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Fulltext Comparative study on interaction of form and motion processing streams by applying two different classifiers in mechanism for recognition of biological movement

Yousefi B, Loo CK

ScientificWorldJournal, 2014;2014:723213.
PMID: 25276860 DOI: 10.1155/2014/723213

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility.
Fulltext Development of biological movement recognition by interaction between active basis model and fuzzy optical flow division

Yousefi B, Loo CK

ScientificWorldJournal, 2014;2014:238234.
PMID: 24883361 DOI: 10.1155/2014/238234

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.
Fulltext A generalized quantum-inspired decision making model for intelligent agent

Hu Y, Loo CK

ScientificWorldJournal, 2014;2014:240983.
PMID: 24778580 DOI: 10.1155/2014/240983

A novel decision making for intelligent agent using quantum-inspired approach is proposed. A formal, generalized solution to the problem is given. Mathematically, the proposed model is capable of modeling higher dimensional decision problems than previous researches. Four experiments are conducted, and both empirical experiments results and proposed model's experiment results are given for each experiment. Experiments showed that the results of proposed model agree with empirical results perfectly. The proposed model provides a new direction for researcher to resolve cognitive basis in designing intelligent agent.
Fulltext View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid

Dawood F, Loo CK

PLoS One, 2016;11(3):e0152003.
PMID: 26998923 DOI: 10.1371/journal.pone.0152003

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.
Developmental Approach for Behavior Learning Using Primitive Motion Skills

Dawood F, Loo CK

Int J Neural Syst, 2018 May;28(4):1750038.
PMID: 29022403 DOI: 10.1142/S0129065717500381

Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration. In designing such learning system, several key issues will be addressed: automatic segmentation of the observed actions into motion primitives using raw images acquired from the camera without requiring any kinematic model; incremental learning of spatio-temporal motion sequences to dynamically generates a topological structure in a self-stabilizing manner; organization of the learned data for easy and efficient retrieval using a dynamic associative memory; and utilizing segmented motion primitives to generate complex behavior by the combining these motion primitives. In our experiment, the self-posture is acquired through observing the image of its own body posture while performing the action in front of a mirror through body babbling. The complete architecture was evaluated by simulation and real robot experiments performed on DARwIn-OP humanoid robot.
Fulltext A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Tahir GA, Loo CK

Healthcare (Basel), 2021 Dec 03;9(12).
PMID: 34946400 DOI: 10.3390/healthcare9121676

Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.
Explainable deep learning ensemble for food image analysis on edge devices

Tahir GA, Loo CK

Comput Biol Med, 2021 12;139:104972.
PMID: 34749093 DOI: 10.1016/j.compbiomed.2021.104972

Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges such as inter and intraclass variations alongside the practical applications of smart glasses, wearable cameras, and mobile devices require resource-efficient food recognition models with high classification performance. Furthermore, explainable AI is also crucial in health-related domains as it characterizes model performance, enhancing its transparency and objectivity. Our proposed architecture attempts to address these challenges by drawing on the strengths of the transfer learning technique upon initializing MobiletNetV3 with weights from a pre-trained model of ImageNet. The MobileNetV3 achieves superior performance using the squeeze and excitation strategy, providing unequal weight to different input channels and contrasting equal weights in other variants. Despite being fast and efficient, there is a high possibility for it to be stuck in the local optima like other deep neural networks, reducing the desired classification performance of the model. Thus, we overcome this issue by applying the snapshot ensemble approach as it enables the M model in a single training process without any increase in the required training time. As a result, each snapshot in the ensemble visits different local minima before converging to the final solution which enhances recognition performance. On overcoming the challenge of explainability, we argue that explanations cannot be monolithic, since each stakeholder perceive the results', explanations based on different objectives and aims. Thus, we proposed a user-centered explainable artificial intelligence (AI) framework to increase the trust of the involved parties by inferencing and rationalizing the results according to needs and user profile. Our framework is comprehensive in terms of a dietary assessment app as it detects Food/Non-Food, food categories, and ingredients. Experimental results on the standard food benchmarks and newly contributed Malaysian food dataset for ingredient detection demonstrated superior performance on an integrated set of measures over other methodologies.
Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled

Jeyabalan V, Samraj A, Loo CK

Comput Methods Biomech Biomed Engin, 2010 Oct;13(5):617-23.
PMID: 20336561 DOI: 10.1080/10255840903405678

Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.
Biologically inspired face recognition: toward pose-invariance

Wi NT, Loo CK, Chockalingam L

Int J Neural Syst, 2012 Dec;22(6):1250029.
PMID: 23186278 DOI: 10.1142/S0129065712500293

