Biology subjects

Palesi, F.

Publications and source records attributed to Palesi, F..

17 recordsLinked to original sources

bioRxiv2026

Virtual Brain Modelling Reveals Altered Network Dynamics and Predicts Symptom Severity in Schizophrenia: Relevance to Antipsychotic Treatment Response and Resistance.

Background: Schizophrenia (SZ) is a heterogeneous disorder characterized by positive, negative, cognitive and disorganisation symptoms, with pleomorphic phenotype and poorly understood aetiology. Dopamine dysregulation represents the most replicated in vivo pathophysiology finding. However, accumulating evidence suggests altered large-scale brain connectivity and excitation/inhibition balance disruption. While quantifying these alterations would help clarify SZ neurophysiology and guide therapy, single-patient identification remains impractical. This is particularly relevant for treatment-resistant SZ (TRS) whose pathophysiology remains unclear. Methods: Using virtual brain modelling, we simulated large-scale brain dynamics from functional and structural MRI data in 43 healthy controls and 33 SZ patients, stratified into TRS and non-TRS. We used the Wong-Wang model to derive four network parameters reflecting GABAergic and NMDA receptor-mediated glutamatergic transmission: global coupling (G), local inhibitory (Ji) and excitatory (JNMDA) coupling, and recurrent excitation (w+). We performed simulations in the whole brain and in resting-state networks, compared model parameters between groups and tested their association with Positive and Negative Syndrome Scale (PANSS) scores. Results: Compared to non-TRS, TRS patients showed increased JNMDA and decreased w+ at the whole-brain level, increased JNMDA and G in the dorsal attention network, lower G in the limbic network and increased w+ in the visual network. Moreover, Ji in the default mode network predicted negative symptom severity while JNMDA in the limbic and ventral attention networks predicted total PANSS scores and disorganisation. Conclusions: These findings suggest that TRS is characterized by network-specific alterations in excitation/inhibition profiles and support the use of personalised multiscale modelling to identify symptom biomarkers.

neuroscience↗

bioRxiv2026

Brain digital twins reveal network changes in congenital and slowly progressive cerebellar ataxias

Cerebellar ataxias are a rare group of disorders manifesting with motor incoordination and cognitive-affective deficits of variable severity. Although neurogenetic has revealed multiple mutations, the study of ataxias still relies on clinical evaluation, while the underlying neural network changes remain unclear. It has been argued that the less severe symptoms in congenital (like Joubert syndrome, JS) than in slowly progressive (SP) ataxias reflect a different interplay of alteration and compensation but direct evidence is still lacking. Moreover, it is unclear why, in front of a wide heterogeneity of molecular alterations, SPs show common clinical symptoms. To address these questions, we created brain digital twins for each participant by combining volumetry, graph theory analysis of structural and functional connectivity, and dynamical simulations using the virtual brain. We studied 8 JS (3 females, 21{+/-}6years), 8 SP (3 females, 20{+/-}5years) and 11 healthy controls (HC; 5 females, 21{+/-}2years).Volumetry quantified atrophy, graph metrics (centrality, segregation and integration) characterized topology, and neural dynamical simulations estimated excitation/inhibition balance, providing anatomo-physiological parameters within the somatomotor (SMN) and ventral attention (VAN) networks. Anatomo-physiological parameters were correlated with clinical/neuropsychological scores, and unsupervised clustering was applied to assess whether network features can discriminate between JS and SP beyond clinical classification. MRI morphometry confirmed selective vermis reduction in JS and a widespread cerebellar atrophy in SP compared to HC. In both ataxia groups, SMN and VAN showed reduced volume and structural connectivity but with different patterns of topological and dynamical alterations. In the SMN of SP, reduced centrality and excitation/inhibition balance depressed information transfer through the network. In the VAN of JS, reduced centrality, segregation, and integration, were detrimental but coexisted with a higher number of functional core nodes and an increased large-scale excitatory coupling, supporting compensatory reorganisation in extracerebellar nodes. Clustering confirmed that SMN better differentiates SP, whereas VAN better clusters JS. Importantly, anatomo-physiological parameters of network volume, topology, and dynamics correlated with patients motor and cognitive performance. In conclusion, primary cerebellar damage secondarily impacts large-scale brain networks, altered in both ataxia groups but compensated only in JS. Similar clinical symptoms in SP reflects the similarity of network changes, while differential involvement of SMN and VAN in JS and SP reflects the connectivity pattern of the lesioned areas inside these large-scale brain circuits. Importantly, anatomo-physiological parameters are sufficient to explain individual motor and cognitive performance, offering a basis for improved patient profiling and personalized therapies.

