Positional contrastive learning for improved thigh muscle segmentation in MR images.

The accurate segmentation of individual muscles is essential for quantitative MRI analysis of thigh images. Deep learning methods have achieved state-of-the-art results in segmentation, but they require large numbers of labeled data to perform well. However, labeling individual thigh muscles slice by slice for numerous volumes is a laborious and time-consuming task, which limits the availability of annotated datasets. To address this challenge, self-supervised learning (SSL) emerges as a promising technique to enhance model performance by pretraining the model on unlabeled data. A recent approach, called positional contrastive learning, exploits the information given by the axial position of the slices to learn features transferable on the segmentation task. The aim of this work was to propose positional contrastive SSL for the segmentation of individual thigh muscles from MRI acquisitions in a population of elderly healthy subjects and to evaluate it on different levels of limited annotated data. An unlabeled dataset of 72 T1w MRI thigh acquisitions was available for SSL pretraining, while a labeled dataset of 52 volumes was employed for the final segmentation task, split into training and test sets. The effectiveness of SSL pretraining to fine-tune a U-Net architecture for thigh muscle segmentation was compared with that of a randomly initialized model (RND), considering an increasing number of annotated volumes (S = 1, 2, 5, 10, 20, 30, 40). Our results demonstrated that SSL yields substantial improvements in Dice similarity coefficient (DSC) when using a very limited number of labeled volumes (e.g., for S $$ S $$ = 1, DSC 0.631 versus 0.530 for SSL and RND, respectively). Moreover, enhancements are achievable even when utilizing the full number of labeled subjects, with DSC = 0.927 for SSL and 0.924 for RND. In conclusion, positional contrastive SSL was effective in obtaining more accurate thigh muscle segmentation, even with a very low number of labeled data, with a potential impact of speeding up the annotation process in clinics.

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 [...].

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.

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.

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 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∗ .

Microstructural characterization of corticospinal tract in subacute and chronic stroke patients with distal lesions by means of advanced diffusion MRI.

PURPOSE: The aim of the paper is to evaluate if advanced dMRI techniques, including diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI), could provide novel insights into the subtle microarchitectural modifications occurring in the corticospinal tract (CST) of stroke patients in subacute and chronic phases. METHODS: Seventeen subjects (age 68 ± 11 years) in the subacute phase (14 ± 3 days post-stroke), 10 of whom rescanned in the chronic phase (231 ± 36 days post-stroke), were enrolled. Images were acquired using a 3-T MRI scanner with a two-shell EPI protocol (20 gradient directions, b = 700 s/mm2, 3 b = 0; 64 gradient directions, b = 2000 s/mm2, 9 b = 0). DTI-, DKI-, and NODDI-derived parameters were calculated in the posterior limb of the internal capsule (PLIC) and in the cerebral peduncle (CP). RESULTS: In the subacute phase, a reduction of FA, AD, and KA values was correlated with an increase of ODI, RD, and AK parameters, in both the ipsilesional PLIC and CP, suggesting that increased fiber dispersion can be the main structural factor. In the chronic phase, a reduction of FA and an increase of ODI persisted in the ipsilesional areas. This was associated with reduced Fic and increased MD, with a concomitant reduction of MK and increase of RD, suggesting that fiber reduction, possibly due to nerve degeneration, could play an important role. CONCLUSIONS: This study shows that advanced dMRI approaches can help elucidate the underpinning architectural modifications occurring in the CST after stroke. Further follow-up studies on bigger cohorts are needed to evaluate if DKI- and NODDI-derived parameters might be proposed as complementary biomarkers of brain microstructural alterations.

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.

Superparamagnetic Nanoparticles as High Efficiency Magnetic Resonance Imaging T2 Contrast Agent.

