 0.05) compared with PD-NC patients. The cingulum, superior longitudinal fasciculi, and thalamocortical circuit exhibited predominantly fiber-bundle cross-section (FC) changes. In regression analysis, reduced FC values in cerebellar circuits were associated with poor motor function in PD-MCI patients and poor picture-naming ability in PD-NC patients. ConclusionsPD-MCI patients have significant WM alterations compared with PD-NC patients. FBA revealed these changes in various bundle tracts, helping us to better understand specific WM changes that are functionally implicated in PD cognitive decline. FBA is potentially useful in detecting early cognitive decline in PD.

42 White matter injury is driven by HIV duration, immune and vascular factors in virally suppressed people living with HIV: a longitudinal diffusion imaging study

Objective:The aim of the current longitudinal study was to use improved brain white matter integrity outcomes (better at resolving white matter complexity, hence with improved biological significance compared to traditional diffusion tensor imaging - DTI outcomes) while considering baseline age, cardiovascular diseases (CVD), and HIV disease markers impacts on the health of major white matter tracts in virally suppressed people Living with HIV infection (PLHIV) versus demographically, geographically, and life-style comparable HIVnegative controls. Furthermore, white-matter hyperintensity (WMH) and normal-appearing white matter (NAWM) volumes and microstructure were considered.Participants and Methods:At baseline 48 HIV-controls and 84 virally suppressed PLHIV (mean age 55), and at 24-month follow-up, 40 HIV-controls and 75 virally suppressed PLHIV underwent an MRI scan (3T Phillips) collecting a high-resolution anatomical MRI, FLAIR, and a 32-direction diffusion imaging. The diffusion data were processed using mrtrix and intra-cranial volume-corrected outcomes included fibre density (FD), fibre cross-section (log was used; logFC) and a composite fibre density and cross-section (FDC). The volumetric data was first processed in Freesurfer 6.0, and WMH were segmented using the “pgs” U-Net neural network. Using mixed models, we examine the longitudinal mrtrix outcomes across major white matter tracts by HIV status, and associations with CVD (sum of the scaled scores of total cholesterol, HDL, Systolic BP, current smoking, and diabetes) and HIV disease (HIV duration, historical AIDS, nadir CD4, baseline CD4) markers. Additionally, we assessed the volume, and FDC in the periventricular and deep WMH, as well as NAWM, and the associations with CVD and HIV disease markers. We used FDR control procedure (alpha = 0.05), and all p-values reported are FDR adjusted.Results:Relative to controls, PLHIV showed significant reductions (p&lt;.05 - p&lt;.01) of FC, and FDC to a lesser extent, in multiple long cortical association tracts, and within striatal- and thalamic-frontoparietal connections. A small HIV by age interaction was only detected for FC of inferior longitudinal fasciculus (Beta = -0.004, SE = 0.002 p&lt;.04). However, HIV duration (corrected for baseline age) was associated with worse FDC across multiple tracts (p&lt;03 -p&lt;.001). Baseline CD4 counts associated with lower FD in frontal association tracts (p&lt;.05 -p&lt;.005). Furthermore, WMH increased in size with time, age, and higher CVD risk factors, but not HIV status. In PLHIV, deep WMH and NAWM microstructure were both associated with worse CVD but not HIV disease markers.Conclusions:The fine integrity of major white matter tracts is impacted by HIV status, HIV duration and baseline CD4, whereas WMH and NAWM volumes and microstructure are affected by CVD. Our study provides further evidence of the immuno-vascular underpinning of HIV neuropathogenesis in virally suppressed PLHIV. The convergence of these effects in aging PLHIV may lead to early neurodegeneration. Hence, improving CVD health and maintaining high CD4 is critical.

Age-related changes in the healthy adult visual pathway: evidence from diffusion tensor imaging with fixel-based analysis.

