Cortex Parcellation Associated Whole White Matter Parcellation in Individual Subjects

PPA: Principal parcellation analysis for brain connectomes and multiple traits

Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

Purpose

We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity.

Theory

The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β.

Methods

MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract.

Results

The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm^−α.

Conclusion

The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.

Highly Reproducible Whole Brain Parcellation in Individuals via Voxel Annotation with Fiber Clusters

A central goal in systems neuroscience is to parcellate the brain into discrete units that are neurobiologically coherent. Here, we propose a strategy for consistent whole-brain parcellation of white matter (WM) and gray matter (GM) in individuals. We parcellate the brain into coherent parcels using non-negative matrix factorization based on voxel annotation using fiber clusters. Tractography is performed using an algorithm that mitigates gyral bias, allowing full gyral and sulcal coverage for reliable parcellation of the cortical ribbon. Experimental results indicate that parcellation using our approach is highly reproducible with 100% test-retest parcel identification rate and is highly consistent with significantly lower inter-subject variability than FreeSurfer parcellation. This implies that reproducible parcellation can be obtained for subject-specific investigation of brain structure and function.

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.

Regional White Matter Atrophy Correlates with Spike Activity in Encephalopathy Related to Status Epilepticus During Slow Sleep (ESES) After Early Thalamic Lesions

Encephalopathy related to Status Epilepticus during slow Sleep (ESES) is an age-related, epileptic syndrome, which associates cognitive/behavioral disturbances with a peculiar pattern of spike activity. One promising line of research is the study of ESES in cases of early thalamic lesions. We studied 7 ESES patients with unilateral thalamic lesions using magnetic resonance imaging to assess regional white matter (WM) and thalamic nuclei volume differences, and long-term electroencephalogram recordings to localize the epileptogenic cortex. N170 event-related potentials were used to demonstrate the dysfunctional character of the WM abnormalities. Diffusion-weighted images in a subset of 4 patients were used to parcellate the thalamus and evaluate volume asymmetries, based on cortical connectivity. Large WM regional atrophy in the hemisphere with the thalamic lesion was associated with both cortical dysfunction and epileptic activity. A correlation was demonstrated between lesions in the pulvinar and the mediodorsal thalamic nuclei and WM atrophy of the corresponding cortical projection areas. We propose that these abnormalities are due to the widespread structural disconnection produced by the thalamic lesions associated to a yet unknown age-dependent factor. Further exploration of WM regional atrophy association with the spike activity in other etiologies could lend support to the cortical disconnection role in ESES genesis.

Relationship between Measures of Cerebrovascular Reactivity and Intracranial Lesion Progression in Acute Traumatic Brain Injury Patients: A CENTER-TBI Study

Failure of cerebral autoregulation has been linked to unfavorable outcome after traumatic brain injury (TBI). Preliminary evidence from a small, retrospective, single-center analysis suggests that autoregulatory dysfunction may be associated with traumatic lesion expansion, particularly for pericontusional edema. The goal of this study was to further explore these associations using prospective, multi-center data from the Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) and to further explore the relationship between autoregulatory failure, lesion progression, and patient outcome. A total of 88 subjects from the CENTER-TBI High Resolution ICU Sub-Study cohort were included. All patients had an admission computed tomography (CT) scan and early repeat scan available, as well as high-frequency neurophysiological recordings covering the between-scan interval. Using a novel, semiautomated approach at lesion segmentation, we calculated absolute changes in volume of contusion core, pericontusional edema, and extra-axial hemorrhage between the imaging studies. We then evaluated associations between cerebrovascular reactivity metrics and radiological lesion progression using mixed-model regression. Analyses were adjusted for baseline covariates and non-neurophysiological factors associated with lesion growth using multi-variate methods. Impairment in cerebrovascular reactivity was significantly associated with progression of pericontusional edema and, to a lesser degree, intraparenchymal hemorrhage. In contrast, there were no significant associations with extra-axial hemorrhage. The strongest relationships were observed between RAC-based metrics and edema formation. Pulse amplitude index showed weaker, but consistent, associations with contusion growth. Cerebrovascular reactivity metrics remained strongly associated with lesion progression after taking into account contributions from non-neurophysiological factors and mean cerebral perfusion pressure. Total hemorrhagic core and edema volumes on repeat CT were significantly larger in patients who were deceased at 6 months, and the amount of edema was greater in patients with an unfavourable outcome (Glasgow Outcome Scale-Extended 1-4). Our study suggests associations between autoregulatory failure, traumatic edema progression, and poor outcome. This is in keeping with findings from a single-center retrospective analysis, providing multi-center prospective data to support those results.