Using connectomics for predictive assessment of brain parcellations.

The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard. We propose a framework for quantitative evaluation of brain parcellations via statistical prediction of connectomics data. Specifically, we evaluate the extent in which the network representation at the level of cortical units (defined as parcels) accounts for high-resolution brain connectivity. Herein, we assess the pertinence and comparative ranking of ten existing parcellation atlases to account for functional (FC) and structural connectivity (SC) data based on data from the Human Connectome Project (HCP), and compare them to data-driven as well as spatially-homogeneous geometric parcellations including geodesic parcellations with similar size distributions as the atlases. We find substantial discrepancy in parcellation structures that well characterize FC and SC and differences in what well represents an individual's functional connectome when compared against the FC structure that is preserved across individuals. Surprisingly, simple spatial homogenous parcellations generally provide good representations of both FC and SC, but are inferior when their within-parcellation distribution of individual parcel sizes is matched to that of a valid atlas. This suggests that the choice of fine grained and coarse representations used by existing atlases are important. However, we find that resolution is more critical than the exact border location of parcels.

Training shortest-path tractography: Automatic learning of spatial priors.

Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we demonstrate that our framework also retains support for conventional interactive constraints such as waypoint regions. We apply our approach to the open access, high quality Human Connectome Project data, as well as a dataset acquired on a typical clinical scanner. Our results show that the use of a learned prior substantially increases the overlap of tractography output with a reference atlas on both populations, and this is confirmed by visual inspection. Furthermore, we demonstrate how a prior learned on the high quality dataset significantly increases the overlap with the reference for the more typical yet lower quality data acquired on a clinical scanner. We hope that such automatic incorporation of prior knowledge and the obviation of expert interactive tract delineation on every subject, will improve the feasibility of large clinical tractography studies.

Validation of tractography: Comparison with manganese tracing.

In this study, we used invasive tracing to evaluate white matter tractography methods based on ex vivo diffusion-weighted magnetic resonance imaging (dwMRI) data. A representative selection of tractography methods were compared to manganese tracing on a voxel-wise basis, and a more qualitative assessment examined whether, and to what extent, certain fiber tracts and gray matter targets were reached. While the voxel-wise agreement was very limited, qualitative assessment revealed that tractography is capable of finding the major fiber tracts, although there were some differences between the methods. However, false positive connections were very common and, in particular, we discovered that it is not possible to achieve high sensitivity (i.e., few false negatives) and high specificity (i.e., few false positives) at the same time. Closer inspection of the results led to the conclusion that these problems mainly originate from regions with complex fiber arrangements or high curvature and are not easily resolved by sophisticated local models alone. Instead, the crucial challenge in making tractography a truly useful and reliable tool in brain research and neurology lies in the acquisition of better data. In particular, the increase of spatial resolution, under preservation of the signal-to-noise-ratio, is key.

Moving pain management programmes into the digital age: development and evaluation of an online PMP for people with chronic pain.

Introduction: In response to Coronovirus Disease (COVID-19) health care restrictions, the pain management programme delivered group treatment digitally (OPMP). We aimed to: 1) evaluate pain related outcomes of the OPMP, 2) evaluate patient satisfaction and qualitive feedback of the OPMP and 3) compare OPMP outcomes with the pre-pandemic face to face (F2F) PMP outcomes. Methods: Age, gender, pain duration, occupational status, referral information and patient satisfaction data were collected. Pre- and post-treatment pain related outcomes were compared by calculating mean difference, benchmarking with effect size (Cohen's d) and determining clinically significant change (CSC) for OPMP and F2F PMP. Results: Two-hundred and thirty-seven patients provided outcome data, with 60 completing the OPMP and 177 completing the F2F PMP. OPMP patients were 10 years younger than the F2F PMP (44.8 vs 53.3), more were female (6.5:1 vs 2.8:1), more were working (45% vs 27%) and fewer were retired (3% vs 17%). The OPMP showed improvements comparable to the F2F PMP. Large effect size was reported across all outcome domains including objective physical outcomes. Eighty-one percent of OPMP patients were 'extremely likely' to recommend the programme but just over 50% of patients felt F2F would provide greater clinical benefits. Conclusion: The results support that OPMP is effective for carefully selected patients following a multidisciplinary team assessment however more complex cases still require F2F PMP.