Fixel-based analysis (FBA) is anew method that overcomes the technical limitations of diffusion tensor imaging (DTI) by enabling the characterization of multiple fiber populations within avoxel, and provides biologically meaningful indicators. This study aimed to explore age-related changes in the visual pathway in healthy adults and to observe differences in imaging quality between data collected using different b‑values. In this prospective cross-sectional study, brain DTI scans which were collected with more than six uniformly distributed gradient directions and higher b‑values (up to 2000 s/mm2) than traditional DTI were performed in 72healthy adults across the adult lifespan (20-79years). After image preprocessing, FBA was used to process the dataset. At the same time, conventional DTI metrics were also calculated. Pearson's correlation analysis showed that DTI parameters of white matter (optic nerve, optic chiasma, optic tract, and optic radiation) in the optic pathway were correlated with age. FA values were negatively correlated with age, while MD/AD/RD showed apositive correlation (P < 0.05). FBA showed that the index including FD/FC/FDC tended to decline with age (P < 0.05). Linear regression analysis showed alinear relationship between DTI metrics of the dataset collected by b‑values of 1000 and 2000 s/mm2 (P < 0.05). FBA provides auseful method to assess age-related changes in the visual pathway, which is sensitive to diffusion. In addition, the b‑value influences DTI parameters and signal-to-noise ratio of the image.

Generalized q‐sampling imaging‐based fiber tracking measures of white matter tracts in cognitive impairment

AbstractBackgroundDiffusion magnetic resonance imaging (dMRI) is a non‐invasive technique for exploring white matter fiber organization of the brain. As traditional diffusion tensor imaging (DTI) methods may provide inaccurate descriptions of local crossing and diverging fibers, we examined generalized q‐sampling imaging (GQI), a novel method providing better directional and quantitative information using dMRI data from community‐dwelling older adults.MethodWe examined baseline dMRI data from 227 adults aged 71±8 on average from the Wake Forest Alzheimer’s Disease Research Center’s Clinical Core, who had baseline and year‐1 follow‐up global Clinical Dementia Rating (CDR) score of 0 (indicates normal cognitive function) and 0.5 (akin to MCI). The primary outcome measure was cognitive migration status determined by CDR increase/decrease between baseline and follow‐up (mean difference = 13.9 months), shown in Figure. We performed atlas‐based fiber tracking analysis using GQ in DSI Studio to generate normalized quantitative anisotropy (NQA), DTI fractional anisotropy (DTI_FA) and restricted diffusion (RDI) measures in several AD‐related white matter (WM) tracts: Corpus Callosum (CC), Fornix, Cingulum bundle/Parahippocampal tracts (see Table 2: CFP; CP; CPP), Uncinate Fasciculus (UF), Superior Longitudinal Fasciculus (SLF) and Inferior Fronto‐Occipital Fasciculus (IFOF). We performed multinomial logistic regression (MLR) models adjusted for age, sex, and education, using cognitive migration status as the outcome and standardized GQI‐based diffusion indices sNQA, sRDI, and sDTI_FA as predictors.Result Table 1 lists the baseline demographics and biomarker characteristics for cognitive migration groups. MLR results in Table 2 show significant associations between diffusion indices and tracts for outcome 4 (Reversion) in reference to outcome 1 (stability of normal cognition). Decrease in sNQA in bilateral CP and right Fornix was significantly associated with higher odds of Reversion relative to the reference. Increase in sDTI_FA in left IFOF was associated with higher odds of reversion relative to reference. Decrease in sRDI in bilateral CFP, CPP, Fornix and UF and right CP was associated with higher reversion relative to reference.ConclusionIn early trajectories of cognitive impairment, RDI may be a sensitive diffusion marker for cognitive reversion in several AD‐related WM tracts. Future work will validate these markers in other cognitive migration groups (negative migration and MCI stability).

Generalized q‐sampling imaging‐based fiber tracking measures of white matter tracts in cognitive impairment

