Biology subjects

Jones, D. K.

Publications and source records attributed to Jones, D. K..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

White matter microstructure in face and body networks predicts facial expression and body posture perception across development

Facial expression and body posture recognition have protracted developmental trajectories. Interactions between face and body perception, such as the influence of body posture on facial expression perception, also change with development. While the brain regions underpinning face and body processing are well-defined, little is known about how white-matter tracts linking these regions relate to perceptual development. Here, we obtained complementary diffusion magnetic resonance imaging (MRI) measures (fractional anisotropy FA, spherical mean [Formula]), and a quantitative MRI myelin-proxy measure (R1), within white-matter tracts of face- and body-selective networks in children and adolescents and related these to perceptual development. In tracts linking occipital and fusiform face areas, facial expression perception was predicted by age-related microstructural development, as measured by [Formula] and R1, as well as age-independent individual differences in microstructure, as measured by FA. Tract microstructure linking the body region in posterior superior temporal sulcus with anterior temporal lobe (ATL) was related to the influence of body on facial expression perception, supporting ATL as a site of face and body network convergence. Overall, our results highlight age-dependent and age-independent constraints that white-matter microstructure poses on perceptual abilities during development and the importance of complementary microstructural measures in linking brain structure and behaviour.

neuroscience↗

bioRxiv2022

Increased sensorimotor structural connectivity in high schizotypy

The link between brain structural connectivity and schizotypy was explored in two healthy-participant cohorts, collected at two different neuroimaging centres, comprising 140 and 115 participants respectively. The participants completed the Schizotypal Personality Questionnaire (SPQ), through which their schizotypy scores were calculated. Diffusion-MRI data were used to perform tractography and to generate the structural brain networks of the participants. The edges of the networks were weighted with the inverse radial diffusivity. Graph theoretical metrics of the default-mode, sensorimotor, visual and auditory subnetworks were derived and their correlation coefficients with the schizotypy scores were calculated. To the best of our knowledge, this is the first time that graph theoretical measures of structural brain networks are investigated in relation to schizotypy. A positive correlation was found between the schizotypy score and the mean node degree and mean clustering coefficient of the sensorimotor and the default-mode subnetworks. The nodes driving these correlations were the right postcentral gyrus, the left paracentral lobule, the right superior frontal gyrus, the left parahippocampal gyrus and the bilateral precuneus, i.e., nodes that exhibit compromised functional connectivity in schizophrenia. Implications for schizophrenia and schizotypy are discussed.

neuroscience↗

bioRxiv2022

Hypervoxels: A Multidimensional Framework For The Representation And Analysis Of Neuroimaging Data

Most neuroimaging modalities use regular grids of voxels to represent the three-dimensional space occupied by the brain. However, a regular 3D voxel grid does not reflect the anatomical and topological complexity represented by the brains white matter connections. In contrast, tractography reconstructions based on diffusion MRI provide a closer characterisation of the white matter pathways followed by the neuronal fibres interconnecting different brain regions. In this work, we introduce hypervoxels as a new methodological framework that combines the spatial encoding capabilities of multidimensional voxels with the anatomical and topological information found in tractography data. We provide a detailed description of the framework and evaluate the benefits of using hypervoxels by carrying out comparative voxel and hypervoxel cluster inference analyses on diffusion MRI data from a neuroimaging study on amyotrophic lateral sclerosis (ALS). Compared to the voxel analyses, the use of hypervoxels improved the detection of effects of interest in the data in terms of statistical significance levels and spatial distribution across white matter regions known to be affected in ALS. In these regions, the hypervoxel results also identified specific white matter pathways that resolve the anatomical ambiguity otherwise observed in the results from the voxel analyses. The observed increase in sensitivity and specificity can be explained by the superior ability of hypervoxel-based methods to represent and disentangle the anatomical overlap of white matter connections. Based on this premise, we expect that the use of hypervoxels should improve the analysis of neuroimaging data when the effects of interest under investigation are expected to be aligned along distinct but potentially overlapping white matter pathways.

neuroscience↗

bioRxiv2021

Mutation-related apparent myelin, not axon density, drives white matter differences in premanifest Huntington's disease: Evidence from in vivo ultra-strong gradient MRI