The CONNECT project: Combining macro- and micro-structure.

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.

Uncovering Cortical Units of Processing From Multi-Layered Connectomes.

Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.

Long-term global and regional brain volume changes following severe traumatic brain injury: a longitudinal study with clinical correlates.

Traumatic brain injury (TBI) results in neurodegenerative changes that progress for months, perhaps even years post-injury. However, there is little information on the spatial distribution and the clinical significance of this late atrophy. In 24 patients who had sustained severe TBI we acquired 3D T1-weighted MRIs about 8 weeks and 12 months post-injury. For comparison, 14 healthy controls with similar distribution of age, gender and education were scanned with a similar time interval. For each subject, longitudinal atrophy was estimated using SIENA, and atrophy occurring before the first scan time point using SIENAX. Regional distribution of atrophy was evaluated using tensor-based morphometry (TBM). At the first scan time point, brain parenchymal volume was reduced by mean 8.4% in patients as compared to controls. During the scan interval, patients exhibited continued atrophy with percent brain volume change (%BVC) ranging between -0.6% and -9.4% (mean -4.0%). %BVC correlated significantly with injury severity, functional status at both scans, and with 1-year outcome. Moreover, %BVC improved prediction of long-term functional status over and above what could be predicted using functional status at approximately 8 weeks. In patients as compared to controls, TBM (permutation test, FDR 0.05) revealed a large coherent cluster of significant atrophy in the brain stem and cerebellar peduncles extending bilaterally through the thalamus, internal and external capsules, putamen, inferior and superior longitudinal fasciculus, corpus callosum and corona radiata. This indicates that the long-term atrophy is attributable to consequences of traumatic axonal injury. Despite progressive atrophy, remarkable clinical improvement occurred in most patients.

Regional activation of the human medial temporal lobe during intentional encoding of objects and positions.

The medial temporal lobe (MTL) consists of several regions thought to be involved in learning and memory. However, the degree of functional specialization among these regions remains unclear. Previous studies have demonstrated effects of both content and processing stage, but findings have been inconsistent. In particular, studies have suggested that the perirhinal cortex is more involved in object processing than spatial processing, while other regions such as the parahippocampal cortex have been implicated in spatial processing. In this study, functional magnetic resonance imaging (fMRI) optimized for the MTL region was used to probe MTL activation during intentional encoding of object identities or positions. A region of interest analysis showed that object encoding evoked stronger activation than position encoding in bilateral perirhinal cortex, temporopolar cortex, parahippocampal cortex, hippocampus and amygdala. Results also indicate an unexpected significant correlation in activation level between anterior and posterior portions in both the left parahippocampal cortex and left hippocampus. Exploratory analysis did not show any regional content effects during preparation and rehearsal stages. These results provide additional evidence for functional specialization within the MTL, but were less clear regarding the specific nature of content specificity in these regions.

Diffusion tensor imaging during recovery from severe traumatic brain injury and relation to clinical outcome: a longitudinal study.

Diffusion tensor imaging (DTI) has been proposed as a sensitive biomarker of traumatic white matter injury, which could potentially serve as a tool for prognostic assessment and for studying microstructural changes during recovery from traumatic brain injury (TBI). However, there is a lack of longitudinal studies on TBI that follow DTI changes over time and correlate findings with long-term clinical outcome. We performed a prospective longitudinal study of 30 adult patients admitted for subacute rehabilitation following severe traumatic brain injury. DTI and conventional MRI were acquired at mean 8 weeks (5-11 weeks), and repeated in 23 of the patients at mean 12 months (9-15 months) post-trauma. Using a region-of-interest-based approach, DTI parameters were compared to those of healthy matched controls, scanned during the same time period and rescanned with a similar interval as that of patients. At the initial scan, fractional anisotropy was reduced in all the investigated white matter regions in patients compared to controls (P

In vivo study of experimental pneumococcal meningitis using magnetic resonance imaging.