AbstractBackgroundDiffusion magnetic resonance imaging (dMRI) is a non‐invasive technique for exploring white matter fiber organization of the brain. As traditional diffusion tensor imaging (DTI) methods may provide inaccurate descriptions of local crossing, kissing and diverging fibers, we examined generalized q‐sampling imaging (GQI), a novel method providing better directional and quantitative information using dMRI data from community‐dwelling older adults.MethodWe examined baseline dMRI data from 227 adults aged 71±8 on average from the Wake Forest Alzheimer’s Disease Research Center’s Clinical Core, who had baseline and year‐1 follow‐up global Clinical Dementia Rating (CDR) score of 0 (indicates normal cognitive function) and 0.5 (akin to mild cognitive impairment [MCI]). The primary outcome measure was cognitive migration status determined by CDR increase/decrease between baseline and follow‐up (mean difference=13.9 months), shown in Figure. We performed atlas‐based fiber tracking analysis using GQI in DSI Studio to generate normalized quantitative anisotropy (NQA), DTI fractional anisotropy (DTI_FA) and restricted diffusion (RDI) measures in several AD‐related white matter (WM) tracts: Corpus Callosum (CC), Fornix, Cingulum bundle/Parahippocampal tracts (CFP; CP; CPP), Uncinate Fasciculus (UF), Superior Longitudinal Fasciculus (SLF) and Inferior Fronto‐Occipital Fasciculus (IFOF). We performed multinomial logistic regression (MLR) models adjusted for age, sex, and education, using cognitive migration status as the outcome, and standardized GQI‐based diffusion indices sNQA, sRDI, and sDTI_FA as predictors.ResultTable 1 lists the baseline demographics and biomarker characteristics for cognitive migration groups. MLR results in Table 2 show significant associations between diffusion indices and tracts for outcome 4 (Reversion) in reference to outcome 1 (stability of normal cognition). Decrease in sNQA in bilateral CP and right Fornix was significantly associated with higher odds of Reversion relative to the reference. Increase in sDTI_FA in left IFOF was associated with higher odds of reversion relative to reference. Decrease in sRDI in bilateral CFP, CPP, Fornix and UF and right CP was associated with higher reversion relative to reference.ConclusionIn early trajectories of cognitive impairment, RDI may be a sensitive diffusion marker for cognitive reversion in several AD‐related WM tracts. Future work will validate these markers in other cognitive migration groups (negative migration and MCI stability).

Free-water imaging of the cholinergic basal forebrain and pedunculopontine nucleus in Parkinson's disease.

Free-water imaging can predict and monitor dopamine system degeneration in people with Parkinson's disease. It can also enhance the sensitivity of traditional diffusion tensor imaging (DTI) metrics for indexing neurodegeneration. However, these tools are yet to be applied to investigate cholinergic system degeneration in Parkinson's disease, which involves both the pedunculopontine nucleus and cholinergic basal forebrain. Free-water imaging, free-water-corrected DTI and volumetry were used to extract structural metrics from the cholinergic basal forebrain and pedunculopontine nucleus in 99 people with Parkinson's disease and 46 age-matched controls. Cognitive ability was tracked over 4.5 years. Pearson's partial correlations revealed that free-water-corrected DTI metrics in the pedunculopontine nucleus were associated with performance on cognitive tasks that required participants to make rapid choices (behavioural flexibility). Volumetric, free-water content and DTI metrics in the cholinergic basal forebrain were elevated in a sub-group of people with Parkinson's disease with evidence of cognitive impairment, and linear mixed modelling revealed that these metrics were differently associated with current and future changes to cognition. Free water and free-water-corrected DTI can index cholinergic degeneration that could enable stratification of patients in clinical trials of cholinergic interventions for cognitive decline. In addition, degeneration of the pedunculopontine nucleus impairs behavioural flexibility in Parkinson's disease, which may explain this region's role in increased risk of falls.

Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping.

Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.

White Matter Abnormalities and Cognitive Deficit After Mild Traumatic Brain Injury: Comparing DTI, DKI, and NODDI.

White matter (WM) disruption is an important determinant of cognitive impairment after mild traumatic brain injury (mTBI), but traditional diffusion tensor imaging (DTI) shows some limitations in assessing WM damage. Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) show advantages over DTI in this respect. Therefore, we used these three diffusion models to investigate complex WM changes in the acute stage after mTBI. From 32 mTBI patients and 31 age-, sex-, and education-matched healthy controls, we calculated eight diffusion metrics based on DTI (fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity), DKI (mean kurtosis), and NODDI (orientation dispersion index, volume fraction of intracellular water (Vic), and volume fraction of the isotropic diffusion compartment). We used tract-based spatial statistics to identify group differences at the voxel level, and we then assessed the correlation between diffusion metrics and cognitive function. We also performed subgroup comparisons based on loss of consciousness. Patients showed WM abnormalities and cognitive deficit. And these two changes showed positive correlation. The correlation between Vic of the splenium of the corpus callosum and Digit Symbol Substitution Test scores showed the smallest p-value (p = 0.000, r = 0.481). We concluded that WM changes, especially in the splenium of the corpus callosum, correlate to cognitive deficit in this study. Furthermore, the high voxel count of NODDI results and the consistency of mean kurtosis and the volume fraction of intracellular water in previous studies and our study showed the functional complementarity of DKI and NODDI to DTI.