Nanoparticle-based magnetic resonance imaging T2 negative agents are of great interest, and much effort is devoted to increasing cell-loading capability while maintaining low cytotoxicity. Herein, two classes of mixed-ligand protected magnetic-responsive, bimetallic gold/iron nanoparticles (Au/Fe NPs) synthesized by a two-step method are presented. Their structure, surface composition, and magnetic properties are characterized. The two classes of sulfonated Au/Fe NPs, with an average diameter of 4 nm, have an average atomic ratio of Au to Fe equal to 7 or 8, which enables the Au/Fe NPs to be superparamagnetic with a blocking temperature of 56 K and 96 K. Furthermore, preliminary cellular studies reveal that both Au/Fe NPs show very limited toxicity. MRI phantom experiments show that r2/r1 ratio of Au/Fe NPs is as high as 670, leading to a 66% reduction in T2 relaxation time. These nanoparticles provide great versatility and potential for nanoparticle-based diagnostics and therapeutic applications and as imaging contrast agents.

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.

A superfluorinated molecular probe for highly sensitive in vivo(19)F-MRI.

(19)F-MRI offers unique opportunities to image diseases and track cells and therapeutic agents in vivo. Herein we report a superfluorinated molecular probe, herein called PERFECTA, possessing excellent cellular compatibility, and whose spectral properties, relaxation times, and sensitivity are promising for in vivo (19)F-MRI applications. The molecule, which bears 36 equivalent (19)F atoms and shows a single intense resonance peak, is easily synthesized via a simple one-step reaction and is formulated in water with high stability using trivial reagents and methods.

Optimization of rapid acquisition with relaxation enhancement (RARE) pulse sequence parameters for ¹9F-MRI studies.

PURPOSE: To optimize signal-to-noise ratio (SNR) in fast spin echo (rapid acquisition with relaxation enhancement [RARE]) sequences and to improve sensitivity in ¹9F magnetic resonance imaging (MRI) on a 7T preclinical MRI system, based on a previous experimental evaluation of T1 and T2 actual relaxation times. MATERIALS AND METHODS: Relative SNR changes were theoretically calculated at given relaxation times (T1, T2) and mapped in RARE parameter space (TR, number of echoes, flip back pulse), at fixed acquisition times. T1 and T2 of KPF6 phantom samples (solution, agar mixtures, ex vivo perfused brain) were measured and experimental SNR values were compared with simulations, at optimal and suboptimal RARE parameter values. RESULTS: The optimized setting largely depended on T1, T2 times and the use of flip back pulse improved SNR up to 30% in case of low T1/T2 ratios. Relaxation times in different conditions showed negligible changes in T1 (below 14%) and more evident changes in T2 (-95% from water solution to ex vivo brain). Experimental data confirmed theoretical forecasts, within an error margin always below 4.1% at SNR losses of ~20% and below 8.8% at SNR losses of ~40%. The optimized settings permitted a detection threshold at a concentration of 0.5 mM, corresponding to 6.22 × 10¹6 fluorine atoms per voxel. CONCLUSION: Optimal settings according to measured relaxation times can significantly improve the sensitivity threshold in ¹9F MRI studies. They were provided in a wide range of (T1, T2) values and experimentally validated showing good agreement.

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.

Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model.

Muscular dystrophies present diagnostic challenges, requiring accurate classification for effective diagnosis and treatment. This study investigates the efficacy of deep learning methodologies in classifying these disorders using skeletal muscle MRI scans. Specifically, we assess the performance of the Swin Transformer (SwinT) architecture against traditional convolutional neural networks (CNNs) in distinguishing between healthy individuals, Becker muscular dystrophy (BMD), and limb-girdle muscular Dystrophy type 2 (LGMD2) patients. Moreover, 3T MRI scans from a retrospective dataset of 75 scans (from 54 subjects) were utilized, with multiparametric protocols capturing various MRI contrasts, including T1-weighted and Dixon sequences. The dataset included 17 scans from healthy volunteers, 27 from BMD patients, and 31 from LGMD2 patients. SwinT and CNNs were trained and validated using a subset of the dataset, with the performance evaluated based on accuracy and F-score. Results indicate the superior accuracy of SwinT (0.96), particularly when employing fat fraction (FF) images as input; it served as a valuable parameter for enhancing classification accuracy. Despite limitations, including a modest cohort size, this study provides valuable insights into the application of AI-driven approaches for precise neuromuscular disorder classification, with potential implications for improving patient care.