neuroscience↗

bioRxiv2026

Automated derivation of mean field models from spiking neural networks for the simulation of brain dynamics

A mean field model (MFM) is a mesoscopic description of neuronal population dynamics that can reduce the complexity of neural microcircuits into equations preserving key functional properties. The generation of a MFM is a complex mathematical process that starts with the incorporation of single neuron input/output relationships and local connectivity. Once neuron electroresponsiveness and synaptic properties are defined, in principle, the process can be automatized. Here we develop a tool for automatic MFM derivation from biophysically grounded spiking networks (Auto-MFM) by performing micro-to-mesoscale parameter remapping, estimating input/output relationships specific for different neuronal populations (i.e., transfer functions), and optimizing transfer function parameters. Auto-MFM was tested using a spiking cerebellar circuit as a generative model. The cerebellar MFM derived with Auto-MFM accurately reproduced cerebellar population dynamics of the corresponding spiking network, matching mean and time-varying firing rates across a wide range of stimulation patterns. Auto-MFM allowed us to model and explore physiological and pathological circuit variants; indeed, it was used to map ataxia-related structural connectivity alterations of the cerebellar network, in which Purkinje cells with simplified dendritic structure altered the cerebellar connectivity. Furthermore, Auto-MFM was used to create a library of cerebellar MFMs by sweeping the level of the excitatory conductance at mossy fiber - granule cell synapse, which is altered in several neuropathologies. Auto-MFM is thus proving a flexible and powerful tool to generate region-specific MFMs of healthy and pathological brain networks to be embedded in brain digital models.

neuroscience↗

bioRxiv2025

Causal inference and functional dynamics of a visuomotor network demonstrate excitatory/inhibitory alterations in Multiple Sclerosis

Balanced excitation and inhibition are essential for brain dynamics, and their disruption can lead to network dysfunction in neurological diseases. Here, we present a conceptually unified multiscale brain modelling framework combining Dynamic Causal Modelling (DCM) applied to task and resting-state functional Magnetic Resonance Imaging (fMRI) data and The Virtual Brain (TVB) to characterise the excitatory/inhibitory balance of the brain. We applied the framework to a visuomotor brain subnetwork in a cohort of 9 healthy controls and 17 people with multiple sclerosis (pwMS). Acquired data included an event-related task fMRI experiment with variable grip force, resting-state fMRI, and diffusion-weighted imaging. The visuomotor network comprised the bilateral primary visual cortex (V1), left primary motor cortex (M1), supplementary motor and premotor cortex (SMAPMC), cingulate cortex (CC), superior parietal lobule (SPL), and right cerebellar lobule VI (CR). Results from DCM showed that while the overall network architecture was preserved in MS, there were significant alterations in the excitatory/inhibitory nature of effective connectivity: at rest, a statistical change was observed in CR-to-V1 connectivity, which was inhibitory in healthy volunteers but excitatory in MS. During task, effective connectivity feedback, including cerebellar self-connection, was positive in healthy volunteers but negative in MS and became increasingly dysregulated with higher motor demand. Alterations in functional and effective connectivity were associated with behavioural performance (task reaction time) and clinical measures (disability severity). At the overall group level, TVB parameters linked reduced NMDA-mediated excitatory gain to slower task responses. Moreover, integrating DCM and TVB demonstrated that higher global excitatory gain was associated with stronger task-engaged effective connectivity across sensorimotor and visuomotor pathways, linking network-level excitability captured by TVB to context-dependent reconfiguration of directed interactions and to connection-level strength revealed by DCM.

neuroscience↗

bioRxiv2025

Region-specific mean field models enhance simulations of local and global brain dynamics

Brain dynamics can be simulated using virtual brain models, in which a standard mathematical representation of oscillatory activity is usually adopted for all cortical and subcortical regions. However, some brain regions have specific microcircuit properties that are not recapitulated by standard oscillators. Moreover, magnetic resonance imaging (MRI)-based connectomes may not be able to capture local circuit connectivity. Region-specific models incorporating computational properties of local neurons and microcircuits have recently been generated using the mean field (MF) approach and proposed to impact large-scale brain dynamics. Here we have used a MF of the cerebellar cortex to generate a mesoscopic model of the whole cerebellum featuring a prewired connectivity of multiple cerebellar cortical areas with deep cerebellar nuclei. This multi-node cerebellar mean field model was then used to substitute the corresponding standard oscillators and build up a cerebellar mean field virtual brain (cMF-TVB), for a group of healthy human subjects. Simulations revealed that electrophysiological and fMRI signals generated by the cMF-TVB significantly improved the fitness of local and global dynamics with respect to a homogeneous model made solely of standard oscillators. The cMF-TVB reproduced the rhythmic oscillations and coherence typical of the cerebellar circuit and allowed to correlate electrophysiological and functional MRI signals to specific neuronal populations. In aggregate, region-specific models based on MF technology and pre-wired circuit connectivity can significantly improve virtual brain simulations fostering the generation of effective brain digital twins that could be used for physiological studies and clinical applications.