A Preliminary DTI Tractography Study of Developmental Neuroplasticity 5-15 Years After Early Childhood Traumatic Brain Injury.

Plasticity is often implicated as a reparative mechanism when addressing structural and functional brain development in young children following traumatic brain injury (TBI); however, conventional imaging methods may not capture the complexities of post-trauma development. The present study examined the cingulum bundles and perforant pathways using diffusion tensor imaging (DTI) in 21 children and adolescents (ages 10–18 years) 5–15 years after sustaining early childhood TBI in comparison with 19 demographically-matched typically-developing children. Verbal memory and executive functioning were also evaluated and analyzed in relation to DTI metrics. Beyond the expected direction of quantitative DTI metrics in the TBI group, we also found qualitative differences in the streamline density of both pathways generated from DTI tractography in over half of those with early TBI. These children exhibited hypertrophic cingulum bundles relative to the comparison group, and the number of tract streamlines negatively correlated with age at injury, particularly in the late-developing anterior regions of the cingulum; however, streamline density did not relate to executive functioning. Although streamline density of the perforant pathway was not related to age at injury, streamline density of the left perforant pathway was significantly and positively related to verbal memory scores in those with TBI, and a moderate effect size was found in the right hemisphere. DTI tractography may provide insight into developmental plasticity in children post-injury. While traditional DTI metrics demonstrate expected relations to cognitive performance in group-based analyses, altered growth is reflected in the white matter structures themselves in some children several years post-injury. Whether this plasticity is adaptive or maladaptive, and whether the alterations are structure-specific, warrants further investigation.

Advanced diffusion‐weighted MRI methods demonstrate improved sensitivity to white matter aging: Percentile charts for over 15,000 UK Biobank participants

AbstractBackgroundAging and Alzheimer’s disease are both associated with alterations in the brain’s white matter. Understanding how the brain’s white matter changes as we age may also improve our understanding of processes involved in Alzheimer’s disease. Here we investigated the ability of both traditional and advanced diffusion‐weighted MRI methods to capture age effects on white matter microstructure in a large‐scale, population‐based sample of middle‐aged and older adults.MethodDiffusion‐weighted MRI (dMRI) scans were analyzed from 15,628 UK Biobank participants (age: 45‐80 years; 47.6% male). Diffusivity metrics were derived using four dMRI reconstruction models, including traditional diffusion tensor imaging (DTI) and the following advanced dMRI models: the tensor distribution function (TDF), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAPMRI). Metrics were projected to a standard white matter skeleton using publicly available ENIGMA protocols based on FSL’s tract‐based spatial statistics and the JHU atlas. Mean values for each dMRI metric were extracted from the whole white matter skeleton (full WM) and for the corpus callosum (CC). We investigated effects of age, sex, and their interaction on each dMRI metric for the full WM and the CC separately, to rank the sensitivity of these different metrics. Sex‐stratified centile reference curves were created for each dMRI metric to provide normative charts of white matter aging for this population.ResultParticipant age and sex were both significantly associated with full WM and CC microstructure for nearly all dMRI metrics, with TDF exhibiting the greatest sensitivity to age and NODDI the greatest sensitivity to participant sex (Figure 1). A significant age by sex interaction was observed in the full WM and the CC for many dMRI measures; NODDI detected this interaction with the greatest sensitivity. (Figure 1). Normative centile reference curves are presented for all dMRI indices separately for men and women (Figures 2 and 3).ConclusionParticipant age and sex robustly impact the brain’s white matter microstructure, with advanced dMRI models displaying the greatest sensitivity to such differences. These findings offer a normative reference to guide future studies and treatment of Alzheimer’s disease.

