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Fulltext The Time Course of Dorsal and Rostral-Ventral Anterior Cingulate Cortex Activity in the Emotional Stroop Experiment Reveals Valence and Arousal Aberrant Modulation in Patients with Schizophrenia

Feroz FS, Leicht G, Rauh J, Mulert C

Brain Topogr, 2019 01;32(1):161-177.
PMID: 30288663 DOI: 10.1007/s10548-018-0677-0

This paper aims to investigate the temporal dynamics within the dorsal anterior cingulate cortex (dACC) and the rostral-ventral (rv) ACC during the interaction of emotional valence and arousal with cognitive control in patients with Schizophrenia (SZ). Although cognitive deficits in SZ are highly relevant and emotional disturbances are common, the temporal relationship of brain regions involved in the interaction of emotional and cognitive processing in SZ is yet to be determined. To address this issue, the reaction time (RT), event-related potential (ERP) and temporal dynamics of the dACC and rvACC activity were compared between SZ subjects and healthy controls (HC), using a modified emotional Stroop experiment (with factors namely congruence, arousal and valence). EEG was recorded with 64 channels and source localisation was performed using the sLORETA software package. We observed slower initial increase and lower peaks of time course activity within the dACC and rvACC in the SZ group. In this particular group, the dACC activity during late negativity was negatively correlated with a significantly higher RT in the high arousal conflict condition. In contrast to HC subjects, at the N450 window, there was no significant valence (ERP and rvACC ROI) modulation effect in the SZ subjects. Using high density EEG and source localisation, it was possible to distinguish various disturbances within the dACC and rvACC in patients with SZ, during emotion-cognition processing.
Fulltext Effect of Forearm Postures and Elbow Joint Angles on Elbow Flexion Torque and Mechanomyography in Neuromuscular Electrical Stimulation of the Biceps Brachii

Uwamahoro R, Sundaraj K, Feroz FS

Sensors (Basel), 2023 Sep 29;23(19).
PMID: 37836995 DOI: 10.3390/s23198165

Neuromuscular electrical stimulation plays a pivotal role in rehabilitating muscle function among individuals with neurological impairment. However, there remains uncertainty regarding whether the muscle's response to electrical excitation is affected by forearm posture, joint angle, or a combination of both factors. This study aimed to investigate the effects of forearm postures and elbow joint angles on the muscle torque and MMG signals. Measurements of the torque around the elbow and MMG of the biceps brachii (BB) muscle were conducted in 36 healthy subjects (age, 22.24 ± 2.94 years; height, 172 ± 0.5 cm; and weight, 67.01 ± 7.22 kg) using an in-house elbow flexion testbed and neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle was stimulated while the forearm was positioned in the neutral, pronation, or supination positions. The elbow was flexed at angles of 10°, 30°, 60°, and 90°. The study analyzed the impact of the forearm posture(s) and elbow joint angle(s) on the root-mean-square value of the torque (TQRMS). Subsequently, various MMG parameters, such as the root-mean-square value (MMGRMS), the mean power frequency (MMGMPF), and the median frequency (MMGMDF), were analyzed along the longitudinal, lateral, and transverse axes of the BB muscle fibers. The test-retest interclass correlation coefficient (ICC21) for the torque and MMG ranged from 0.522 to 0.828. Repeated-measure ANOVAs showed that the forearm posture and elbow flexion angle significantly influenced the TQRMS (p < 0.05). Similarly, the MMGRMS, MMGMPF, and MMGMDF showed significant differences among all the postures and angles (p < 0.05). However, the combined main effect of the forearm posture and elbow joint angle was insignificant along the longitudinal axis (p > 0.05). The study also found that the MMGRMS and TQRMS increased with increases in the joint angle from 10° to 60° and decreased at greater angles. However, during this investigation, the MMGMPF and MMGMDF exhibited a consistent decrease in response to increases in the joint angle for the lateral and transverse axes of the BB muscle. These findings suggest that the muscle contraction evoked by NMES may be influenced by the interplay between actin and myosin filaments, which are responsible for muscle contraction and are, in turn, influenced by the muscle length. Because restoring the function of limbs is a common goal in rehabilitation services, the use of MMG in the development of methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels is imperative.
Fulltext Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression

Uwamahoro R, Sundaraj K, Feroz FS

Front Rehabil Sci, 2025;6:1469797.
PMID: 40046456 DOI: 10.3389/fresc.2025.1469797

BACKGROUND: The assessment of limb joint torque is essential for understanding musculoskeletal system dynamics. Yet, the lack of direct muscle strength measurement techniques has prompted previous research to deploy joint torque estimation using machine learning models. These models often suffer from reduced estimation accuracies due to the presence of redundant and irrelevant information within the rapidly expanding complex biomedical datasets as well as suboptimal hyperparameters configurations.
METHODS: This study utilized a random forest regression (RFR) model to estimate elbow flexion torque using mechanomyography (MMG) signals recorded during electrical stimulation of the biceps brachii (BB) muscle in 36 right-handed healthy subjects. Given the significance of both feature engineering and hyperparameter tuning in optimizing RFR performance, this study proposes a hybrid method leveraging the General Learning Equilibrium Optimizer (GLEO) to identify most informative MMG features and tune RFR hyperparameters. The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals.
RESULTS: Experimental results showed that selected features and tuned hyperparameters demonstrated a significant improvement in root mean square error (RMSE), coefficient of determination (R2) and slope with values improving from 0.1330 to 0.1174, 0.7228 to 0.7853 and 0.6946 to 0.7414, respectively for the test dataset. Convergence analysis further revealed that the GLEO algorithm exhibited a superior learning capability compared to EO.
CONCLUSION: This study underscores the potential of the hybrid GLEO approach in selecting highly informative features and optimizing hyperparameters for machine learning models. These advancements are essential for evaluating muscle function and represent a significant advancement in musculoskeletal biomechanics research.
Fulltext Estimation of elbow flexion torque from anthropometric and NMES MMG variables using random forest regression

Uwamahoro R, Sundaraj K, Feroz FS

Sci Rep, 2025 Mar 07;15(1):8038.
PMID: 40055347 DOI: 10.1038/s41598-024-81504-w
The Time Course of Activity within the Dorsal and Rostral-Ventral Anterior Cingulate Cortex in the Emotional Stroop Task

Feroz FS, Leicht G, Steinmann S, Andreou C, Mulert C

Brain Topogr, 2017 Jan;30(1):30-45.
PMID: 27659288 DOI: 10.1007/s10548-016-0521-3

Growing evidence from neuroimaging studies suggest that emotional and cognitive processes are interrelated. Anatomical key structures in this context are the dorsal and rostral-ventral anterior cingulate cortex (dACC and rvACC). However, up to now, the time course of activations within these regions during emotion-cognition interactions has not been disentangled. In the present study, we used event-related potentials (ERP) and standardized low-resolution electromagnetic tomography (sLORETA) region of interest (ROI) source localization analyses to explore the time course of neural activations within the dACC and rvACC using a modified emotional Stroop paradigm. ERP components related to Stroop conflict (N200, N450 and late negativity) were analyzed. The time course of brain activations in the dACC and rvACC was strikingly different with more pronounced initial responses in the rvACC followed by increased dACC activity mainly at the late negativity window. Moreover, emotional valence modulated the earlier N450 stage within the rvACC region with higher neural activations in the positive compared to the negative and neutral conditions. Emotional arousal modulated the late negativity stage; firstly in the significant arousal × congruence ERP effect and then the significant higher current density in the low arousal condition within the dACC. Using sLORETA source localization, substantial differences in the activation time courses in the dACC and rvACC could be found during the emotional Stroop task. We suggest that during late negativity, within the dACC, emotional arousal modulated the processing of response conflict, reflected in the correlation between the ex-Gaussian µ and the current density in the dACC.