bioengineering↗

bioRxiv2024

Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an action

The motor learning theory anticipates that cerebro-cerebellar loops perform sensorimotor prediction thereby regulating motor control. This operation has been identified during action execution (AE) and observation (AO) but the causal interaction between the cerebellum and cerebral cortex remained unclear. Here we used Dynamic Causal Modelling (DCM) to study functional MRI (fMRI) data obtained during a squeeze ball task in either the AE or AO conditions. In both cases, active regions included bilateral primary visual cortex (V1), left primary motor cortex (M1), left supplementary motor and premotor cortex (SMAPMC), left cingulate cortex (CC), left superior parietal lobule (SPL), and right cerebellum (CRBL). AE and AO networks showed the same fixed effective connectivity, with pathways between V1, CRBL, SMAPMC and CC wired in a closed loop. However, the cerebellar communication towards the cerebral cortex switched from excitatory in AE to inhibitory in AO. Moreover, in AE only, signal modulation was non-linear from SMAPMC to CRBL and within the CRBL self-connection, supporting the role of the CRBL in elaborating motor plans received from SMAPMC. Thus, the need for motor planning and the presence of a sensorimotor feedback in AE discriminate the modality of forward control operated by the CRBL on SMAPMC. While the underlying circuit mechanisms remain to be determined, these results reveal that the CRBL differentially controls the excitatory/inhibitory dynamics of inter-regional effective connectivity depending on its functional engagement, opening new prospective for the design of artificial sensorimotor controllers and for the investigation of neurological diseases.

neuroscience↗

bioRxiv2024

Multiscale modelling of neuronal dynamics in hippocampus CA1

The development of biologically realistic models of brain microcircuits and regions is currently a very relevant topic in computational neuroscience. From basic research to clinical applications, there is an increasing demand for accurate models that incorporate local cellular and network specificities, able to capture a broad range of dynamics and functions associated with given brain regions. One of the main challenges of these models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. One novel approach to this problem is the use of mean-field models of neuronal activity to build large-scale simulations. This provides an effective solution to the passage between scales with relatively low computational demands, which is achieved by a drastic reduction in the dimensionality of the system. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. To develop the mean-field model we make use of a recently introduced formalism based on a bottom-up approach that is easily applicable to different neuronal models and cell types. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. In addition, we show an example of the implementation of our model to study a stimulus propagation at the macro-scale, and we compare the results obtained from our model with the corresponding spiking network model of the whole CA1 area.

neuroscience↗

bioRxiv2023

Structural and connectivity parameters reveal compensation patterns in young patients with non-progressive and slow-progressive cerebellar ataxia

IntroductionWithin Pediatric Cerebellar Ataxias (PCAs), patients with non-progressive ataxia (NonP) surprisingly show postural motor behavior comparable to that of healthy controls, differently to slow-progressive ataxia patients (SlowP). This difference may depend on the building of the compensatory strategies of the intact areas in NonP brain network. MethodsEleven PCAs patients were recruited: five with NonP and six with SlowP. We assessed volumetric and axonal bundles alterations with a multimodal approach to investigate the connections between basal ganglia and cerebellum as putative compensatory tracts. ResultsCerebellar lobules were smaller in SlowP patients. NonP patients showed a lower number of streamlines in the cerebello-thalamo-cortical tracts but a generalized higher integrity of white matter tracts connecting the cortex and the basal ganglia with the cerebellum. DiscussionThis work reveals that the axonal bundles connecting the cerebellum with basal ganglia and cortex demonstrate a higher integrity in NonP patients. This evidence highlights the importance of the cerebellum-basal ganglia connectivity to explain the different postural motor behavior of NonP and SlowP patients and support the compensatory role of basal ganglia in patients with stable cerebellar malformation.

neuroscience↗

bioRxiv2023

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

IntroductionNeural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. Virtual brain modelling (TVB), by exploiting structural and functional MRI, yields mesoscopic parameters of connectivity and synaptic transmission. MethodsWe used TVB to simulate brain networks, which are key for human brain function, in Alzheimers disease (AD) and Frontotemporal Dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. ResultsThe pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. DiscussionThese TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

neuroscience↗

bioRxiv2022

A multi-layer mean-field model for the cerebellar cortex: design, validation, and prediction

