Cerebellar involvement in cognition, as well as in sensorimotor control, is increasingly recognized and is thought to depend on connections with the cerebral cortex. Anatomical investigations in ...animals and post-mortem humans have established that cerebro-cerebellar connections are contralateral to each other and include the cerebello-thalamo-cortical (CTC) and cortico-ponto-cerebellar (CPC) pathways. CTC and CPC characterization in humans in vivo is still challenging. Here advanced tractography was combined with quantitative indices to compare CPC to CTC pathways in healthy subjects. Differently to previous studies, our findings reveal that cerebellar cognitive areas are reached by the largest proportion of the reconstructed CPC, supporting the hypothesis that a CTC-CPC loop provides a substrate for cerebro-cerebellar communication during cognitive processing. Amongst the cerebral areas identified using in vivo tractography, in addition to the cerebral motor cortex, major portions of CPC streamlines leave the prefrontal and temporal cortices. These findings are useful since provide MRI-based indications of possible subtending connectivity and, if confirmed, they are going to be a milestone for instructing computational models of brain function. These results, together with further multi-modal investigations, are warranted to provide important cues on how the cerebro-cerebellar loops operate and on how pathologies involving cerebro-cerebellar connectivity are generated.
Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally ...incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.
Objective
Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation‐based methods suffer from ...measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration‐based method, with a standard segmentation‐based method.
Methods
Baseline and 1‐year spinal cord 3‐dimensional T1‐weighted images (1mm isotropic) were obtained from 282 patients (52 clinically isolated syndrome CIS, 196 relapsing–remitting MS RRMS, 34 progressive MS PMS), and 82 controls from 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer and multi–field‐strength scans. Spinal Cord Toolbox was used for C2‐5 segmentation and cross‐sectional area (CSA) calculation. After cord straightening and registration, GBSI measured atrophy based on the probabilistic boundary‐shift region of interest. CSA and GBSI percentage annual volume change was calculated.
Results
GBSI provided similar rates of atrophy, but reduced measurement variability compared to CSA in all MS subtypes (CIS: −0.95 ± 2.11% vs −1.19 ± 3.67%; RRMS: −1.74 ± 2.57% vs −1.74 ± 4.02%; PMS: −2.29 ± 2.40% vs −1.29 ± 3.20%) and healthy controls (0.02 ± 2.39% vs −0.56 ± 3.77%). GBSI performed better than CSA in differentiating healthy controls from CIS (area under the curve AUC = 0.66 vs 0.53; p = 0.03), RRMS (AUC = 0.73 vs 0.59; p < 0.001), PMS (AUC = 0.77 vs 0.53; p < 0.001), and patients with disability progression from patients without progression (AUC = 0.59 vs 0.50; p = 0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over 1 year was lower for GBSI than CSA (CIS: 106 vs 830; RRMS: 95 vs 335; PMS: 44 vs 215; power = 80%; alpha = 5%).
Interpretation
The registration‐based method (GBSI) allowed better separation between MS patients and healthy controls and improved statistical power, when compared with a conventional segmentation‐based method (CSA), although it is still far from perfect. ANN NEUROL 2019 ANN NEUROL 2019;86:704–713
Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often ...unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher‐order functions in humans in vivo. Whole‐brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion‐derived metrics in an unsupervised fuzzy c‐means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule‐specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non‐motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule‐specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher‐order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non‐motor functions and that connectivity‐based and microstructure‐based atlases provide complementary information. These results represent a step‐ahead for the interpretation of pathological conditions involving cerebro‐cerebellar circuits.
We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity, in turn with either the cerebral or the cerebellar cortex, as well as their microstructural features, inputting them in an unsupervised fuzzy c‐means classification algorithm. The three approaches provided quite overlapping results in terms of geometrical distribution and, for the first time, indicate that more than half of the DN volumes is involved in non‐motor higher‐order functions.
An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several ...semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.
•First grey matter spinal cord segmentation challenge.•Six institutions participated in the challenge and compared their methods.•Public available dataset from multiple vendors and sites.•The challenge web site remains open to new submissions.
MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. ...However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure.
