Projects

Mental or brain disorders are characterized by a significant disturbance in a person’s cognition, emotional regulation or behavior, that can be occasional or chronic. Usually, mental disorders are connected to distress or impairment in important areas of functioning. In fact, 1 in every 8 people was living with a mental disorder in 2019; being anxiety and depressive disorders the most common. Despite the availability of effective prevention and treatment options, a large majority of those with mental disorders lack access to adequate care. Additionally, individuals with mental health issues often face stigma, discrimination, and human rights violations.

Notwithstanding the evident genetic substrate and the many cerebral alterations identified during the last decades, there is still little knowledge about the causes of severe psychiatric disorders, such as schizophrenia and bipolar disorder; which leads to a non-targeted treatment that is only partially efficacious. On top of that, there exists a large heterogeneity within each diagnosis, being the patients too different among themselves, suggesting that each diagnostic label includes different biologically substrates. Thus, segregating valid clusters of cases may help in identifying causal mechanisms that may be very useful in both diagnosis and in defining targets for new more tolerable and efficacious treatments. In the last decade, we have reported the possibility of identifying the aforementioned clusters based on the cerebral structure, the cognitive performance and the pattern of bioelectrical activity of patients. Concretely, we have reported a subgroup across schizophrenia and bipolar disorder characterized by a very poor cognitive performance, anatomical deficits and a hyperactive brain that cannot modulate adequately its activity during cognitive tasks.

A deficit in the inhibitory systems of the brain, which are based on the GABA neurotransmitter, is thought to contribute significantly to this group. Therefore, we propose examining its function using transcranial magnetic stimulation (TMS), since it is thought to modulate them. Moreover, we have been using it during the last years. We have already collected a sample of more than 200 cases and 200 controls studied with magnetic resonance, electroencephalography (EEG), clinical and cognitive assessments and, in part, transcranial magnetic stimulation. We need to increase this sample to approximately 500 subjects per group to be able to identify subgroups of patients based on the causal mechanisms contributing to their illness. Finally, we also expect that the patients with a significant cerebral inhibitory deficit would respond to a particular treatment (clozapine) selectively, since this drug improves GABA function in the cortex.

The main objectives are:

• To identify a group of patients with psychotic disorders (possibly including a majority of patients with schizophrenia, but also cases of bipolar disorders) characterized primarily by a significant cognitive deficit, which will be accompanied by abnormal ego experiences, functional cortical hyperactivation, decreased regional cortical thickness and decreased mean fractional anisotropy.
• To assess cortical GABA hypofunction of GABA-A or/and GABA-B type with TMS in patients and controls and its relation to cognition.
• To assess the relation between inhibitory deficits and cognitive task-induced variation (modulation) of bioelectrical activity, assessed with EEG.
• To compare treatment resistance between patients with or without inhibitory activity deficits, and the degree of response to clozapine
• To compare treatment resistance between patients with or without inhibitory activity deficits, and the degree of response to clozapine

In order to achieve the aforementioned objectives, different procedures are going to be implemented. First, we will use TMS-EEG data to assess the cortical inhibitory systems of the brain of both pathological and non-pathological populations. To assess this function, two different paired-pulses paradigms will be employed: the long-interval intracortical inhibition (LICI) protocol, that is thought to be mediated by GABA-B receptor and the short-interval intracortical inhibition (SICI), mediated by GABA-A receptor. For the evaluation of the cognitive performance, an auditory oddball task will be used. Three different tones (a distractor, a target and a standard tones) will be presented to the participant, who will have to press a button each time they hear the target one. The cortical activity during the task will be recorded with EEG. Both, TMS-EEG and EEG data will be analyzed to characterize the cortical activity of the participants and to extract the features needed to perform the clustering and the assessing of the cortical GABA hypofunction and the relation between the inhibitory deficits and the cognitive performance.

