Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications

The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation [...].

The quantification of IntraVoxel incoherent motion - MRI maps cannot preserve texture information: An evaluation based on simulated and in-vivo images

BACKGROUND

Radiomics can be applied on parametric maps obtained from IntraVoxel Incoherent Motion (IVIM) MRI to characterize heterogeneity in diffusion and perfusion tissue properties. The purpose of this work is to assess the accuracy and reproducibility of radiomic features computed from IVIM maps using different fitting methods.

METHODS

200 digitally simulated IVIM-MRI images with various SNR containing different combinations of texture patterns were generated from ground truth maps of true diffusion D, pseudo-diffusion D* and perfusion fraction f. Four different methods (segmented least-square LSQ, Bayesian, supervised and unsupervised deep learning DL) were adopted to quantify IVIM maps from simulations and from two real images of liver tumor. Radiomic features were computed from ground truth and estimated maps. Accuracy and reproducibility among quantification methods were assessed.

RESULTS

Almost 50% of radiomic features computed from D maps using DL approaches, 36% using Bayes and 27% using LSQ presented errors lower than 50%. Radiomic features from f and D* maps were accurate only if computed using DL methods from histogram. High reproducibility (ICC>0.8) was found only for D maps among DL and Bayes methods, whereas features from f and D* maps were less reproducible, with LSQ approach in lower agreement with the others.

CONCLUSIONS

Texture patterns were preserved and correctly estimated only on D maps, except for LSQ approach. We suggest limiting radiomic analysis only to histogram and some texture features from D maps, to histogram features from f maps, and to avoid it on D* maps.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

BACKGROUND AND OBJECTIVE

Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs).

METHODS

Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis.

RESULTS

MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively).

CONCLUSIONS

The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi-SNR images

Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusion-weighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a limitation of this technique. Artificial intelligence can overcome the current limitations and be a suitable solution to advance use of this technique in both preclinical and clinical settings. The purpose of this work was to develop a deep neural network (DNN) approach, trained on numerical simulated phantoms with different signal to noise ratios (SNRs), to improve IVIM parameter estimation. The proposed approach is based on a supervised fully connected DNN having 3 hidden layers, 18 inputs and 3 targets with standardized values. 14 × 10³ simulated DW images, based on a Shepp-Logan phantom, were randomly generated with varying SNRs (ranging from 10 to 100). 7 × 10³ images (1000 for each SNR) were used for training. Performance accuracy was assessed in simulated images and the proposed approach was compared with the state-of-the-art Bayesian approach and other DNN algorithms. The DNN approach was also evaluated in vivo on a high-field MRI preclinical scanner. Our DNN approach showed an overall improvement in accuracy when compared with the Bayesian approach and other DNN methods in most of the simulated conditions. The in vivo results demonstrated the feasibility of the proposed approach in real settings and generated quantitative results comparable to those obtained using the Bayesian and unsupervised approaches, especially for D and f, and with lower variability in homogeneous regions. The DNN architecture proposed in this work outlines two innovative features as compared with other studies: (1) the use of standardized targets to improve the estimation of parameters, and (2) the implementation of a single DNN to enhance the IVIM fitting at different SNRs, providing a valuable alternative tool to compute IVIM parameters in conditions of high background noise.

Comparison between directed causal flow metrics for the assessment of resting-state EEG motor network connectivity in subacute stroke patients

Isolated effective coherence (iCoh) is a measure of neural causal functional connectivity from EEG signals that was proven to overperform the Generalized Partial Directed Coherence (gPDC). However, iCoh sensitivity in the identification of reliable functional neural connections with respect to random links was not investigated. This study aims to compare the sensitivity of iCoh and gPDC with a statistical surrogates' approach. The cerebral motor network topology of a cohort of subjects in sub-acute stage after stroke was investigated. iCoh showed enhanced statistical discriminative power of the relevant connections within the motor network with respect to gPDC. This property influenced the assessment of ipsilesional intra-hemispheric topographic variations occurring in the population after a physical rehabilitation program.

A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals

Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.

The stability of oncologic MRI radiomic features and the potential role of deep learning: a review

The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.