BACKGROUND: Magnetic Resonance Imaging (MRI) methods were evaluated as a tool for the study of experimental meningitis. The identification and characterisation of pathophysiological parameters that vary during the course of the disease could be used as markers for future studies of new treatment strategies. METHODS: Rats infected intracisternally with S. pneumoniae (n = 29) or saline (n = 13) were randomized for imaging at 6, 12, 24, 30, 36, 42 or 48 hours after infection. T1W, T2W, quantitative diffusion, and post contrast T1W images were acquired at 4.7 T. Dynamic MRI (dMRI) was used to evaluate blood-brain-barrier (BBB) permeability and to obtain a measure of cerebral and muscle perfusion. Clinical- and motor scores, bacterial counts in CSF and blood, and WBC counts in CSF were measured. RESULTS: MR images and dMRI revealed the development of a highly significant increase in BBB permeability (P < 0.002) and ventricle size (P < 0.0001) among infected rats. Clinical disease severity was closely related to ventricle expansion (P = 0.024). Changes in brain water distribution, assessed by ADC, and categorization of brain 'perfusion' by cortex DeltaSI(bolus) were subject to increased inter-rat variation as the disease progressed, but without overall differences compared to uninfected rats (P > 0.05). Areas of well-'perfused' muscle decreased with the progression of infection indicative of septicaemia (P = 0.05). CONCLUSION: The evolution of bacterial meningitis was successfully followed in-vivo with MRI. Increasing BBB-breakdown and ventricle size was observed in rats with meningitis whereas changes in brain water distribution were heterogeneous. MRI will be a valuable technique for future studies aiming at evaluating or optimizing adjunctive treatments.

Collocation for Diffeomorphic Deformations in Medical Image Registration.

Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is invertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms. We present CDD (Collocation for Diffeomorphic Deformations), a numerical solution to diffeomorphic image registration, which solves for the Stationary Velocity Field (SVF) using an implicit A-stable collocation method. CDD guarantees the preservation of the diffeomorphic properties at all discrete points and is thereby consistent to machine precision. We compared CDD's collocation method with the following standard methods: Scaling and Squaring, Forward Euler, and Runge-Kutta 4, and found that CDD is up to 9 orders of magnitude more consistent. Finally, we evaluated CDD on a number of standard bench-mark data sets and compared the results with current state-of-the-art methods: SPM-DARTEL, Diffeomorphic Demons and SyN. We found that CDD outperforms state-of-the-art methods in consistency and delivers comparable or superior registration precision.

Addressing the path-length-dependency confound in white matter tract segmentation.

We derive the Iterative Confidence Enhancement of Tractography (ICE-T) framework to address the problem of path-length dependency (PLD), the streamline dispersivity confound inherent to probabilistic tractography methods. We show that PLD can arise as a non-linear effect, compounded by tissue complexity, and therefore cannot be handled using linear correction methods. ICE-T is an easy-to-implement framework that acts as a wrapper around most probabilistic streamline tractography methods, iteratively growing the tractography seed regions. Tract networks segmented with ICE-T can subsequently be delineated with a global threshold, even from a single-voxel seed. We investigated ICE-T performance using ex vivo pig-brain datasets where true positives were known via in vivo tracers, and applied the derived ICE-T parameters to a human in vivo dataset. We examined the parameter space of ICE-T: the number of streamlines emitted per voxel, and a threshold applied at each iteration. As few as 20 streamlines per seed-voxel, and a robust range of ICE-T thresholds, were shown to sufficiently segment the desired tract network. Outside this range, the tract network either approximated the complete white-matter compartment (too low threshold) or failed to propagate through complex regions (too high threshold). The parameters were shown to be generalizable across seed regions. With ICE-T, the degree of both near-seed flare due to false positives, and of distal false negatives, are decreased when compared with thresholded probabilistic tractography without ICE-T. Since ICE-T only addresses PLD, the degree of remaining false-positives and false-negatives will consequently be mainly attributable to the particular tractography method employed. Given the benefits offered by ICE-T, we would suggest that future studies consider this or a similar approach when using tractography to provide tract segmentations for tract based analysis, or for brain network analysis.

Healthy aging attenuates task-related specialization in the human medial temporal lobe.