Publications
Fernández-Linsenbarth I, Mijancos-Martínez G, Bachiller A, Núñez P, Rodríguez-González V, Beño-Ruiz-de-la-Sierra RM, Roig-Herrero A, Arjona-Valladares A, Poza J, Mañanas MÁ, Molina V. Relation between task-related activity modulation and cortical inhibitory function in schizophrenia and healthy controls: a TMS-EEG study. Eur Arch Psychiatry Clin Neurosci. 2024 Jan 19. doi: /10.1007/s00406-023-01745-0 . Epub ahead of print. PMID: 38243018.

In this pilot study the authors aim to explore the association between EEG modulation during a cognitive task (using and auditory task) and the inhibitory system function (employing TMS-EEG) in vivo in a sample including healthy controls and patients with schizophrenia. Results replicated the task-related cortical activity modulation deficit in schizophrenia patients. Moreover, they showed higher cortical reactivity compared to healthy controls. Cortical reactivity was inversely associated with EEG modulation, supporting the idea that a hypofunction of the inhibitory system could hamper the task-related modulation of EEG activity.

Díez Á, Gomez-Pilar J, Poza J, Beño-Ruiz-de-la-Sierra R, Fernández-Linsenbarth I, Recio-Barbero M, Núñez P, Holgado-Madera P, Molina V. Functional network properties in schizophrenia and bipolar disorder assessed with high-density electroencephalography. Prog Neuropsychopharmacol Biol Psychiatry. 2024 Feb 8;129:110902. doi: 10.1016/j.pnpbp.2023.110902. Epub 2023 Nov 29. PMID: 38036032.

This study investigates cortical functional networks in schizophrenia (SCZ) using graph theory parameters applied to high-density EEG. Connectivity Strength (CS) measures global network synchrony, and Shannon Graph Complexity (SGC) assesses network distribution of link weights, allowing to distinguish between primary and secondary pathways. Brain activity of the participants (healthy controls -HC-, SCZ patients and bipolar disorder -BD- patietns) during a P300 oddball task was recorded using EEG. SCZ patients had higher pre-stimulus CS values and lower theta-band CS modulation compared to HC. Both SCZ and BD patients showed reduced theta-band CS modulation. Chronic SCZ patients also exhibited reduced SGC modulation. Relationships were found between theta-band SGC modulation and CS measures, which correlated with cognitive outcomes and negative symptoms. The study concluded that SCZ and BD patients have hyperactive, hypomodulatory networks, particularly in secondary pathways, independent of antipsychotic effects.

Beño-Ruiz-de-la-Sierra RM, Arjona-Valladares A, Hernández-García M, Fernández-Linsenbarth I, Díez Á, Fondevila Estevez S, Castaño C, Muñoz F, Sanz-Fuentenebro J, Roig-Herrero A, Molina V. Corollary Discharge Dysfunction as a Possible Substrate of Anomalous Self-experiences in Schizophrenia. Schizophr Bull. 2023 Nov 10:sbad157. doi: 10.1093/schbul/sbad157 . Epub ahead of print. PMID: 37951230.

The study explores the dysfunction of the corollary discharge mechanism in schizophrenia (SCZ), which suppresses the perception of self-generated speech and reduces the auditory N1 event-related potential during EEG recordings. A dysfunction in this mechanism may be linked to anomalous self-experiences (AEs) in SCZ.ERP were recorded with EEG from SCZ patients and healthy control; both during concurrent listening to self-produced vowels (talk condition) and and non-concurrent listening to the same vowels (listen condition). AEs and symptomatology were scored with IPASE, PANSS and BNSS. N1 amplitude was lower during talk conditions compared to listen condition in both populations. However, the difference in N1 amplitude between both conditions was significantly higher in controls than in schizophrenia patients. These differences in patients correlated significantly and negatively with the IPASE, PANSS, and BNSS scores. The findings suggest that corollary discharge dysfunction may underlie ASEs in schizophrenia.

Beño-Ruiz-de-la-Sierra RM, Arjona-Valladares A, Fondevila Estevez S, Fernández-Linsenbarth I, Díez Á, Molina V. Corollary discharge function in healthy controls: Evidence about self-speech and external speech processing. Eur J Neurosci. 2023 Oct;58(7):3705-3713. doi: 10.1111/ejn.16125. Epub 2023 Aug 27. PMID: 37635264.