White matter (WM) alterations have been observed early in Huntingtons disease (HD) progression but their role in the disease-pathophysiology remains unknown. We exploited ultra-strong-gradient MRI to tease apart contributions of myelin (with the magnetization transfer ratio), and axon density (with the restricted volume fraction from the Composite Hindered and Restricted Model of Diffusion) to WM differences between premanifest HD patients and age- and sex-matched controls. Diffusion tensor MRI (DT-MRI) measures were also assessed. We used tractometry to investigate region-specific changes across callosal segments with well-characterized early- and late-myelinating axonal populations, while brain-wise alterations were explored with tract-based cluster analysis (TBCA). Behavioural measures were included to explore disease-associated brain-function relationships. We detected lower myelin in the rostrum of patients (tractometry: p = 0.0343; TBCA: p = 0.030), but higher myelin in their splenium (p = 0.016). Importantly, patients myelin and mutation size were positively associated (all p-values < 0.01), indicating that increased myelination might be a direct result of the mutation. Finally, myelin was higher than controls in younger patients but lower in older patients (p = 0.003), suggesting detrimental effects of increased myelination later in the course of the disease. Higher FR in patients left cortico-spinal tract (CST) (p = 0.03) was detected, and was found to be positively associated with MTR in the posterior callosum (p = 0.033), possibly suggesting compensation to myelin alterations. This comprehensive, ultra-strong gradient MRI investigation provides novel evidence of CAG-driven myelin alterations in premanifest HD which may reflect neurodevelopmental, rather than neurodegenerative disease-associated changes.

neuroscience↗

bioRxiv2021

The impact of genetic risk for Alzheimers disease on the structural brain networks of young adults

We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimers disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Diffusion MRI data were used to perform whole-brain tractography and to generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participants genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and a) the graph theoretical metrics of the structural networks and b) the rich-club, feeder and local connectivity of the whole-brain networks. In the visual subnetwork, the mean nodal strength was negatively correlated with the genomewide PRS (r=-0.19, p=1.3x10-5), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r=0.16, p=9.2x10-4), and the mean clustering coefficient was negatively correlated with the tau protein binding PRS (r=-0.16, p=9.2x10-4). In the default mode network, the mean nodal strength was negatively correlated with the genomewide PRS (r=-0.14, p=1.5x10-3). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r=-0.16, p=3.7x10-4; r=-0.15, p=8.8x10-4). Our results indicate small reductions in brain connectivity in young adults at risk of developing Alzheimers disease in later life.

neuroscience↗

bioRxiv2021

Short Association Fibre Tractography

It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short ([≤]30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surfacebased measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.

neuroscience↗

bioRxiv2021

Validating Pore Size Estimates in a Complex Microfibre Environment on a Human MRI System

PurposeRecent advances in diffusion-weighted MRI provide restricted diffusion signal fraction and restricting pore size estimates. Materials based on co-electrospun oriented hollow cylinders have been introduced to provide validation for such methods. This study extends this work, exploring accuracy and repeatability using an extended acquisition on a 300 mT/m gradient human MRI scanner, in substrates closely mimicking tissue, i.e., non-circular cross-sections, intra-voxel fibre crossing, intra-voxel distributions of pore-sizes and smaller pore-sizes overall. MethodsIn a single-blind experiment, diffusion-weighted data were collected from a biomimetic phantom on a 3T Connectom system using multiple gradient directions/diffusion times. Repeated scans established short-term and long-term repeatability. The total scan time (54 minutes) matched similar protocols used in human studies. The number of distinct fibre populations was estimated using spherical deconvolution, and median pore size estimated through the combination of CHARMED and AxCaliber3D framework. Diffusion-based estimates were compared with measurements derived from scanning electron microscopy. ResultsThe phantom contained substrates with different orientations, fibre configurations and pore size distributions. Irrespective of one or two populations within the voxel, the pore-size estimates (~5m) and orientation-estimates showed excellent agreement with the median values of pore-size derived from scanning electron microscope and phantom configuration. Measurement repeatability depended on substrate complexity, with lower values seen in samples containing crossing-fibres. Sample-level repeatability was found to be good. ConclusionWhile no phantom mimics tissue completely, this study takes a step closer to validating diffusion microstructure measurements for use in vivo by demonstrating the ability to quantify microgeometry in relatively complex configurations.

biophysics↗

bioRxiv2021

Multi-compartment analysis of the complex gradient-echo signal quantifies myelin breakdown in premanifest Huntington's disease