Blurring in patients with temporal lobe epilepsy: clinical, high-field imaging and ultrastructural study.

Magnetic resonance imaging-positive temporal lobe atrophy with temporo-polar grey/white matter abnormalities (usually called 'blurring') has been frequently reported in patients with temporal lobe epilepsy associated with hippocampal sclerosis. The poor distinction of grey and white matter has been attributed to various causes, including developmental cortical abnormalities, gliosis, myelin alterations, a non-specific increase in temporal lobe water content and metabolic/perfusion alterations. However, there is still no consensus regarding the genesis of these abnormalities and no histopathological proof for a structural nature of magnetic resonance imaging changes. The aim of this study was to investigate the pathological substrate of temporo-polar blurring using different methodological approaches and evaluate the possible clinical significance of the abnormalities. The study involved 32 consecutive patients with medically intractable temporal lobe epilepsy and hippocampal sclerosis who underwent surgery after a comprehensive electroclinical and imaging evaluation. They were divided into two groups on the basis of the presence/absence of temporo-polar blurring. Surgical specimens were examined neuropathologically, and selected samples from both groups underwent high-field 7 T magnetic resonance imaging and ultrastructural studies. At the clinical level, the two groups were significantly different in terms of age at epilepsy onset (earlier in the patients with blurring) and epilepsy duration (longer in the patients with blurring). Blurring was also associated with lower neuropsychological test scores, with a significant relationship to abstract reasoning. On 7 T magnetic resonance image examination, the borders between the grey and white matter were clear in all of the samples, but only those with blurring showed a dishomogeneous signal in the white matter, with patchy areas of hyperintensity mainly in the depth of the white matter. Sections from the patients with blurring that were processed for myelin staining revealed dishomogeneous staining of the white matter, which was confirmed by analyses of the corresponding semi-thin sections. Ultrastructural examinations revealed the presence of axonal degeneration and a significant reduction in the number of axons in the patients with blurring; there were no vascular alterations in either group. These data obtained using different methodological approaches provide robust evidence that temporo-polar blurring is caused by the degeneration of fibre bundles and suggest slowly evolving chronic degeneration with the redistribution of the remaining fibres. The article also discusses the correlations between the morphological findings and clinical data.

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.

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.

Penumbra region excitability is not enhanced acutely after cerebral ischemia in the in vitro isolated guinea pig brain.

PURPOSE: Early seizures are a frequent consequence of stroke. The main goal of the present study is to verify whether anoxic ischemia per se is able to induce early changes in excitability that may be a prelude to the generation of seizures and, ultimately, to epileptogenesis. Excitability changes in the very acute postischemic phase are here analyzed in a new model of ischemia developed in the isolated guinea pig brain preparation. METHODS: Permanent bilateral occlusion of the anterior cerebral arteries (ACAs) was performed in the isolated guinea pig brain maintained in vitro by arterial perfusion. Magnetic resonance imaging and immunohistochemistry were utilized to identify the penumbra and core regions induced by ACA occlusion (ACAo). Slow potentials and evoked responses recorded in olfactory cortices were utilized to evaluate excitability changes in the acute phase after ischemia. KEY FINDINGS: ACAo induces a core area located in the shell of the nucleus accumbens and a region of penumbra in the underlying olfactory cortices, where characteristic slow potential shifts, but no reduction of diffusion tensor magnetic resonance (MR) signal and microtubule associated protein 2 (MAP-2) immunostaining (typical of ischemic core) was observed. Recording of responses evoked by low- and high-frequency stimulations of the lateral olfactory tract showed no excitability changes in the early hours that follow ischemia in the olfactory cortical areas supplied by ACAs. SIGNIFICANCE: The absence of early hyperexcitability changes in an isolated whole brain model of ischemia, strongly suggests that brain anoxia per se does not contribute to the generation of early seizures. These findings support the view that blood-borne events (such as hemorrhage and inflammation) may play a major role in early postischemic seizures.