The clinical course of relapse-onset multiple sclerosis is highly variable. Demographic factors, clinical features and global brain T2 lesion load have limited value in counselling individual ...patients. We investigated early MRI predictors of key long-term outcomes including secondary progressive multiple sclerosis, physical disability and cognitive performance, 15 years after a clinically isolated syndrome. A cohort of patients with clinically isolated syndrome (n = 178) was prospectively recruited within 3 months of clinical disease onset and studied with MRI scans of the brain and spinal cord at study entry (baseline) and after 1 and 3 years. MRI measures at each time point included: supratentorial, infratentorial, spinal cord and gadolinium-enhancing lesion number, brain and spinal cord volumetric measures. The patients were followed-up clinically after ∼15 years to determine disease course, and disability was assessed using the Expanded Disability Status Scale, Paced Auditory Serial Addition Test and Symbol Digit Modalities Test. Multivariable logistic regression and multivariable linear regression models identified independent MRI predictors of secondary progressive multiple sclerosis and Expanded Disability Status Scale, Paced Auditory Serial Addition Test and Symbol Digit Modalities Test, respectively. After 15 years, 166 (93%) patients were assessed clinically: 119 (72%) had multiple sclerosis 94 (57%) relapsing-remitting, 25 (15%) secondary progressive, 45 (27%) remained clinically isolated syndrome and two (1%) developed other disorders. Physical disability was overall low in the multiple sclerosis patients (median Expanded Disability Status Scale 2, range 0-10); 71% were untreated. Baseline gadolinium-enhancing (odds ratio 3.16, P < 0.01) and spinal cord lesions (odds ratio 4.71, P < 0.01) were independently associated with secondary progressive multiple sclerosis at 15 years. When considering 1- and 3-year MRI variables, baseline gadolinium-enhancing lesions remained significant and new spinal cord lesions over time were associated with secondary progressive multiple sclerosis. Baseline gadolinium-enhancing (β = 1.32, P < 0.01) and spinal cord lesions (β = 1.53, P < 0.01) showed a consistent association with Expanded Disability Status Scale at 15 years. Baseline gadolinium-enhancing lesions was also associated with performance on the Paced Auditory Serial Addition Test (β = - 0.79, P < 0.01) and Symbol Digit Modalities Test (β = -0.70, P = 0.02) at 15 years. Our findings suggest that early focal inflammatory disease activity and spinal cord lesions are predictors of very long-term disease outcomes in relapse-onset multiple sclerosis. Established MRI measures, available in routine clinical practice, may be useful in counselling patients with early multiple sclerosis about long-term prognosis, and personalizing treatment plans.
Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use ...computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time τi of water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus τi. We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including τi. We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (R2={0.88,0.95,0.82,0.99}) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (R2={0.75,0.60,0.11,0.99}). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC-S) the estimate of the residence time is 0.57±0.05s for the healthy subjects, while in the MS patient with a lesion in CC-S it is 0.33±0.12s in the normal appearing white matter (NAWM) and 0.19±0.11s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52±0.09s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56±0.05s in the NAWM and 0.13±0.09s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique.
•Some tissue parameters remain elusive because mathematical models are intractable.•We propose to use machine learning to estimate these parameters, here permeability.•Simulation results show an excellent agreement between estimations and ground truth.•New technique performs better than the standard Karger Model.•In-vivo results consistent with pathology of MS lesions showing clinical potential.
Neurodegeneration is the pathological substrate that causes major disability in secondary progressive multiple sclerosis. A synthesis of preclinical and clinical research identified three ...neuroprotective drugs acting on different axonal pathobiologies. We aimed to test the efficacy of these drugs in an efficient manner with respect to time, cost, and patient resource.
We did a phase 2b, multiarm, parallel group, double-blind, randomised placebo-controlled trial at 13 clinical neuroscience centres in the UK. We recruited patients (aged 25–65 years) with secondary progressive multiple sclerosis who were not on disease-modifying treatment and who had an Expanded Disability Status Scale (EDSS) score of 4·0–6·5. Participants were randomly assigned (1:1:1:1) at baseline, by a research nurse using a centralised web-based service, to receive twice-daily oral treatment of either amiloride 5 mg, fluoxetine 20 mg, riluzole 50 mg, or placebo for 96 weeks. The randomisation procedure included minimisation based on sex, age, EDSS score at randomisation, and trial site. Capsules were identical in appearance to achieve masking. Patients, investigators, and MRI readers were unaware of treatment allocation. The primary outcome measure was volumetric MRI percentage brain volume change (PBVC) from baseline to 96 weeks, analysed using multiple regression, adjusting for baseline normalised brain volume and minimisation criteria. The primary analysis was a complete-case analysis based on the intention-to-treat population (all patients with data at week 96). This trial is registered with ClinicalTrials.gov, NCT01910259.