White matter (WM) alterations have been identified as a relevant pathological feature of Huntingtons disease (HD). Increasing evidence suggests that WM changes in this disorder are due to alterations in myelin-associated biological processes. Multi-compartmental analysis of the complex gradient-echo MRI signal evolution in WM has been shown to quantify myelin in vivo, therefore pointing to the potential of this technique for the study of WM myelin changes in health and disease. This study first characterized the reproducibility of metrics derived from the complex multi-echo gradient-recalled echo (mGRE) signal across the corpus callosum in healthy participants, finding highest reproducibility in the posterior callosal segment. Subsequently, the same analysis pipeline was applied in this callosal region in a sample of premanifest HD patients (n = 19) and age, sex and education matched healthy controls (n = 21). In particular, we focused on two myelin-associated derivatives: i. the myelin water signal fraction (fm), a parameter dependent on myelin content; and ii. the difference in frequency between myelin and intra-axonal water pools ({Delta}{omega}), a parameter dependent on the ratio between the inner and the outer axonal radii. fm was found to be lower in HD patients ({beta} = -0.13, p = 0.03), while {Delta}{omega} did not show a group effect. Performance in tests of working memory, executive function, social cognition and movement was also assessed, and a greater age-related decline in executive function was detected in HD patients ({beta} = -0.06, p = 0.006), replicating previous evidence of executive dysfunction in HD. Finally, the correlation between fm, executive function, and proximity to disease onset was explored in patients, and a positive correlation between executive function and fm was detected (r = 0.542; p = 0.02). This study emphasises the potential of complex mGRE signal analysis for aiding understanding of HD pathogenesis and progression. Moreover, expanding on evidence from pathology and animal studies, it provides novel in vivo evidence supporting myelin breakdown as an early feature of HD.

neuroscience↗

bioRxiv2021

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

neuroscience↗

bioRxiv2020

Distributional changes in myelin-specific MRI markers uncover dynamics in the fornix following spatial navigation training

Increasing evidence implicates white matter (WM) dynamics supporting learning in the mature brain. Recent MRI studies, mostly using diffusion tensor MRI (DT-MRI), have demonstrated learning-induced WM changes at the microstructural level. However, while DT-MRI-derived measures have sensitivity to general WM microstructural changes, they lack compartmental specificity, making them difficult to relate to underlying cellular mechanisms, stymying deeper understanding of mechanisms supporting training-induced gains in performance. Gaining a deeper understanding demands a more detailed characterization of changes in specific WM sub-components. To this end, four microstructural MRI techniques were employed to study alterations in rat brains after 5-days of water maze training: DT-MRI; Composite Hindered and Restricted Model of Diffusion (CHARMED); magnetization transfer (MT) imaging; quantitative susceptibility mapping and R2*. The hypothesis tested here was that microstructural changes would be: (i) observed in tracts supporting spatial navigation, i.e., fornix and corpus callosum (CC); and (ii) more pronounced in the myelin-specific measures. Medians and distributions of microstructural parameters were derived along the fornix, CC and cingulum (as a comparison tract) using the tractometry approach. Summary measures were derived from different metrics using unsupervised data reduction. Significant pre-vs-post training differences were found in the medians of two principal components loading on: (i) anisotropy indices; and (ii) MT ratio. The most striking effect, however, was seen in the distributions of pre-vs-post training MT ratio in the fornix, consistent with the primary hypothesis, and highlighting the value of this alternative to the standard approach (i.e., comparing means/medians of DT-MRI parameters) for studying neuroplasticity in vivo. Significance statementRecent MRI-studies have demonstrated that white matter (WM) dynamics support learning, even in the mature brain. However, most studies are based on diffusion tensor MRI (DT-MRI) measures, which although providing sensitivity to WM microstructural changes, do not provide a direct translation to the underlying cellular mechanisms making interpretation difficult. Using alternative quantitative MRI approaches, this study provided more specific subcomponent microstructural insights into the brains WM response to water-maze training. We show that myelin-specific MR measures show more marked changes than axonal-specific and DT-MRI measures, in WM tracts responsible for spatial navigation. The results promote adoption of these alternative approaches to DT-MRI for studying neuroplasticity in vivo.

neuroscience↗

bioRxiv2020

Computing the Orientational-Average of Diffusion-Weighted MRI Signals: A Comparison of Different Techniques

