Development and Testing of SPIDER-NET: An Interactive Tool for Brain Connectogram Visualization, Sub-Network Exploration and Graph Metrics Quantification

Directed Weighted EEG Connectogram Insights of One-to-One Causality for Identifying Developmental Dyslexia

Developmental dyslexia (DD) affects approximately 5-12% of learners, posing persistent challenges in reading and writing. This study presents a novel electroencephalography (EEG)-based methodology for identifying DD using two auditory stimuli modulated at 4.8[Formula: see text]Hz (prosodic) and 40[Formula: see text]Hz (phonemic). EEG signals were processed to estimate one-to-one Granger causality, yielding directed and weighted connectivity matrices. A novel Mutually Informed Correlation Coefficient (MICC) feature selection method was employed to identify the most relevant causal links, which were visualized using connectograms. Under the 4.8[Formula: see text]Hz stimulus, altered theta-band connectivity between frontal and occipital regions indicated compensatory frontal activation for prosodic processing and visual-auditory integration difficulties, while gamma-band anomalies between occipital and temporal regions suggested impaired visual-prosodic integration. Classification analysis under the 4.8[Formula: see text]Hz stimulus yielded area under the ROC curve (AUC) values of 0.92 (theta) and 0.91 (gamma band). Under the 40[Formula: see text]Hz stimulus, theta abnormalities reflected dysfunctions in integrating auditory phoneme signals with executive and motor regions, and gamma alterations indicated difficulties coordinating visual and auditory inputs for phonological decoding, with AUC values of 0.84 (theta) and 0.89 (gamma). These results support both the Temporal Sampling Framework and the Phonological Core Deficit Hypothesis. Future research should extend the range of stimuli frequencies and include more diverse cohorts to further validate these potential biomarkers.

Efficacy of a Rehabilitation Treatment Using Action Observation Therapy Enhanced by Muscle Synergy-Derived Electrical Stimulation (OTHELLO) in Post-Stroke Patients: A RCT Study Protocol

Background: Action Observation Therapy (AOT) and Neuromuscular Electrical Stimulation (NMES) are widely adopted techniques for upper-limb rehabilitation in post-stroke patients. Although AOT and NMES are individually effective, studies investigating a potential synergistic effect on enhancing rehabilitative outcomes are lacking. Objectives: This study aims at comparing the effect of AOT and NMES applied together (AOT-NMES) on muscle synergies with respect to either AOT alone or a Motor Neutral Observation treatment alone (MNO, involving neither AOT nor NMES) on motor function recovery of upper limb. Design: Randomized Controlled Trial (RCT) with n = 60 post-stroke patients with resulting upper limb disability, randomly allocated (1:1:1 ratio) in three interventional arms: AOT-NMES (n = 20), AOT (n = 20) and MNO (n = 20). Methods and Analyses: All rehabilitation treatments will consist of n°15 60 min-long rehabilitative sessions. Primary outcome measure will be upper limb motor function, assessed using the Fugl-Meyer Assessment scale for upper limb (FM-UL), collected at the baseline (T0), post-intervention (T1) and at follow-up (T2, 6-months after T1). Other outcome measures will be collected through a multidimensional evaluation including assessing stroke-associated quality of life, neurophysiological data, biomechanical and MRI measures. The innovative protocol will also be evaluated for usability and safety. Discussion: We expect to determine the efficacy, usability and safety of the AOT-NMES rehabilitation approach for the recovery of upper limb motor function in post-stroke patients. The obtained results will also help reveal the neural underpinnings of motor recovery, as assessed by neurophysiological data, biomechanical and MRI measures.

Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer's Disease Classification

As the leading cause of dementia worldwide, Alzheimer's Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases.

Unconsciousness or unresponsiveness in akinetic mutism? Insights from a multimodal longitudinal exploration

The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioural responses to standardised sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behaviour-based diagnoses. One such comorbidity is akinetic mutism (AM), a rare neurological syndrome characterised by the inability to initiate volitional motor responses, sometimes associated with clinical presentations that overlap with those of DoC. In this paper, we describe the case of a patient with large bilateral mesial frontal lesions, showing prolonged behavioural unresponsiveness and severe disorganisation of electroencephalographic (EEG) background, compatible with a vegetative state/unresponsive wakefulness syndrome (VS/UWS). By applying an unprecedented multimodal battery of advanced imaging and electrophysiology-based techniques (AIE) encompassing spontaneous EEG, evoked potentials, event-related potentials, transcranial magnetic stimulation combined with EEG and structural and functional MRI, we provide the following: (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of AM, (ii) a plausible neurophysiological explanation for behavioural unresponsiveness and its subsequent recovery during rehabilitation stay and (iii) novel insights into the relationships between DoC, AM and parkinsonism. The present case offers proof-of-principle evidence supporting the clinical utility of a multimodal hierarchical workflow that combines AIEs to detect covert signs of consciousness in unresponsive patients.

Bridging functional and anatomical neural connectivity through cluster synchronization

The dynamics of the brain results from the complex interplay of several neural populations and is affected by both the individual dynamics of these areas and their connection structure. Hence, a fundamental challenge is to derive models of the brain that reproduce both structural and functional features measured experimentally. Our work combines neuroimaging data, such as dMRI, which provides information on the structure of the anatomical connectomes, and fMRI, which detects patterns of approximate synchronous activity between brain areas. We employ cluster synchronization as a tool to integrate the imaging data of a subject into a coherent model, which reconciles structural and dynamic information. By using data-driven and model-based approaches, we refine the structural connectivity matrix in agreement with experimentally observed clusters of brain areas that display coherent activity. The proposed approach leverages the assumption of homogeneous brain areas; we show the robustness of this approach when heterogeneity between the brain areas is introduced in the form of noise, parameter mismatches, and connection delays. As a proof of concept, we apply this approach to MRI data of a healthy adult at resting state.