The technical-scientific area of neurophysiology has seen significant advancements in recent years, with the introduction of new technologies aimed at improving the diagnosis, treatment, and management of neurological conditions. However, these advancements resulted in significant repercussions in terms of specificity and sensitivity, diagnostic accuracy, and prognosis in more specific situations, particularly in the integration of artificial intelligence (AI) and telemedicine. In this chapter, the authors aim to explore the current state of neurophysiology and examine the role of AI and telemedicine in addressing the challenges faced by this field. The authors will delve into the potential benefits and limitations of these technologies and their impact on patient outcomes, healthcare delivery, and the future of neurophysiology. The chapter's goal is to provide a comprehensive overview of the challenges and potentialities of AI and telemedicine in neurophysiology, highlighting their significance in advancing the field and improving patient care. 

The Intersection of Artificial Intelligence, Telemedicine, and Neurophysiology

1 Accessible MR Laboratory, Biomedical Engineering, and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Mount Sinai Hospital, New York City, New York 2 Department of Electrical and Computer Engineering, Technical University Munich, Munich, Germany 3 Department of Biomedical Engineering, Columbia University in the City of New York, New York City, New York, USA 4 Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York City, New York, USA 5 Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts

Developing and deploying deep learning models in brain MRI: a review

The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioral responses to standardized sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behavior-based diagnoses. One such comorbidity is Akinetic Mutism (AM), a rare neurological syndrome characterized 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 behavioral unresponsiveness and severe disorganization 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: (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of complete AM, (ii) a plausible neurophysiological explanation for behavioral 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.

Unconsciousness or unresponsiveness in Akinetic Mutism? Insights from a multimodal longitudinal exploration.

Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas‐guided computational framework based on brain disconnectome to assess the spatial–temporal patterns of WMH‐related structural disconnectivity within a longitudinal investigation. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was adopted with 91, 90 and 44 subjects including in cognitive normal aging, stable and progressive mild cognitive impairment (MCI), respectively. The parcel‐wise disconnectome was computed by indirect mapping of individual WMHs onto population‐averaged tractography atlas. By performing chi‐square test, we discovered a spatial–temporal pattern of brain disconnectome along AD evolution. When applied such pattern as predictor, our models achieved highest mean accuracy of 0.82, mean sensitivity of 0.86, mean specificity of 0.82 and mean area under the receiver operating characteristic curve (AUC) of 0.91 for predicting conversion from MCI to dementia, which outperformed methods utilizing lesion volume as predictors. Our analysis suggests that brain WMH‐related structural disconnectome contributes to AD evolution mainly through attacking connections between: (1) parahippocampal gyrus and superior frontal gyrus, orbital gyrus, and lateral occipital cortex; and (2) hippocampus and cingulate gyrus, which are also vulnerable to Aβ and tau confirmed by other researches. All the results further indicate that a synergistic relationship exists between multiple contributors of AD as they attack similar brain connectivity at the prodromal stage of disease.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.26344

Spatial–temporal patterns of brain disconnectome in Alzheimer's disease

Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.

/pdf/multi-scale-evaluation-of-sleep-quality-based-on-motion-1vszqk8u.pdf

Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device

Brain connectomics consists in the modeling of human brain as networks, mathematically represented as numerical connectivity matrices. However, this representation may result in difficult interpretation of the data. To overcome this limitation, graphical representation by connectograms is currently used via open-source tools, which, however, lack user-friendly interfaces and options to explore specific sub-networks. In this context, we developed SPIDER-NET (Software Package Ideal for Deriving Enhanced Representations of brain NETworks), an easy-to-use, flexible, and interactive tool for connectograms generation and sub-network exploration. This study aims to present SPIDER-NET and to test its potential impact on pilot cases. As a working example, structural connectivity (SC) was investigated with SPIDER-NET in a group of 17 healthy controls (HCs) and in two subjects with stroke injury (Case 1 and Case 2, both with a focal lesion affecting part of the right frontal lobe, insular cortex and subcortical structures). 165 parcels were determined from individual structural magnetic resonance imaging data by using the Destrieux atlas, and defined as nodes. SC matrices were derived with Diffusion Tensor Imaging tractography. SC matrices of HCs were averaged to obtain a single group matrix. SC matrices were then used as input for SPIDER-NET. First, SPIDER-NET was used to derive the connectogram of the right hemisphere of Case 1 and Case 2. Then, a sub-network of interest (i.e., including gray matter regions affected by the stroke lesions) was interactively selected and the associated connectograms were derived for Case 1, Case 2 and HCs. Finally, graph-based metrics were derived for whole-brain SC matrices of Case 1, Case 2 and HCs. The software resulted effective in representing the expected (dis) connectivity pattern in the hemisphere affected by the stroke lesion in Cases 1 and 2. Furthermore, SPIDER-NET allowed to test an a priori hypothesis by interactively extracting a sub-network of interest: Case 1 showed a sub-network connectivity pattern different from Case 2, reflecting the different clinical severity. Global and local graph-based metrics derived with SPIDER-NET were different between cases with stroke injury and HCs. The tool proved to be accessible, intuitive, and interactive in brain connectivity investigation and provided both qualitative and quantitative evidence.