Resting State EEG Directed Functional Connectivity Unveils Changes in Motor Network Organization in Subacute Stroke Patients After Rehabilitation

Brain plasticity and functional reorganization are mechanisms behind functional motor recovery of patients after an ischemic stroke. The study of resting-state motor network functional connectivity by means of EEG proved to be useful in investigating changes occurring in the information flow and find correlation with motor function recovery. In the literature, most studies applying EEG to post-stroke patients investigated the undirected functional connectivity of interacting brain regions. Quite recently, works started to investigate the directionality of the connections and many approaches or features have been proposed, each of them being more suitable to describe different aspects, e.g., direct or indirect information flow between network nodes, the coupling strength or its characteristic oscillation frequency. Each work chose one specific measure, despite in literature there is not an agreed consensus, and the selection of the most appropriate measure is still an open issue. In an attempt to shed light on this methodological aspect, we propose here to combine the information of direct and indirect coupling provided by two frequency-domain measures based on Granger’s causality, i.e., the directed coherence (DC) and the generalized partial directed coherence (gPDC), to investigate the longitudinal changes of resting-state directed connectivity associated with sensorimotor rhythms α and β, occurring in 18 sub-acute ischemic stroke patients who followed a rehabilitation treatment. Our results showed a relevant role of the information flow through the pre-motor regions in the reorganization of the motor network after the rehabilitation in the sub-acute stage. In particular, DC highlighted an increase in intra-hemispheric coupling strength between pre-motor and primary motor areas, especially in ipsi-lesional hemisphere in both α and β frequency bands, whereas gPDC was more sensitive in the detection of those connection whose variation was mostly represented within the population. A decreased causal flow from contra-lesional premotor cortex towards supplementary motor area was detected in both α and β frequency bands and a significant reinforced inter-hemispheric connection from ipsi to contra-lesional pre-motor cortex was observed in β frequency. Interestingly, the connection from contra towards ipsilesional pre-motor area correlated with upper limb motor recovery in α band. The usage of two different measures of directed connectivity allowed a better comprehension of those coupling changes between brain motor regions, either direct or mediated, which mostly were influenced by the rehabilitation, revealing a particular involvement of the pre-motor areas in the cerebral functional reorganization.

"Hi This Is NESTORE, Your Personal Assistant": Design of an Integrated IoT System for a Personalized Coach for Healthy Aging

In the context of the fourth revolution in healthcare technologies, leveraging monitoring and personalization across different domains becomes a key factor for providing useful services to maintain and promote well-being. This is even more crucial for older people, with aging being a complex multi-dimensional and multi-factorial process which can lead to frailty. The NESTORE project was recently funded by the EU Commission with the aim of supporting healthy older people to sustain their well-being and capacity to live independently. It is based on a multi-dimensional model of the healthy aging process that covers physical activity, nutrition, cognition, and social activity. NESTORE is based on the paradigm of the human-in-the-loop cyber-physical system that, exploiting the availability of Internet of Things technologies combined with analytics in the cloud, provides a virtual coaching system to support healthy aging. This work describes the design of the NESTORE methodology and its IoT architecture. We first model the end-user under several domains, then we present the NESTORE system that, analyzing relevant key-markers, provides coaching activities and personalized feedback to the user. Finally, we describe the validation strategy to assess the effectiveness of NESTORE as a coaching platform for healthy aging.

Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review

Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters

PURPOSE

To propose a multivariate multi-step framework for a systematic assessment of the estimation reliability and discriminability of Intravoxel Incoherent Motion (IVIM) model parameters.

METHODS

Monte-Carlo simulations were generated on a range of SNRs and in different IVIM combinations considering: i) a dense discretization with 24 b-values; ii) a discretization with 9 b-values. A state-of-the-art Bayesian fitting method was adopted. The framework assessed: i) the best model between mono- and bi-exponential, through the BIC index; ii) the fitting accuracy; iii) the power in discriminating two different IVIM parameters distributions of estimated coefficients, using a multivariate test. Exemplificative oncologic cases were also presented.

RESULTS

The bi-exponential fitting was reliable for perfusion fraction higher than 5%, with high accuracy in D estimation, acceptable error for f, but high uncertainty in D*. The discrimination of two distributions is generally feasible if differences in D values (at least 0.3 x10^-3 mm²/s) are present; in the case of similar D values, a minimal difference of 5% in f can be discriminated just in case of balanced sample size and dense b-values discretization, whereas the impact of D* is quite negligible. These results were also supported by clinical examples.

CONCLUSIONS

IVIM model is generally accurate in estimating diffusion, but uncertainties related to perfusion estimation are not negligible and compromise the discrimination power when different populations should be differentiated. The proposed framework should be adopted as interpretative guidelines to better understand when IVIM model applied on real data can provide reliable findings.

A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion-weighted MRI

The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification. The two segmented Bayesian approaches, with and without CAR specification, were compared with two standard least-square and a direct Bayesian fitting methods. All approaches were tested on simulated images and real data of patients with head-and-neck and rectal cancer. Estimation accuracy and maps noisiness were quantified on simulated images, whereas the coefficient of variation and the goodness of fit were evaluated for real data. Both versions of the segmented Bayesian approach outperformed the standard methods on simulated images for pseudo-diffusion (D^∗ ) and perfusion fraction (f), whilst the segmented least-square fitting remained the less biased for the diffusion coefficient (D). On real data, Bayesian approaches provided the less noisy maps, and the two Bayesian methods without CAR generally estimated lower values for f and D^∗ coefficients with respect to the other approaches. The proposed segmented Bayesian approaches were superior, in terms of estimation accuracy and maps quality, to the direct Bayesian model and the least-square fittings. The CAR method improved the estimation accuracy, especially for D^∗ .

Triggered intravoxel incoherent motion MRI for the assessment of calf muscle perfusion during isometric intermittent exercise

The main aim of this paper was to propose triggered intravoxel incoherent motion (IVIM) imaging sequences for the evaluation of perfusion changes in calf muscles before, during and after isometric intermittent exercise. Twelve healthy volunteers were involved in the study. The subjects were asked to perform intermittent isometric plantar flexions inside the MRI bore. MRI of the calf muscles was performed on a 3.0 T scanner and diffusion-weighted (DW) images were obtained using eight different b values (0 to 500 s/mm² ). Acquisitions were performed at rest, during exercise and in the subsequent recovery phase. A motion-triggered echo-planar imaging DW sequence was implemented to avoid movement artifacts. Image quality was evaluated using the average edge strength (AES) as a quantitative metric to assess the motion artifact effect. IVIM parameters (diffusion D, perfusion fraction f and pseudo-diffusion D*) were estimated using a segmented fitting approach and evaluated in gastrocnemius and soleus muscles. No differences were observed in quality of IVIM images between resting state and triggered exercise, whereas the non-triggered images acquired during exercise had a significantly lower value of AES (reduction of more than 20%). The isometric intermittent plantar-flexion exercise induced an increase of all IVIM parameters (D by 10%; f by 90%; D* by 124%; fD* by 260%), in agreement with the increased muscle perfusion occurring during exercise. Finally, IVIM parameters reverted to the resting values within 3 min during the recovery phase. In conclusion, the IVIM approach, if properly adapted using motion-triggered sequences, seems to be a promising method to investigate muscle perfusion during isometric exercise.

Proof of concept of an automatic tool for bioluminescence imaging data analysis

Bioluminescence Imaging (BLI) is an important molecular imaging tool to assess complex biological processes in vivo. BLI is a sensitive technique, which is frequently used in small-animal preclinical research, mainly in oncology and neurology. Tracking of labeled cells is one of the major applications. However, BLI data analysis for the segmentation of up-taking regions and their quantification is not trivial and it is usually an operator-dependent activity. In this work, a proof of concept of an automatic method to analyze BL images is presented which is based on a multi-step approach. Different segmentation algorithms (K-means, Gaussian Mixture Model (GMM), and GMM initialized by K-means) were evaluated and an adequate image normalization step was suggested to include the background bioluminescence in the data analysis process. K-means segmentation is the most stable and accurate approach for different levels of signal intensity.

In Vivo Fate Imaging of Intracerebral Stem Cell Grafts in Mouse Brain

We generated transgenic human neural stem cells (hNSCs) stably expressing the reporter genes Luciferase for bioluminescence imaging (BLI) and GFP for fluorescence imaging, for multimodal imaging investigations. These transgenic hNSCs were further labeled with a clinically approved perfluoropolyether to perform parallel ¹⁹F MRI studies. In vitro validation demonstrated normal cell proliferation and differentiation of the transgenic and additionally labeled hNSCs, closely the same as the wild type cell line, making them suitable for in vivo application. Labeled and unlabeled transgenic hNSCs were implanted into the striatum of mouse brain. The time profile of their cell fate after intracerebral grafting was monitored during nine days following implantation with our multimodal imaging approach, assessing both functional and anatomical condition. The ¹⁹F MRI demarcated the graft location and permitted to estimate the cell number in the graft. BLI showed a pronounce cell loss during this monitoring period, indicated by the decrease of the viability signal. The in vivo obtained cell fate results were further validated and confirmed by immunohistochemistry. We could show that the surviving cells of the graft continued to differentiate into early neurons, while the severe cell loss could be explained by an inflammatory reaction to the graft, showing the graft being surrounded by activated microglia and macrophages. These results are different from earlier cell survival studies of our group where we had implanted the identical cells into the same mouse strain but in the cortex and not in the striatum. The cortical transplanted cells did not show any loss in viability but only pronounced and continuous neuronal differentiation.