Between Jan 29, 2015, and June 22, 2016, 445 patients were randomly allocated amiloride (n=111), fluoxetine (n=111), riluzole (n=111), or placebo (n=112). The primary analysis included 393 patients who were allocated amiloride (n=99), fluoxetine (n=96), riluzole (n=99), and placebo (n=99). No difference was noted between any active treatment and placebo in PBVC (amiloride vs placebo, 0·0% 95% CI −0·4 to 0·5; p=0·99; fluoxetine vs placebo −0·1% –0·5 to 0·3; p=0·86; riluzole vs placebo −0·1% –0·6 to 0·3; p=0·77). No emergent safety issues were reported. The incidence of serious adverse events was low and similar across study groups (ten 9% patients in the amiloride group, seven 6% in the fluoxetine group, 12 11% in the riluzole group, and 13 12% in the placebo group). The most common serious adverse events were infections and infestations. Three patients died during the study, from causes judged unrelated to active treatment; one patient assigned amiloride died from metastatic lung cancer, one patient assigned riluzole died from ischaemic heart disease and coronary artery thrombosis, and one patient assigned fluoxetine had a sudden death (primary cause) with multiple sclerosis and obesity listed as secondary causes.
The absence of evidence for neuroprotection in this adequately powered trial indicates that exclusively targeting these aspects of axonal pathobiology in patients with secondary progressive multiple sclerosis is insufficient to mitigate neuroaxonal loss. These findings argue for investigation of different mechanistic targets and future consideration of combination treatment trials. This trial provides a template for future simultaneous testing of multiple disease-modifying medicines in neurological medicine.
Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership, UK Multiple Sclerosis Society, and US National Multiple Sclerosis Society.
Purpose
A method is proposed to quantify cerebral blood volume (vb$$ {v}_b $$) and intravascular water residence time (τb$$ {\tau}_b $$) using MR fingerprinting (MRF), applied using a spoiled ...gradient echo sequence without the need for contrast agent.
Methods
An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement to vb$$ {v}_b $$ and τb$$ {\tau}_b $$ changes. Its accuracy in the presence of variations in T1,t$$ {\mathrm{T}}_{1,t} $$, T1,b$$ {\mathrm{T}}_{1,b} $$, and B1$$ {\mathrm{B}}_1 $$ was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 ±$$ \pm $$ 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability.
Results
Simulations show that assuming literature values for T1,b$$ {\mathrm{T}}_{1,b} $$ and T1,t$$ {\mathrm{T}}_{1,t} $$, no variation in B1$$ {\mathrm{B}}_1 $$, while fitting only vb$$ {v}_b $$ and τb$$ {\tau}_b $$, leads to large errors in quantification of vb$$ {v}_b $$ and τb$$ {\tau}_b $$, regardless of noise levels. However, simulations also show that matching T1,t$$ {\mathrm{T}}_{1,t} $$, T1,b$$ {\mathrm{T}}_{1,b} $$, B1+$$ {\mathrm{B}}_1^{+} $$, vb$$ {v}_b $$ and τb$$ {\tau}_b $$, simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matter T1,t$$ {\mathrm{T}}_{1,t} $$ (p < 0.0001) and vb$$ {v}_b $$ (p < 0.0001) are observed, T1,b$$ {\mathrm{T}}_{1,b} $$ and τb$$ {\tau}_b $$ show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra‐class correlation coefficient of T1,t:0.91$$ {\mathrm{T}}_{1,t}:0.91 $$, T1,b:0.58$$ {\mathrm{T}}_{1,b}:0.58 $$, vb:0.90$$ {v}_b:0.90 $$, and τb:0.96$$ {\tau}_b:0.96 $$.
Conclusion
We demonstrate that regional simultaneous quantification of vb$$ {v}_b $$, τb$$ {\tau}_b $$, T1,b,T1,t$$ {\mathrm{T}}_{1,b},{T}_{1,t} $$, and B1+$$ {\mathrm{B}}_1^{+} $$ using MRF is feasible in vivo.
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