Mean-field (MF) models can be used to summarize in a few statistical parameters the salient properties of an inter-wired neuronal network incorporating different types of neurons and synapses along with their topological organization. MF are crucial to efficiently implement the modules of large-scale brain models maintaining the specificity of local 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 network (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 satisfactorily reproduced the average dynamics of the different neuronal populations in response to various input patterns and predicted the modulation of 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 that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.

bioengineering↗

bioRxiv2022

Parallel processing relies on a distributed, low-dimensional cortico-cerebellar architecture

A characteristic feature of human cognition is our ability to multi-task - performing two or more tasks in parallel - particularly when one task is well-learned. How the brain supports this capacity remains poorly understood. Most past studies have focussed on identifying the areas of the brain - typically the dorsolateral prefrontal cortex - that are required to navigate information processing bottlenecks. In contrast, we take a systems neuroscience approach to test the hypothesis that the capacity to conduct effective parallel processing relies on a distributed architecture that interconnects the cerebral cortex with the cerebellum. The latter structure contains over half of the neurons in the adult human brain, and is well-suited to support the fast, effective, dynamic sequences required to perform tasks relatively automatically. By delegating stereotyped within-task computations to the cerebellum, the cerebral cortex can be freed up to focus on the more challenging aspects of performing the tasks in parallel. To test this hypothesis, we analysed task-based fMRI data from 50 participants who performed a task in which they either balanced an avatar on a screen ( Balance), performed serial-7 subtractions ( Calculation) or performed both in parallel ( Dual-Task). Using a set of approaches that include dimensionality reduction, structure-function coupling and time-varying functional connectivity, we provide robust evidence in support of our hypothesis. We conclude that distributed interactions between the cerebral cortex and cerebellum are crucially involved in parallel processing in the human brain.

neuroscience↗

bioRxiv2021

Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

Brain pathologies are based on microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients excitation/inhibition profiles on neurodegenerative diseases including Alzheimers disease, Frontotemporal Dementia and Amyotrophic Lateral Sclerosis. In this work we used a simulation platform, The Virtual Brain, to simulate brain dynamics in healthy controls and in Alzheimers disease, Frontotemporal Dementia and Amyotrophic Lateral Sclerosis patients. The brain connectome and functional connectivity were extracted from 3T-MRI scans and The Virtual Brain nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. The integration of cerebro-cerebellar loops improved the correlation between experimental and simulated functional connectivity, and hence The Virtual Brain predictive power, in all pathological conditions. The Virtual Brain biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimers disease and stronger NMDA (N-methyl-D-aspartate) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed an advanced analysis of patients state. In backward regressions, The Virtual Brain parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of The Virtual Brain parameters and neuropsychological scores significantly improved discriminative power between clinical conditions. Eventually, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. In aggregate, The Virtual Brain simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.

neuroscience↗

bioRxiv2021

White matter microstructure characterisation in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy. Parameters of microstructural abnormalities derived from diffusion tensor imaging(DTI) have been reported to be helpful in differentiating between Left and Right TLE (L-TLE and R-TLE) but few of them compared L-TLE and R-TLE with a voxelwise approach. In this study, a whole brain tract based spatial statistical analysis was performed on DTI, diffusion kurtosis and NODDI derived parameters of 88 subjects to identify specific white matter patterns of alteration in patient affected by L-TLE and R-TLE with respect to healthy controls. Our findings demonstrated the presence of specific patterns of white matter alterations, with L-TLE more widely affected both in cerebral and cerebellar regions. This result supports the need to consider patients separately, according to the side of their pathology.

neuroscience↗

bioRxiv2020

Automatic segmentation of dentate nuclei for microstructure assessment: example of application to temporal lobe epilepsy patients