Recent research on aging has established important links between the neurobiology of normal aging and age-related decline in episodic memory, yet the exact nature of this relationship is still unknown. Functional neuroimaging of regions such as the medial temporal lobe (MTL) have produced conflicting findings. Using functional magnetic resonance imaging (fMRI), we have recently shown that young healthy individuals show a stronger activation of the MTL during encoding of objects as compared with encoding of positions. Using the same encoding task, the present study addressed the question whether this greater MTL activation during encoding of objects varies with age. Fifty-four healthy individuals aged between 18 and 81 years underwent functional magnetic resonance imaging while they encoded and subsequently made new-old judgments on objects and positions. Region of interest (ROI) analysis of task related changes in the blood oxygen level-dependent (BOLD) signal was performed in native space after correction for gender effects and individual differences in cerebral blood flow. The hippocampus, amygdala, and parahippocampal, perirhinal, entorhinal, and temporopolar cortices of right and left hemisphere were defined as ROIs. Aging had an adverse effect on memory performance that was similar for memorizing objects or positions. In left and right MTL, relatively greater activation for object stimuli was attenuated in older individuals. Age-related attenuation in content specificity was most prominent in the recognition stage. During recognition, the larger response to objects gradually decreased with age in all ROIs apart from left temporopolar and entorhinal cortex. An age-related attenuation was also present during encoding, but only in right parahippocampus and amygdala. Our results suggest that memory-related processing in the MTL becomes gradually less sensitive to content during normal aging.

Impact of bacteremia on the pathogenesis of experimental pneumococcal meningitis.

BACKGROUND: Bacteremia plays a major role in the outcome of pneumococcal meningitis. This experimental study investigated how bacteremia influences the pathophysiologic profile of the brain. METHODS: Rats with Streptococcus pneumoniae meningitis were randomized to 1 of 3 groups of infected study rats: (1) rats with attenuated bacteremia resulting from intravenous injection of serotype-specific pneumococcal antibody, (2) rats with early-onset bacteremia resulting from concomitant intravenous infection, or (3) a meningitis control group. The blood-brain barrier (BBB) breakdown, ventricle size, brain water distribution, and brain pathologic findings were analyzed using magnetic resonance morphological and functional imaging. Laboratory data and clinical disease scores were obtained. RESULTS: Attenuation of the bacteremic component of pneumococcal meningitis improved clinical disease symptoms and significantly reduced ventricle expansion and BBB breakdown (P< .05). Early-onset bacteremia did not further increase ventricle size or BBB leakage. Significantly increased brain edema developed among rats with both attenuated and early-onset bacteremia (P< .05). Focal brain pathologic findings were unaffected by bacteremia and were found to be associated with cerebrospinal fluid inflammation. CONCLUSION: Although brain lesions appear to result from local meningeal infection, systemic infection significantly contributes to clinical disease presentation and the pathophysiology of BBB breakdown and ventricle expansion. The different end points affected by the systemic and local infectious processes should be addressed in future studies.

Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling.

In emission tomography, quantification of brain tracer uptake, metabolism or binding requires knowledge of the cerebral input function. Traditionally, this is achieved with arterial blood sampling. We propose a noninvasive alternative via the use of a blood vessel time-activity curve (TAC) extracted directly from dynamic positron emission tomography (PET) scans by cluster analysis. Five healthy subjects were injected with the 5HT(2A)-receptor ligand [(18)F]-altanserin and blood samples were subsequently taken from the radial artery and cubital vein. Eight regions-of-interest (ROI) TACs were extracted from the PET data set. Hierarchical K-means cluster analysis was performed on the PET time series to extract a cerebral vasculature ROI. The number of clusters was varied from K = 1 to 10 for the second of the two-stage method. Determination of the correct number of clusters was performed by the 'within-variance' measure and by 3D visual inspection of the homogeneity of the determined clusters. The cluster-determined input curve was then used in Logan plot analysis and compared with the arterial and venous blood samples, and additionally with one of the currently used alternatives to arterial blood sampling, the Simplified Reference Tissue Model (SRTM) and Logan analysis with cerebellar TAC as an input. There was a good agreement (P < 0.05) between the values of Distribution Volume (DV) obtained from the K-means-clustered input function and those from the arterial blood samples. This work acts as a proof-of-principle that the use of cluster analysis on a PET data set could obviate the requirement for arterial cannulation when determining the input function for kinetic modelling of ligand binding, and that this may be a superior approach as compared to the other noninvasive alternatives.