/pdf/development-and-testing-of-spider-net-an-interactive-tool-78ihz5ll.pdf

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

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB.

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Deep Learning approaches are powerful tools in a great variety of classification tasks. However, they are limitedly accepted or trusted in clinical frameworks due to their typical “black box” outline: their architecture is well-known, but processes employed in classification are often inaccessible to humans. With this work, we explored the problem of “Explainable AI” (XAI) in Alzheimer's disease (AD) classification tasks. Data from a neuroimaging cohort (n = 251 from OASIS-3) of early-stage AD dementia and healthy controls (HC) were analysed. The MR scans were initially fed to a pre-trained DL model, which achieved good performance on the test set (AUC: 0.82, TPR: 0.78, TNR: 0.81). Results were then investigated by means of an XAI approach (Occlusion Sensitivity method) that provided measures of relevance (RV) as outcome. We compared RV values obtained within healthy tissues with those underlying white matter hyperintensity (WMH) lesions. The analysis was conducted on 4 different groups of data, obtained by stratifying correct and misclassified images according to the health condition of participants (AD/HC). Results highlighted that the DL model found favourable leveraging lesioned brain areas for AD identification. A statistically significant difference ($p<0.01$) between WMH and healthy tissue contributions was indeed observed for AD recognition, differently from the HC case ($p=0.27$). Clinical Relevance - This study, though preliminary, suggested that DL models might be trained to use known clinical information and reinforced the role of WMHs as neuroimaging biomarker for AD dementia. The outlined findings have a significant clinical relevance as they prepare the ground for a progressive increase in the level of trust laid in DL approaches.

Explainable AI Points to White Matter Hyperintensities for Alzheimer's Disease Identification: a Preliminary Study

Alzheimer's Disease (AD) is the world leading cause of dementia, a progressively impairing condition leading to high hospitalization rates and mortality. To optimize the diagnostic process, numerous efforts have been directed towards the development of deep learning approaches (DL) for the automatic AD classification. However, their typical black box outline has led to low trust and scarce usage within clinical frameworks. In this work, we propose two state-of-the art DL models, trained respectively on structural MRI (ResNet18) and brain connectivity matrixes (BC-GCN-SE) derived from diffusion data. The models were initially evaluated in terms of classification accuracy. Then, results were analyzed using an Explainable Artificial Intelligence (XAI) approach (Grad-CAM) to measure the level of interpretability of both models. The XAI assessment was conducted across 132 brain parcels, extracted from a combination of the Harvard-Oxford and AAL brain atlases, and compared to well-known pathological regions to measure adherence to domain knowledge. Results highlighted acceptable classification performance as compared to the existing literature (ResNet18: TPRmedian = 0.817, TNRmedian = 0.816; BC-GCN-SE: TPRmedian = 0.703, TNRmedian = 0.738). As evaluated through a statistical test (p<0.05) and ranking of the most relevant parcels (first 15%), Grad-CAM revealed the involvement of target brain areas for both the ResNet18 and BC-GCN-SE models: the medial temporal lobe and the default mode network. The obtained interpretabilities were not without limitations. Nevertheless, results suggested that combining different imaging modalities may result in increased classification performance and model reliability. This could potentially boost the confidence laid in DL models and favor their wide applicability as aid diagnostic tools.

Davide Coluzzi

Papers

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

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Explainable AI Points to White Matter Hyperintensities for Alzheimer's Disease Identification: a Preliminary Study

Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device

Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification