Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation.

OBJECTIVES: Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth. METHODS: Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes. RESULTS: Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05). CONCLUSIONS: CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

Correlations between DKI and DWI with Ki-67 in gastric adenocarcinoma.

BACKGROUND: Diffusion kurtosis imaging (DKI) has been applied for gastric adenocarcinoma. Correlations between its parameters and Ki-67 are unclear. PURPOSE: To investigate the correlation between DKI and diffusion-weighted imaging (DWI) parameters with the Ki-67 index in gastric adenocarcinoma. MATERIAL AND METHODS: A total of 54 patients with gastric adenocarcinoma were enrolled in the study and underwent DWI and DKI at 3.0-T MRI before surgery. Based on the settings of the regions of interest, the DWI and DKI parameters (including apparent diffusion coefficient [ADC], diffusion kurtosis [K], and diffusion coefficient [DK]) of each patient's gastric adenocarcinoma were measured and calculated. The participants were divided into two groups (low Ki-67 group and high Ki-67 groups). The intraclass correlation coefficient (ICC) and independent-sample t-test were used to compare differences in each parameter between two groups. Spearman's correlation coefficient was calculated to determine the correlation between Ki-67 and the parameters. Each parameter was compared using the area under the receiver operating characteristic curve. All parameters were included in the multivariate logistic regression analysis to explore the relationship between each parameter and high Ki-67 index. RESULTS: ADC and DK were negatively relevant with Ki-67 and K was positively relevant with Ki-67 in gastric adenocarcinoma. ADC, DK, and K had diagnostic efficiency in differentiating the low Ki-67 group from the high Ki-67 group. A higher K value independently predicted a high Ki-67 status. CONCLUSION: DWI and DKI reflected the proliferative characteristics of gastric adenocarcinoma. K was the strongest independent factor for predicting high Ki-67 status.

Multiparametric MRI-guided Hypofractionated Intensity-modulated Radiation Therapy With Simultaneous Integrated Boost for Glioblastoma.

BACKGROUND/AIM: This study investigated the feasibility and efficacy of multiparametric magnetic resonance imaging (MRI)-guided dose-escalated hypofractionated intensity-modulated radiation therapy with simultaneous integrated boost (IMRT-SIB) for glioblastoma. PATIENTS AND METHODS: Eighteen patients underwent postoperative IMRT-SIB for glioblastoma using three MRI sequences: double inversion recovery (DIR), diffusion tensor imaging (DTI), and post-gadolinium T1-weighted imaging. Prescribed doses were 60 Gy and 40 Gy in 15 fractions for residual enhancing lesions and surrounding tumor-infiltrating areas, respectively. For surrounding tumor-infiltrating areas, asymmetric margins were set with reference to DTI imaging. RESULTS: The 1-year overall survival rate was 58.0%, and the 1-year local control rate for the residual enhancing lesions was 76.2%, while that for surrounding tumor-infiltrating areas was 39.4%. One patient (6%) developed grade 2 cerebral radiation necrosis 10 months after IMRT-SIB, but there was no grade 3 or higher adverse event. CONCLUSION: Multiparametric MRI-guided dose-escalated IMRT-SIB with DIR and DTI imaging has the potential to improve local control rates without increasing adverse events.

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

Nucleus accumbens projections: Validity and reliability of fiber reconstructions based on high-resolution diffusion-weighted MRI.

Clinical effects of deep brain stimulation are largely mediated by the activation of myelinated axons. Hence, increasing attention has been paid in the past on targeting white matter tracts in addition to gray matter. Aims of the present study were: (i) visualization of discrete afferences and efferences of the nucleus accumbens (NAc), supposed to be a major hub of neural networks relating to mental disorders, using probabilistic fiber tractography and a data driven approach, and (ii) validation of the applied methodology for standardized routine clinical applications. MR-data from 11 healthy subjects and 7 measurement sessions each were acquired on a 3T MRI-scanner. For probabilistic fiber tracking the NAc as a seed region and the medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), amygdala (AMY), hippocampus (HPC), dorsomedial thalamus (dmT) and ventral tegmental area (VTA) as target regions were segmented for each subject and both hemispheres. To quantitatively assess the reliability and stability of the reconstructions, we filtered and clustered the individual fiber-tracts (NAc to target) for each session and subject and performed a point-by-point calculation of the maximum cluster distances for intra-subject comparison. The connectivity patterns formed by the obtained fibers were in good concordance with published data from tracer and/or fiber-dissection studies. Furthermore, the reliability assessment of the (NAc to target)-fiber-tracts yielded to high correlations between the obtained clustered-tracts. Using DBS with directional lead technology, the workflow elaborated in this study may guide selective electrical stimulation of NAc projections.