In vivo quantitative magnetization transfer imaging correlates with histology during de- and remyelination in cuprizone-treated mice

The pool size ratio measured by quantitative magnetization transfer MRI is hypothesized to closely reflect myelin density, but their relationship has so far been confirmed mostly in ex vivo conditions. We investigate the correspondence between this parameter measured in vivo at 7.0 T, with Black Gold II staining for myelin fibres, and with myelin basic protein and beta-tubulin immunofluorescence in a hybrid longitudinal study of C57BL/6 and SJL/J mice treated with cuprizone, a neurotoxicant causing relatively selective myelin loss followed by spontaneous remyelination upon treatment suspension. Our results confirm that pool size ratio measurements correlate with myelin content, with the correlation coefficient depending on strain and staining method, and demonstrate the in vivo applicability of this MRI technique to experimental mouse models of multiple sclerosis.

7T MRI features in control human hippocampus and hippocampal sclerosis: an ex vivo study with histologic correlations

OBJECTIVE

Hippocampal sclerosis (HS) is the major structural brain lesion in patients with temporal lobe epilepsy (TLE). However, its internal anatomic structure remains difficult to recognize at 1.5 or 3 Tesla (T) magnetic resonance imaging (MRI), which allows neither identification of specific pathology patterns nor their proposed value to predict postsurgical outcome, cognitive impairment, or underlying etiologies. We aimed to identify specific HS subtypes in resected surgical TLE samples on 7T MRI by juxtaposition with corresponding histologic sections.

METHODS

Fifteen nonsclerotic and 18 sclerotic hippocampi were studied ex vivo using an experimental 7T MRI scanner. T2 -weighted images (T2wi) and diffusion tensor imaging (DTI) data were acquired and validated using a systematic histologic analysis of same specimens along the anterior-posterior axis of the hippocampus.

RESULTS

In nonsclerotic hippocampi, differences in MR intensity could be assigned to seven clearly recognizable layers and anatomic boundaries as confirmed by histology. All hippocampal subfields could be visualized also in the hippocampal head with three-dimensional imaging and angulated coronal planes. Only four discernible layers were identified in specimens with histopathologically confirmed HS. All sclerotic hippocampi showed a significant atrophy and increased signal intensity along the pyramidal cell layer. Changes in DTI parameters such as an increased mean diffusivity, allowed to distinguish International League Against Epilepsy (ILAE) HS type 1 from type 2. Whereas the increase in T2wi signal intensities could not be attributed to a distinct specific histopathologic substrate, that is, decreased neuronal or increased glial cell densities, intrahippocampal projections and fiber tracts were distorted in HS specimens suggesting a complex disorganization of the cellular composition, fiber networks, as well as its extracellular matrix.

SIGNIFICANCE

Our data further advocate high-resolution MRI as a helpful and promising diagnostic tool for the investigation of hippocampal pathology along the anterior-posterior extent in TLE, as well as in other neurologic and neurodegenerative disorders.

Different permeability of potassium salts across the blood-brain barrier follows the Hofmeister series

The passage of ions across biological membranes is regulated by passive and active mechanisms. Passive ion diffusion into organs depends on the ion-pairing properties of salts present in the serum. Potassium ions could affect brain activity by crossing the blood-brain barrier (BBB) and its accumulation in the extracellular cerebral space could precipitate seizures. In the present study, we analyze passive diffusion of a series of potassium salts in the in vitro isolated guinea pig brain preparation. Different potassium counter-anions confer ion-pairing and lipophilicity properties that modulate membrane diffusion of the salt. Extracellular recordings in different cortical areas demonstrated the presence of epileptiform activities that strongly relate to anion identity, following the qualitative order of the Hofmeister series. Indeed, highly lipophilic salts that easily cross the BBB enhanced extracellular potassium concentration measured by ion-selective electrodes and were the most effective pro-epileptic species. This study constitutes a novel contribution for the understanding of the potential epileptogenicity of potassium salts and, more generally, of the role of counter-anions in the passive passage of salts through biological membranes.