The study examines corollary discharge in healthy individuals, which reduces perception of self-generated speech and attenuates the auditory N1 component. Event-related potentials were recorded in a healthy population in three conditions: self-spoken vowels (talk condition), played-back vowels of their own voice (listen-self condition), and played-back vowels of an external voice (listen-other condition). The N1 amplitude was smaller for self-spoken vowels compared to both played-back conditions, with no differences between the two listen conditions. The P2 component and peak latencies showed no differences across conditions. These results confirm that corollary discharge dampens sensory responses to self-generated speech and provide new neurophysiological evidence about the similarities in the processing of played-back vowels with our own voice (ownership experience) and with an external voice.

Congresses proceedings

2024 SIRS Congress (Schizophrenia International Research Society), 3-7 April, Florence, Italy.
Fernández-Linsenbarth, I; Mijancos-Martínez, G; Bachiller, A; Beño-Ruis-de-la-Sierra, R; Arjona-Valladares, A; Roig-Herrero, A; Mañanas, A; Molina, V. Cortical reactivity and task-related activity modulation in schizophrenia and healthy controls: A TMS-EEG study.

Beño-Ruiz-de-la-Sierra, Arjona-Valladares, A; R; G; Fondevila-Estévez, S; Fernández-Linsenbarth, I; Roig-Herrero, A; Díez, A; Molina, V. Corollary discharge dysfunction as a potential underlying mechanism for Abnormal Self-Experiences and its association with clinical symptoms in schizophrenia.

2023 EMBC Congress (45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society), 24-27 July, Sydney, Australia
Mijancos-Martinez G, Bachiller A, Fernandez-Linsenbarth I, Romero S, Alonso JF, Molina V, Mañanas MA. Cortical inhibition on TMS-EEG: interstimulus interval effect on short-interval paired-pulse. Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10340654 . PMID: 38083290.

Strength training, functional exercises, and movement imbalance assessment are the major pillars of the typical motor rehabilitation process and evaluation. Surprisingly, therapists just have the patient's perspective regarding muscular weariness or pain; they do not evaluate neuromuscular activity, even if it is the cause of the disorder. Since there is currently no commercial tool that can accurately assess muscular status, patients and therapists are generally unaware of it, despite the fact that it is essential to defining the rehabilitation program and tracking its progress.

The available systems to evaluate muscle function for diagnosis consist of intramuscular electromyography (EMG) delivered using needles, which is highly accurate but causes discomfort to patients. The alternative is surface EMG, a non-invasive option that relies primarily on two electrodes, showing a limitation when it comes to measuring muscle activity and, consequently, muscular functionality.

BIOART team improved the surface EMG muscle activity system by advancing innovative technology by developing electrode arrays on a highly adaptable technical fabric. The multiple EMG channels displayed on an array can be recorded over a significant muscle area. Through wireless transmission to a computer or tablet, this project enabled us to finish the preindustrial prototype, create user-friendly interfaces that let patients direct the exercises and give clinicians indexes and reports to track their progress, and, lastly, apply machine learning algorithms for automated patient evaluation.

During this project, we conducted a comprehensive market analysis, examining different potential customer segments and regions, including market size, go-to-market strategy, and reimbursement models. We integrated all available information to develop a preliminary business plan outlining the spin-off creation model and the commercialization of MyoArm as a product for rehabilitation centers, either directly or through insurance companies. The preliminary business plan has been continuously refined and expanded into an investor-ready plan with a 5-year forecast.

The project helped us to mature the technology to create a commercially available product to fit a need in the market. During the product development, we explored other areas that helped us design a roadmap for technology transfer and prepare us as a team for future spin-off creation and product commercialization.