Investigation of Cerebral White Matter Changes After Spinal Cord Injury With a Measure of Fiber Density.

Remote neurodegenerative changes in supraspinal white matter (WM) can manifest after central lesions such as spinal cord injury (SCI). The majority of diffusion tensor imaging (DTI) studies use traditional metrics such as fractional anisotropy (FA) and mean diffusivity (MD) to investigate microstructural changes in cerebral WM after SCI. However, interpretation of FA readouts is often challenged by inherent limitations of the tensor model. Recent developments in novel diffusion markers, such as fiber density (FD), allows more accurate depictions of WM pathways and has shown more reliable quantification of WM alterations compared to FA in recent studies of neurological diseases. This study investigated if FD provides useful characterization of supraspinal WM integrity after SCI in addition to the traditional DTI readouts. FA, MD, and FD maps were derived from diffusion datasets of 20 patients with chronic SCI and compared with 19 healthy controls (HC). Group differences were investigated across whole brain WM using tract-based spatial statistics and averaged diffusion values of the corticospinal tract (CST) and thalamic radiation (TR) were extracted for comparisons between HC and SCI subgroups. We also related diffusion readouts of the CST and TR with clinical scores of sensorimotor function. To investigate which diffusion markers of the CST and TR delineate HC and patients with SCI a receiver operating characteristic (ROC) analysis was performed. Overall, patients with an SCI showed decreased FA of the TR and CST. ROC analysis differentiated HC and SCI based on diffusion markers of large WM tracts including FD of the TR. Furthermore, patients' motor function was positively correlated with greater microstructural integrity of the CST. While FD showed the strongest correlation, motor function was also associated with FA and MD of the CST. In summary, microstructural changes of supraspinal WM in patients with SCI can be detected using FD as a complementary marker to traditional DTI readouts and correlates with their clinical characteristics. Future DTI studies may benefit from utilizing this novel marker to investigate complex large WM tracts in patient cohorts with varying presentations of SCI or neurodegenerative diseases.

White matter changes following experimental pediatric traumatic brain injury: an advanced diffusion-weighted imaging investigation.

Pediatric traumatic brain injury (pTBI) is a major community health concern. Due to ongoing maturation, injury to the brain at a young age can have devastating consequences in later life. However, how pTBI affects brain development, including white matter maturation, is still poorly understood. Here, we used advanced diffusion weighted imaging (DWI) to assess chronic white matter changes after experimental pTBI. Mice at post-natal day 21 sustained a TBI using the controlled cortical impact model and magnetic resonance imaging (MRI) was performed at 6 months post-injury using a 4.7T Bruker scanner. Four diffusion shells with 81 directions and b-values of 1000, 3000, 5000, and 7000s/mm2 were acquired and analyzed using MRtrix3 software. Advanced DWI metrics, including fiber density, fiber cross-section and a combined fiber density and cross-section measure, were investigated together with three track-weighted images (TWI): the average pathlength map, mean curvature and the track density image. These advanced metrics were compared to traditional diffusion tensor imaging (DTI) metrics which indicated that TBI injured mice had reduced fractional anisotropy and increased radial diffusivity in the white matter when compared to age-matched sham controls. Consistent with previous findings, fiber density and TWI metrics appeared to be more sensitive to white matter changes than DTI metrics, revealing widespread reductions in fiber density and TWI metrics in pTBI mice compared to sham controls. These results provide additional support for the use of advanced DWI metrics in assessing white matter degeneration following injury and highlight the chronic outcomes that can follow pTBI.

Examining brain white matter after pediatric mild traumatic brain injury using neurite orientation dispersion and density imaging: An A-CAP study

BackgroundPediatric mild traumatic brain injury (mTBI) affects millions of children annually. Diffusion tensor imaging (DTI) is sensitive to axonal injuries and white matter microstructure and has been used to characterize the brain changes associated with mild traumatic brain injury (mTBI). Neurite orientation dispersion and density imaging (NODDI) is a diffusion model that can provide additional insight beyond traditional DTI metrics, but has not been examined in pediatric mTBI. The goal of this study was to employ DTI and NODDI to gain added insight into white matter alterations in children with mTBI compared to children with mild orthopedic injury (OI). MethodsChildren (mTBI n = 320, OI n = 176) aged 8–16.99 years (12.39 ± 2.32 years) were recruited from emergency departments at five hospitals across Canada and underwent 3 T MRI on average 11 days post-injury. DTI and NODDI metrics were calculated for seven major white matter tracts and compared between groups using univariate analysis of covariance controlling for age, sex, and scanner type. False discovery rate (FDR) was used to correct for multiple comparisons. ResultsUnivariate analysis revealed no significant group main effects or interactions in DTI or NODDI metrics. Fractional anisotropy and neurite density index in all tracts exhibited a significant positive association with age and mean diffusivity in all tracts exhibited a significant negative association with age in the whole sample. ConclusionsOverall, there were no significant differences between mTBI and OI groups in brain white matter microstructure from either DTI or NODDI in the seven tracts. This indicates that mTBI is associated with relatively minor white matter differences, if any, at the post-acute stage. Brain differences may evolve at later stages of injury, so longitudinal studies with long-term follow-up are needed.

White matter microstructural differences identified using multi-shell diffusion imaging in six-year-old children born very preterm.

IntroductionThe underlying microstructural properties of white matter differences in children born very preterm (<32 weeks gestational age) can be investigated in depth using multi-shell diffusion imaging. The present study compared white matter across the whole brain using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics in children born very preterm and full-term children at six years of age. We also investigated associations between white matter microstructure with early brain injury and developmental outcomes.MethodMulti-shell diffusion imaging, T1-weighted anatomical MR images and developmental assessments were acquired in 23 children born very preterm (16 males; mean scan age: 6.57 ± 0.34 years) and 24 full-term controls (10 males, mean scan age: 6.62 ± 0.37 years). DTI metrics were obtained and neurite orientation dispersion index (ODI) and density index (NDI) were estimated using the NODDI diffusion model. FSL's tract-based spatial statistics were performed on traditional DTI metrics and NODDI metrics. Voxel-wise comparisons were performed to test between-group differences and within-group associations with developmental outcomes (intelligence and visual motor abilities) as well as early white matter injury and germinal matrix/intraventricular haemorrhage (GMH/IVH).ResultsIn comparison to term-born children, the children born very preterm exhibited lower fractional anisotropy (FA) across many white matter regions as well as higher mean diffusivity (MD), radial diffusivity (RD), and ODI. Within-group analyses of the children born very preterm revealed associations between higher FA and NDI with higher IQ and VMI. Lower ODI was found within the corona radiata in those with a history of white matter injury. Within the full-term group, associations were found between higher NDI and ODI with lower IQ.ConclusionChildren born very preterm exhibit lower FA and higher ODI than full-term children. NODDI metrics provide more biologically specific information beyond DTI metrics as well as additional information of the impact of prematurity and white matter microstructure on cognitive outcomes at six years of age.

Super-Resolution Track-Density Imaging Reveals Fine Anatomical Features in Tree Shrew Primary Visual Cortex and Hippocampus.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to study white and gray matter (GM) micro-organization and structural connectivity in the brain. Super-resolution track-density imaging (TDI) is an image reconstruction method for dMRI data, which is capable of providing spatial resolution beyond the acquired data, as well as novel and meaningful anatomical contrast that cannot be obtained with conventional reconstruction methods. TDI has been used to reveal anatomical features in human and animal brains. In this study, we used short track TDI (stTDI), a variation of TDI with enhanced contrast for GM structures, to reconstruct direction-encoded color maps of fixed tree shrew brain. The results were compared with those obtained with the traditional diffusion tensor imaging (DTI) method. We demonstrated that fine microstructures in the tree shrew brain, such as Baillarger bands in the primary visual cortex and the longitudinal component of the mossy fibers within the hippocampal CA3 subfield, were observable with stTDI, but not with DTI reconstructions from the same dMRI data. The possible mechanisms underlying the enhanced GM contrast are discussed.

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI.

BackgroundIt is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)‐based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).MethodsThirty‐two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross‐validation (CV) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian‐based method.ResultsMean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 (MK), 0.68 (FA), and 0.51 (MD). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes.ConclusionThese results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE‐associated pathological features than DTI. Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non‐Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE. This is important since treatment outcome varies dependent on type of TLE.