Dentate nuclei (DNs) segmentation is helpful for assessing their potential involvement in neurological diseases. Once DNs have been segmented, it becomes possible to investigate whether DNs they are microstructurally affected, through analysis of quantitative MRI parameters, such as the ones derived from diffusion weighted imaging (DWI). This study, therefore, aimed to develop a fully automated segmentation method using the non-DWI (b0) images from a DWI dataset to obtain DN masks inherently registered with parameter maps. Three different automatic methods were applied to healthy subjects in order to segment the DNs: registration to SUIT (a spatially unbiased atlas template of the cerebellum and brainstem), OPAL (Optimized Patch Match for Label fusion) and CNN (Convolutional Neural Network). DNs manual segmentation was considered the gold standard. Results show that the segmentation obtained with SUIT has an average Dice Similarity Coefficient (DSC) of 0.4907{+/-}0.0793 between the automatic SUIT masks and the gold standard. A comparison with manual masks was also performed for OPAL (DSC = 0.7624 {+/-} 0.1786) and CNN (DSC = 0.8658 {+/-} 0.0255), showing a better performance when using CNN. OPAL and CNN were optimised on heathy subjects data with high spatial resolution from the Human Connectome Project. The three methods were further used to segment the DNs of a subset of subjects affected by Temporal Lobe Epilepsy (TLE). This subset was derived from a 3T MRI research study which included DWI data acquired with a coarser resolution. In TLE dataset, SUIT performed similarly to using the HCP dataset, with a DSC = 0.4145 {+/-} 0.1023. Using TLE data, OPAL performed worse than using HCP data: after changing the probability threshold the DSC was 0.4522 {+/-} 0.1178. CNN was able to extract the DNs using the TLE data without need for retraining and with a good DSC = 0.7368 {+/-} 0.0799. Statistical comparison of quantitative parameters derived from DWI analysis, as well as volumes of each DN, revealed altered and lateralised changes in TLE patients compared to healthy controls. The proposed CNN is therefore a viable option for accurate extraction of DNs from b0 images of DWI data with different resolutions and acquired at different sites.

bioengineering↗

bioRxiv2019

Cervical spinal cord atrophy and Alzheimer’s disease

ObjectiveBrain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and viceversa, it is indeed a very interesting question to assess whether it is affected by atrophy in a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We therefore hypothesize that Alzheimer Disease severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia.\n\nMethods3DT1 images of 31 Alzheimers disease (AD) and 35 healthy control (HC) subjects were processed to calculate volumes of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord (per vertebra as well as the C2-C3 pair (CSA23 and CSV23)). Correlated features ({rho}>0.7) were removed, and best subset identified for patients classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p<=0.05). Linear regression was implemented to assess the explained variance of the Mini Mental State Examination (MMSE) score as dependent variable with best features as predictors.\n\nResultsSpinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and grey matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance.\n\nDiscussionOur findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. Results show that CSA23, calculated form the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD.

neuroscience↗

bioRxiv2018

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

PurposeFiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do not capture variabilities because of in vivo physiological factors.\n\nMethodsTo date, a large-scale reproducibility analysis has not been performed for the assessment of the newest generation of tractography algorithms with in vivo data. Reproducibility does not assess the validity of a brain connection however it is still of critical importance because it describes the variability for an algorithm in group studies. The ISMRM 2017 TraCED challenge was created to fulfill the gap. The TraCED dataset consists of a single healthy volunteer scanned on two different scanners of the same manufacturer. The multi-shell acquisition included b-values of 1000, 2000 and 3000 s/mm2 with 20, 45 and 64 diffusion gradient directions per shell, respectively.\n\nResultsNine international groups submitted 46 tractography algorithm entries. The top five submissions had high ICC > 0.88. Reproducibility is high within these top 5 submissions when assessed across sessions or across scanners. However, it can be directly attributed to containment of smaller volume tracts in larger volume tracts. This holds true for the top five submissions where they are contained in a specific order. While most algorithms are contained in an ordering there are some outliers.\n\nConclusionThe different methods clearly result in fundamentally different tract structures at the more conservative specificity choices (i.e., volumetrically smaller tractograms). The data and challenge infrastructure remain available for continued analysis and provide a platform for comparison.

neuroscience↗

bioRxiv2018

Challenges and perspectives of functional sodium imaging (fNaI)

Brain function has been investigated via the blood oxygenation level dependent (BOLD) effect using magnetic resonance imaging (MRI) for the past decades. Advances in sodium imaging offer the unique chance to access signal changes directly linked to sodium ions (23Na) flux across the cell membrane, which generates action potentials, hence signal transmission in the brain. During this process 23Na transiently accumulates in the intracellular space. Here we show that quantitative functional sodium imaging (fNaI) at 3T is potentially sensitive to 23Na concentration changes during finger tapping, which can be quantified in grey and white matter regions key to motor function. For the first time, we measured a 23Na concentration change of 0.54 mmol/l in the ipsilateral cerebellum, 0.46 mmol/l in the contralateral primary motor cortex, 0.27 mmol/l in the corpus callosum and -11 mmol/l in the ipsilateral primary motor cortex, suggesting that fNaI is sensitive to distributed functional alterations. Open issues persist on the role of the glymphatic system in maintaining 23Na homeostasis, the role of excitation and inhibition as well as volume distributions during neuronal activity. Hemodynamic and physiological signal recordings coupled to realistic models of tissue function will be critical to understand the mechanisms of such changes and contribute to meeting the overarching challenge of measuring neuronal activity in vivo.

neuroscience↗