The main objectives are:

• Objective 1: Technical Development. Integration of 2D dry electrode arrays into fabric for obtaining a sensor for the upper arm; completion of the preindustrial prototype by transmitting wirelessly to a computer/tablet; the development of user-friendly interfaces for patients to guide the exercises and for clinicians with indexes and reports to monitor the patient’s progress; and finally, the implementation of Machine Learning algorithms for automatic patient’s evaluation.
• Objective 2: Clinical Valorisation. System validation in control subjects and patients.
• Objective 3: Preliminary Business Development. Business development and technology transfer initiatives including market analysis studies, financial roadmap, business plan development, intellectual property initiatives, regulatory roadmap and exploitation activities.
• Objective 4: Integrating the interdisciplinary team to have a less expensive MVP to obtain the most important clinical outcome on time.

The methodology includes three main areas, Management, Technical Solution and Clinical Evaluation.

MANAGEMENT The project is organized into three working areas associated with the acceleration of the technology transfer: technical, clinical and business. In all areas, the participation of physicians and engineers are very important:
• to design and to develop a medical device according to the needs and usability of therapists and patients.
• its validation with the support of engineers to show its management and functionalities to the physicians during the RCT and the later upgrade.
• to define business strategies for market access of private and public health system and CE mark regulation.
An interdisciplinary team is crucial for the success of all issues and its integration has been considered with a specific Work Package and specific objectives. The project team is composed of experts in biomedical engineering, and medical doctors experts in rehabilitation and one engineer expert on entrepreneurship and business. Separated meetings will be necessary to discuss specific technical or clinical issues, but periodic joint ones will be also programmed and particularly semester one-day workshops to share the project progress, to discuss issues and next steps for sharing the procedures and intermediate outcomes, and preparing deliverables and milestones. They will be the forum in which all participants will contribute their inputs and points of view to the project’s overall management.
TECHNICAL SOLUTION Regarding the design and development of both hardware and software of MyoArm, the common guidelines for medical devices will be followed: limited manufacturing costs, open for scalability, lower battery consumption, maximum usability and comfortability for patients (textile sensor easy to put on, smaller dimensions and weight of the device, motivating serious games) and therapists (automatic and simple reports of patients’ progress, selection of more useful information about muscle function, prognosis indexes based on machine learning), regulatory requirements, low risk level of hazards and maintenance avoiding short term obsolete electronic components or software platforms, etc.


CLINICAL EVALUATION Participants will be asked to answer questionnaires evaluating the impact of the experienced motor impairment fatigue on daily life activities (Modified Fatigue Impact Scale) [Larson et al., 2020], the Spanish version of Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire for functional impact assessment [Hervás et al., 2006] and the perceived quality of life (WHOQOL-BREF questionnaire). Motor function of all participants will be evaluated by a physician to evaluate the motor impairment and to evaluate motor strength and fatigue using clinical tests (Medical Research Council scale) [González-Seguel et al. 2019]. Then, an estimation of muscle mass and body composition will be conducted using ultrasound and bio-impedance, respectively. Instrumental evaluation of motor strength and fatigue will be conducted on upper limbs. Hand grip strength will be measured using hand-held dynamometry [Vanpee et al., 2011], handgrip dynamometry [Vanpee et al., 2014], and isokinetic dynamometry [Baldwin et al., 2012]. Muscle strength is considered the maximal isometric torque during 5s, in 3 consecutive trials . Muscle fatigue is evaluated using the JAMAR grip test during 12 repeated contractions of 30s. A fatigue index is calculated as the ratio between average of last and the first three recordings [Karatrantou, 2019]. Changes in muscle mass and body composition via ultrasound [Formenti et al., 2019] and bio-impedance [Wilhelmus et al., 2018], respectively. All patients will perform isometric tasks depending on the motor impairment (wrist, elbow or shoulder flexion and extension, forearm supination and pronation, and shoulder rotations and hand gripping) and non-isometric tasks (concentric and eccentric exercise, reaching task in 2D and 3D) in previously mentioned actions.