Numerous applications in diffusion MRI, from multi-compartment modeling to power-law analyses, involves computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or shell), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres (either by design, or due to gradient non-linearities). To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its isotropic part. This latter approach can be applied to all q-space sampling schemes, making it suitable for multi-shell acquisitions when unwanted gradient non-linearities are present. Here, these different methods are compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61points per shell), and isotropically-distributed sampling vectors, all methods give comparable results, (accuracy of MAP-MRI-based estimates being slightly higher albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give pronounced improvements in accuracy over the other methods.

neuroscience↗

bioRxiv2020

SPHERIOUSLY? The challenges of estimating spherical pore size non-invasively in the human brain from diffusion MRI

The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. The approach could also enable estimation of microstructure parameters such as the apparent size (radius) of the soma. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called b-tensor encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of soma size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of soma properties in a non-uniform fashion. It may cause overestimation or underestimation of the soma size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and pore-size that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7 m, while the second, pure Monte Carlo simulations, yielded a lower limit of 3 m and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical pore-size estimates particularly challenging. We conclude that due to the aforementioned challenges spherical pore size estimates may be biased when the corresponding signal fraction is low, which must be considered when using them as biomarkers in clinical/research studies.

neuroscience↗

bioRxiv2020

Estimating intra-axonal axial diffusivity in the presence of fibre orientation dispersion

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d[boxV] = 1.7m2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ~ 2 - 2.5m2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

neuroscience↗

bioRxiv2020

Repeatability of soma and neurite metrics incortical and subcortical grey matter

Diffusion magnetic resonance imaging is a technique which has long been used to study white matter microstructure in vivo. Recent advancements in hardware and modelling techniques have opened up interest in disentangling tissue compartments in the grey matter. In this study, we evaluate the repeatability of soma and neurite density imaging in a sample of six healthy adults scanned five times on an ultra-strong gradient magnetic resonance scanner (300 mT/m). Repeatability was expressed as an intraclass correlation coefficient (ICC). Our findings reveal that measures of soma density (mean ICC=.976), neurite density (mean ICC=.959) and apparent soma size (mean ICC=.923) are highly reliable across multiple cortical and subcortical networks. Overall, we demonstrate the promise of moving advanced grey matter microstructural imaging towards applications of development, ageing, and disease.

neuroscience↗

bioRxiv2020

Predicting MEG resting-state functional connectivity using microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries at the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology for better understanding of functional mechanisms. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

neuroscience↗

bioRxiv2020

Visualizing orientation-specific relaxation-diffusion features mapped onto orientation distribution functions estimated via nonparametric Monte Carlo MRI signal inversion

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

neuroscience↗

bioRxiv2020

The Impact of Graph Construction Scheme and Community Detection Algorithm on the Reliability of Community and Hub Identification in Structural Brain Networks

A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organization of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test-retest data from the Human Connectome Project), the repeatability of 33 community detection algorithms, each paired with 7 different graph construction schemes was assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can be belong to more than one community). The highest repeatability was observed for the fast multi-scale community detection algorithm paired with a graph construction scheme that combines 9 white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on optimal pairing of community detection algorithm and graph construction scheme.

neuroscience↗

bioRxiv2020

Imaging Microglia and Astrocytes non-invasively using Diffusion MRI

Glia, and particularly microglia, are increasingly implicated in the pathophysiology of psychiatric and neurodegenerative disorders. However, to date the only methods for imaging these cells in vivo involve either invasive procedures (e.g. multi-photon imaging in rodents) or TSPO-PET radiotracers, which afford low resolution and specificity, since TSPO expresses across multiple cell types. Here, we present a non-invasive diffusion-weighted MRI method to image changes in glia morphometry in vivo. Using two rat models of neuroinflammation, with and without neurodegeneration, we demonstrate that diffusion-weighted MRI carries the fingerprint of microglia and astrocytes activation, and that specific signatures from each population can be quantified non-invasively. We demonstrate that the method can further detect glia proliferation, and provide a quantitative account of neuroinflammation regardless of the existence of a concomitant neuronal loss. We prove the translational value of the approach showing significant correlations between MRI and histological microglia markers measured across different brain regions in humans. This framework holds the potential to transform basic and clinical research by providing a tool to clarify the role of inflammation in health and disease across the lifespan.

neuroscience↗