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

Progressive microstructural alterations in subcortical nuclei in Parkinson's disease: A diffusion magnetic resonance imaging study.

OBJECTIVES: To explore the microstructural alterations in subcortical nuclei in Parkinson's disease (PD) at different stages with diffusion kurtosis imaging (DKI) and tensor imaging and to test the performance of diffusion metrics in identifying PD. METHODS: 108 PD patients (64 patients in early-stage PD group (EPD) and 44 patients in moderate-late-stage PD group (MLPD)) and 64 healthy controls (HC) were included. Tensor and kurtosis metrics in the subcortical nuclei were compared. Partial correlation was used to correlate the diffusion metrics and Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) score. Logistic regression and receiver operating characteristic analysis were applied to test the diagnostic performance of the diffusion metrics. RESULTS: Compared with HC, both EPD and MLPD patients showed higher fractional anisotropy and axial diffusivity, lower mean kurtosis (MK) and axial kurtosis in substantia nigra, lower MK and radial kurtosis (RK) in globus pallidus (GP) and thalamus (all p < 0.05). Compared with EPD, MLPD patients showed lower MK and RK in GP and thalamus (all p < 0.05). MK and RK in GP and thalamus were negatively correlated with UPDRS-III score (all p < 0.01). The logistic regression model combining kurtosis and tensor metrics showed the best performance in diagnosing PD, EPD, and MLPD (areas under curve were 0.817, 0.769, and 0.914, respectively). CONCLUSIONS: PD has progressive microstructural alterations in the subcortical nuclei. DKI is sensitive to detect microstructural alterations in GP and thalamus during PD progression. Combining kurtosis and tensor metrics can achieve a good performance in diagnosing PD.

Strengths and challenges of longitudinal non-human primate neuroimaging.

Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.

High-fidelity diffusion tensor imaging of the cervical spinal cord using point-spread-function encoded EPI.

Diffusion tensor imaging (DTI) of the spinal cord is technically challenging due to the size of its structure and susceptibility-induced field inhomogeneity, which impedes clinical applications. This study aimed to achieve high-fidelity spinal cord DTI with reasonable SNR and practical acquisition efficiency. Particularly, a distortion-free multi-shot EPI technique, namely point-spread-function encoded EPI (PSF-EPI), was adopted for diffusion imaging of the cervical spinal cord (CSC). The shot number can be reduced to six for sagittal scans through titled-CAIPI acceleration and partial Fourier undersampling, consequently rendering this technique beneficial in clinics. Fifteen healthy volunteers and seven patients with metallic implants underwent sagittal scans using tilted-CAIPI PSF-EPI at 3T. Unsuppressed fat signals were further removed by retrospective water/fat separation using the intrinsic chemical-shift encoded signals. Compared with multi-shot interleaved EPI method, highly accelerated PSF-EPI method provided evidently improved distortion reduction and higher consistency with anatomical references even with metallic implants. Additionally, axial DTI scans using PSF-EPI were also evaluated quantitatively, and the measured DTI metrics are similar to those obtained from the zonal oblique multi-slice EPI (ZOOM-EPI) method and reported values. The high anatomical consistency, practical scan time and quantitative reliability indicate PSF-EPI's clinical potential for CSC diffusion imaging.

Characterization and diagnostic potential of diffusion tractography in multiple system atrophy.

INTRODUCTION: Microstructural integrity of the middle cerebellar peduncle (MCP) and the putamen captured by diffusion-tensor imaging (DTI) is differentially affected in the parkinsonian and cerebellar variants of multiple system atrophy (MSA-P, MSA-C) compared to Parkinson's disease (PD). The current study applied DTI and tractography in order to 1) characterize the distribution of DTI metrics along the tracts of the MCP and from the putamen in MSA variants, and 2) evaluate the usefulness of combining these measures for the differential diagnosis of MSA-P against PD in the clinical setting. METHODS: Twenty-nine MSA patients (MSA-C, n = 10; MSA-P, n = 19), with a mean disease duration of 2.8 ± 1.7 years, 19 PD patients, and 27 healthy controls (HC) were included in the study. Automatized tractography with a masking procedure was employed to isolate the MCP tracts. DTI measures along the tracts of the MCP and within the putamen were acquired and jointly used to classify MSA vs. PD, and MSA-P vs. PD. Putamen volume was additionally tested as classification feature in post hoc analyses. RESULTS: DTI measures within the MCP and putamen showed significant alterations in MSA variants compared to HC and PD. Classification accuracy for MSA vs. PD and MSA-P vs PD using diffusion measures was 91.7% and 89.5%, respectively. When replacing the putaminal DTI measure by a normalized measure of putamen volume classification accuracy improved to 95.8% and 94.7%, respectively. CONCLUSION: Multimodal information from MCP tractography and putamen volume yields excellent diagnostic accuracy to discriminate between early-to-moderately advanced patients with MSA and PD.

SPECTRE-A novel dMRI visualization technique for the display of cerebral connectivity.

The visualization of diffusion MRI related properties in a comprehensive way is still a challenging problem. We propose a simple visualization technique to give neuroradiologists and neurosurgeons a more direct and personalized view of relevant connectivity patterns estimated from clinically feasible diffusion MRI. The approach, named SPECTRE (Subject sPEcific brain Connectivity display in the Target REgion), is based on tract-weighted imaging, where diffusion MRI streamlines are used to aggregate information from a different MRI contrast. Instead of using native MRI contrasts, we propose to use continuous template information as the underlying contrast for aggregation. In this respect, the SPECTRE approach is complementary to normative approaches where connectivity information is warped from the group level to subject space by anatomical registration. For the purpose of demonstration, we focus the presentation of the SPECTRE approach on the visualization of connectivity patterns in the midbrain regions at the level of subthalamic nucleus due to its importance for deep brain stimulation. The proposed SPECTRE maps are investigated with respect to plausibility, robustness, and test-retest reproducibility. Clear dependencies of reliability measures with respect to the underlying tracking algorithms are observed.

Tractography Processing with the Sparse Closest Point Transform.

We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.

Along-tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors.

Along-tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities, new parameters reflecting the relative contribution of different diffusion compartments in the tissue can be estimated through advanced diffusion MRI methods as neurite orientation dispersion and density imaging (NODDI), leading to a more specific microstructural characterization. In this study, we extracted both DTI- and NODDI-derived quantitative microstructural diffusion metrics along the most eloquent fiber tracts in 15 healthy subjects and in 22 patients with brain tumors. We obtained a robust intraprotocol reference database of normative along-tract microstructural metrics, and their corresponding plots, from healthy fiber tracts. Each diffusion metric of individual patient's fiber tract was then plotted and statistically compared to the normative profile of the corresponding metric from the healthy fiber tracts. NODDI-derived metrics appeared to account for the pathological microstructural changes of the peritumoral tissue more accurately than DTI-derived ones. This approach may be useful for future studies that may compare healthy subjects to patients diagnosed with other pathological conditions.

Altered glymphatic system in idiopathic normal pressure hydrocephalus.

OBJECTIVE: To assess the glymphatic activity in patients with idiopathic normal pressure hydrocephalus (NPH) using the "Diffusion Tensor Image-Analysis aLong the Perivascular Space (DTI-ALPS)" method, and determine the feasibility of non-invasive MRI for the evaluation of the glymphatic function. METHODS: Between April 2017 and March 2019, 16 patients diagnosed with NPH and 16 age- and sex-matched controls were included. On 3T DTI-MRI, the diffusivities along x-, y-, and z-axes were measured, and the ALPS-index - a ratio that accentuated water diffusion along the perivascular space - was calculated by two independent readers. The inter-observer agreement was tested using the interclass correlation coefficient. The differences in the diffusivities and the ALPS-index between the NPH and control groups were compared using the Mann-Whitney test. The values were also compared according to the treatment response to the cerebrospinal fluid drainage and correlated with the callosal angle using a correlation coefficient. RESULTS: The inter-observer agreements were excellent for the diffusivities and the ALPS-index. The diffusivity along the x-axis in the projection fibers area and the ALPS-index were significantly lower in patients with NPH (median, 0.556/1.181) than in the controls (0.610/1.540), respectively (P = 0.032/< 0.0001). The ALPS-index was significantly lower in the NPH group who did not show treatment response than those who showed symptomatic relief (0.987/1.329; P < 0.0001). The ALPS-index showed a significant positive correlation with the callosal angle (r = 0.82, P = 0.0001). CONCLUSIONS: The DTI-ALPS method can be a useful imaging tool for identifying glymphatic dysfunction and for individually quantifying glymphatic activity in patients with NPH.

Evaluation of Diffusion Tensor Imaging and Fluid Based Biomarkers in a Large Animal Trial of Cyclosporine in Focal Traumatic Brain Injury.

All phase III trials evaluating medical treatments for traumatic brain injury (TBI), performed to date, have failed. To facilitate future success there is a need for novel outcome metrics that can bridge pre-clinical studies to clinical proof of concept trials. Our objective was to assess diffusion tensor imaging (DTI) and biofluid-based biomarkers as efficacy outcome metrics in a large animal study evaluating the efficacy of cyclosporine in TBI. This work builds on our previously published study that demonstrated a reduced volume of injury by 35% with cyclosporine treatment based on magnetic resonance imaging (MRI) results. A focal contusion injury was induced in piglets using a controlled cortical impact (CCI) device. Cyclosporine in a novel Cremophor/Kolliphor EL-free lipid emulsion, NeuroSTAT, was administered by continuous intravenous infusion for 5 days. The animals underwent DTI on day 5. Glial fibrillary acidic protein (GFAP), as a measure of astroglia injury, and neurofilament light (NF-L), as a measure of axonal injury, were measured in blood on days 1, 2, and 5, and in cerebrospinal fluid (CSF) on day 5 post-injury. Normalized fractional anisotropy (FA) was significantly (p = 0.027) higher in in the treatment group, indicating preserved tissue integrity with treatment. For the biomarkers, we observed a statistical trend of a decreased level of NF-L in CSF (p = 0.051), in the treatment group relative to placebo, indicating less axonal injury. Our findings suggest that DTI, and possibly CSF NF-L, may be feasible as translational end-points assessing neuroprotective drugs in TBI.

Diffusion Tensor Imaging Tractography Detecting Isolated Oculomotor Paralysis Caused by Pituitary Apoplexy.

OBJECTIVES: Pituitary apoplexy (PA)-induced oculomotor palsy, although rare, can be caused by compression on the lateral wall of the cavernous sinus. This study aimed to visualize PA-induced oculomotor nerve damage using diffusion tensor imaging (DTI) tractography. MATERIALS AND METHODS: We enrolled 5 patients with PA-induced isolated oculomotor palsy (patient group) and 10 healthy participants (control group); all underwent DTI tractography preoperatively. Fractional anisotropy (FA) and mean diffusion (MD) values of the cisternal portion of the bilateral oculomotor nerve were measured. DTI tractography was repeated after the recovery of oculomotor palsy. RESULTS: While no statistical difference was observed in FA and MD values of the bilateral oculomotor nerve in the control group (P>0.05), the oculomotor nerve on the affected side was disrupted in the patient group, with a statistical difference in FA and MD values of the bilateral oculomotor nerve (P<0.01). After the recovery of oculomotor palsy, the FA value of the oculomotor nerve on the affected side increased, whereas the MD value decreased (P<0.01). Meanwhile, no significant difference was observed in FA and MD values of the bilateral oculomotor nerve (P>0.05). DTI tractography of the oculomotor nerve on the affected side revealed restoration of integrity. Furthermore, the symptoms of oculomotor palsy improved in all patients 7 days postoperatively. CONCLUSION: DTI tractography could be a helpful adjunct to the standard clinical and paraclinical ophthalmoplegia examinations in patients with PA; thus, this study establishes the feasibility of DTI tractography in this specific clinical setting.

Reliability of pre-operative diffusion tensor imaging parameter measurements of the cervical spine in patients with cervical spondylotic myelopathy.

The present study assessed test-retest and inter-observer reliability of diffusion tensor imaging (DTI) in cervical spondylotic myelopathy (CSM), as well as the agreement among measurement methods. A total 34 patients (12 men, 22 women; mean age, 58.7 [range 45-79] years) who underwent surgical decompression for CSM, with pre-operative DTI scans available, were retrospectively enrolled. Four observers independently measured fractional anisotropy (FA) values twice, using three different measurement methods. Test-retest and inter-observer reliability was assessed using intraclass correlation coefficients (ICCs). Overall, inter-observer agreements varied according to spinal cord level and the measurement methods used, and ranged from poor to excellent agreement (ICC = 0.374-0.821), with relatively less agreement for the sagittal region of interest (ROI) method. The radiology resident and neuro-radiologist group showed excellent test-retest reliability at almost every spinal cord level (ICC = 0.887-0.997), but inter-observer agreements varied from fair to good (ICC = 0.404-0.747). Despite excellent test-retest reliability of the ROI measurements, FA measurements in patients with CSM varied widely in terms of inter-observer reliability. Therefore, DTI parameter data should be interpreted carefully when applied clinically.

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

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

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus.

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

XTRACT - Standardised protocols for automated tractography in the human and macaque brain.

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.