Publications
Shirzadi, M., Rojas-Martínez, M., Alonso, J. F., Serna, L. Y., Chaler, J., Mañanas, M. A., & Marateb, H. R. (2024). AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification—Decoding Lateral Epicondylitis in Forearm Muscles. Diagnostics, 14(20), 2255. doi: /10.3390/diagnostics14202255 . Epub ahead of print. PMID: 39451578.

In this study, the authors used Innovative algorithms for wearable devices and garments that are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. They analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. Authors discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. In their study researchers leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction.

Shirzadi, M., Marateb, H. R., Rojas-Martínez, M., Mansourian, M., Botter, A., Vieira dos Anjos, F., Vieira, M.V & Mañanas, M. A. A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs. Frontiers in physiology. 2023 14, 1098225. doi: https://doi.org/10.3389/fphys.2023.1098225.

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

Shirzadi, M., Marateb, H. R., McGill, K. C., Muceli, S., Mañanas, M. A., & Farina, D An accurate and real-time method for resolving superimposed action potentials in multiunit recordings. IEEE Transactions on Biomedical Engineering, 70(1), 378-389. doi: 10.1109/TBME.2022.3192119 . Epub 2023 Aug 27. PMID: 35862323.

Spike sorting of muscular and neural recordings requires separating action potentials that overlap in time (superimposed action potentials (APs)). We propose a new algorithm for resolving superimposed action potentials, and we test it on intramuscular EMG (iEMG) and intracortical recordings. Methods : Discrete-time shifts of the involved APs are first selected based on a heuristic extension of the peel-off algorithm. Then, the time shifts that provide the minimal residual Euclidean norm are identified (Discrete Brute force Correlation (DBC)). The optimal continuous-time shifts are then estimated (High-Resolution BC (HRBC)). In Fusion HRBC (FHRBC), two other cost functions are used. A parallel implementation of the DBC and HRBC algorithms was developed. The performance of the algorithms was assessed on 11,000 simulated iEMG and 14,000 neural recording superpositions, including two to eight APs, and eight experimental iEMG signals containing four to eleven active motor units. The performance of the proposed algorithms was compared with that of the Branch-and-Bound (BB) algorithm using the Rank-Product (RP) method in terms of accuracy and efficiency. Results : The average accuracy of the DBC, HRBC and FHRBC methods on the entire simulated datasets was 92.16±17.70, 93.65±16.89, and 94.90±15.15 (%). The DBC algorithm outperformed the other algorithms based on the RP method. The average accuracy and running time of the DBC algorithm on 10.5 ms superimposed spikes of the experimental signals were 92.1±21.7 (%) and 2.3±15.3 (ms). Conclusion and Significance : The proposed algorithm is promising for real-time neural decoding, a central problem in neural and muscular decoding and interfacing.

Marateb, H. R., Rojas-Martínez, M., Zamani, S., Shirzadi, M., Koochekian, A., & Mañanas, M. A. The application of Industry 4.0 technologies for automated health monitoring and surveillance during pandemics and post-pandemic life. In Advanced Signal Processing for Industry 4.0, Volume 1: Evolution, communication protocols, and applications in manufacturing systems (pp. 6-1). Bristol, UK: IOP Publishing. 2023. doi:10.1088/978-0-7503-5247-5ch6 .

Industry 4.0 signifies a new step in the control and organization of the manufacturing value chain. It can provide better digital solutions for our day-to-day lives during pandemics and post-pandemic life, including better activity planning, global public health emergencies, and risk assessment. The Internet of Things (IoT), as a part of Industry 4.0, brings new opportunities in various applications, including smart healthcare and cities. The combination of an IoT system and artificial intelligence (AI) has the following capabilities in pandemic management: public security improvement, contact tracing to access public places, and automated diagnosis and post-pandemic prognosis. In this chapter, we discuss the contribution of Industry 4.0 (IoT, digitalization, Big Data, AI, and cloud-based computing) for automatic surveillance and health monitoring during pandemics and post-pandemic life. More than fifty papers published in 2020–2 were analyzed in this chapter.