A critical role for regulatory T cells in driving cytokine profiles of Th17 cells and their modulation of glioma microenvironment

IL-17A, produced by Th17 cells, may play a dual role in antitumor immunity. Using the GL261-glioma model, we investigated the effects of Th17 cells on tumor growth and microenvironment. Th17 cells infiltrate mouse gliomas, increase significantly in a time-dependent manner similarly to Treg and do not express Foxp3. To characterize the direct effects of Th17 cells on GL261 murine gliomas and on tumor microenvironment, we isolated IL-17-producing cells enriched from splenocytes derived from naïve (nTh17) or glioma-bearing mice (gTh17) and pre-stimulated in vitro with or without TGF-β. Spleen-derived Th17 cells co-expressing IL-17, IFN-γ and IL-10, but not Treg marker Foxp3, were co-injected intracranially with GL261 in immune-competent mice. Mice co-injected with GL261 and nTh17 survived significantly longer than gTh17 (P < 0.006) and gliomas expressed high level of IFN-γ and TNF-α, low levels of IL-10 and TGF-β. In vitro IL-17 per se did not exert effects on GL261 proliferation; in vivo gliomas grew equally well intracranially in IL-17 deficient and wild-type mice. We further analyzed relationship between Th17 cells and Treg. Treg were significantly higher in splenocytes from glioma-bearing than naïve mice (P = 0.01) and gTh17 produced more IL-10 than IFN-γ (P = 0.002). In vitro depletion of Treg using PC61 in splenocytes from glioma-bearing mice causes increased IL-17/IFN-γ cells (P = 0.007) and decreased IL-17/IL-10 cells (P = 0.03). These results suggest that Th17 polarization may be induced by Treg and that Th17 cells in gliomas modulate tumor growth depending on locally produced cytokines.

Hind limb muscle atrophy precedes cerebral neuronal degeneration in G93A-SOD1 mouse model of amyotrophic lateral sclerosis: a longitudinal MRI study

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal, neurodegenerative disorder caused by the degeneration of motor neurons in the CNS, which results in complete paralysis of skeletal muscles. Recent experimental studies have suggested that the disease could initiate in skeletal muscle, rather than in the motor neurons. To establish the timeframe of motor neuron degeneration in relation to muscle atrophy in motor neuron disease, we have used MRI to monitor changes throughout disease in brain and skeletal muscle of G93A-SOD1 mice, a purported model of ALS. Longitudinal MRI examination of the same animals indicated that muscle volume in the G93A-SOD1 mice was significantly reduced from as early as week 8 of life, 4 weeks prior to clinical onset. Progressive muscle atrophy from week 8 onwards was confirmed by histological analysis. In contrast, brain MRI indicated that neurodegeneration occurs later in G93A-SOD1 mice, with hyperintensity MRI signals detected only at weeks 10-18. Neurodegenerative changes were observed only in the motor nuclei areas of the brainstem; MRI changes indicative of neurodegeneration were not detected in the motor cortex where first motor neurons originate, even at the late disease stage. This longitudinal MRI study establishes unequivocally that, in the experimental murine model of ALS, muscle degeneration occurs before any evidence of neurodegeneration and clinical signs, supporting the postulate that motor neuron disease can initiate from muscle damage and result from retrograde dying-back of the motor neurons.

Tractographic reconstruction protocol optimization in the rat brain in-vivo: towards a normal atlas

The tractographic reconstruction of anatomical and microstructural features provided by Magnetic Resonance (MR) Diffusion Tensor Imaging (DTI) gives essential information of brain damage in several pathological animal models. The optimization of a tractographic protocol is undertaken in normal rats for the future construction of a reference atlas, as prerequisite for preclinical pathological in-vivo studies. High field, preclinical in-vivo DTI faces important difficulties relevant to Signal-to-Noise Ratio (SNR), distortion, high required resolution, movement sensitivity. Given a pixel-size of 0.17 mm and TE/TR = 29/6500 ms, b value and slice thickness were fixed at 700 s/mm(2) and 0.58 mm, respectively, on preventive ex-vivo studies. In-vivo studies led to the choice of 30 diffusion directions, averaged on 16 runs. The final protocol required 51 min scanning and permitted a reliable reconstruction of main rat brain bundles. Tract reconstruction stopping rules required proper setting. In conclusion, the viability of DTI tractography on in-vivo rat studies was shown, towards the construction of a normal reference atlas.