A small change in image will cause a dramatic change in signals. Visual system is required to be able to ignore these changes, yet specific enough to perform recognition. This work intends to provide biological-backed insights into 2D translation and scaling invariance and 3D pose-invariance without imposing strain on memory and with biological justification. The model can be divided into lower and higher visual stages. Lower visual stage models the visual pathway from retina to the striate cortex (V1), whereas the modeling of higher visual stage is mainly based on current psychophysical evidences.
Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure

Loo CK, Rajeswari M, Rao MV

IEEE Trans Neural Netw, 2004 Nov;15(6):1378-95.
PMID: 15565767

This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models.
A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Masuyama N, Loo CK, Wermter S

Int J Neural Syst, 2019 Jun;29(5):1850052.
PMID: 30764724 DOI: 10.1142/S0129065718500521

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.
Kernel Bayesian ART and ARTMAP

Masuyama N, Loo CK, Dawood F

Neural Netw, 2018 Feb;98:76-86.
PMID: 29202265 DOI: 10.1016/j.neunet.2017.11.003

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively.
Fulltext SLAMM: Visual monocular SLAM with continuous mapping using multiple maps

Daoud HA, Md Sabri AQ, Loo CK, Mansoor AM

PLoS One, 2018;13(4):e0195878.
PMID: 29702697 DOI: 10.1371/journal.pone.0195878

This paper presents the concept of Simultaneous Localization and Multi-Mapping (SLAMM). It is a system that ensures continuous mapping and information preservation despite failures in tracking due to corrupted frames or sensor's malfunction; making it suitable for real-world applications. It works with single or multiple robots. In a single robot scenario the algorithm generates a new map at the time of tracking failure, and later it merges maps at the event of loop closure. Similarly, maps generated from multiple robots are merged without prior knowledge of their relative poses; which makes this algorithm flexible. The system works in real time at frame-rate speed. The proposed approach was tested on the KITTI and TUM RGB-D public datasets and it showed superior results compared to the state-of-the-arts in calibrated visual monocular keyframe-based SLAM. The mean tracking time is around 22 milliseconds. The initialization is twice as fast as it is in ORB-SLAM, and the retrieved map can reach up to 90 percent more in terms of information preservation depending on tracking loss and loop closure events. For the benefit of the community, the source code along with a framework to be run with Bebop drone are made available at https://github.com/hdaoud/ORBSLAMM.
Fulltext Optimizing echo state networks for continuous gesture recognition in mobile devices: A comparative study

Yadav A, Pasupa K, Loo CK, Liu X

Heliyon, 2024 Mar 15;10(5):e27108.
PMID: 38562498 DOI: 10.1016/j.heliyon.2024.e27108

Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.
Fulltext A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

Wu Z, Loo CK, Obaidellah U, Pasupa K

Heliyon, 2023 Aug;9(8):e18771.
PMID: 37636411 DOI: 10.1016/j.heliyon.2023.e18771

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.
Fulltext What influences patients' acceptance of a blood pressure telemonitoring service in primary care? A qualitative study

Abdullah A, Liew SM, Hanafi NS, Ng CJ, Lai PS, Chia YC, et al.

Patient Prefer Adherence, 2016;10:99-106.
PMID: 26869773 DOI: 10.2147/PPA.S94687

BACKGROUND: Telemonitoring of home blood pressure (BP) is found to have a positive effect on BP control. Delivering a BP telemonitoring service in primary care offers primary care physicians an innovative approach toward management of their patients with hypertension. However, little is known about patients' acceptance of such service in routine clinical care.
OBJECTIVE: This study aimed to explore patients' acceptance of a BP telemonitoring service delivered in primary care based on the technology acceptance model (TAM).
METHODS: A qualitative study design was used. Primary care patients with uncontrolled office BP who fulfilled the inclusion criteria were enrolled into a BP telemonitoring service offered between the period August 2012 and September 2012. This service was delivered at an urban primary care clinic in Kuala Lumpur, Malaysia. Twenty patients used the BP telemonitoring service. Of these, 17 patients consented to share their views and experiences through five in-depth interviews and two focus group discussions. An interview guide was developed based on the TAM. The interviews were audio-recorded and transcribed verbatim. Thematic analysis was used for analysis.
RESULTS: Patients found the BP telemonitoring service easy to use but struggled with the perceived usefulness of doing so. They expressed confusion in making sense of the monitored home BP readings. They often thought about the implications of these readings to their hypertension management and overall health. Patients wanted more feedback from their doctors and suggested improvement to the BP telemonitoring functionalities to improve interactions. Patients cited being involved in research as the main reason for their intention to use the service. They felt that patients with limited experience with the internet and information technology, who worked out of town, or who had an outdoor hobby would not be able to benefit from such a service.
CONCLUSION: Patients found BP telemonitoring service in primary care easy to use but needed help to interpret the meanings of monitored BP readings. Implementations of BP telemonitoring service must tackle these issues to maximize the patients' acceptance of a BP telemonitoring service.
Fulltext MyDietCam: Development and usability study of a food recognition integrated dietary monitoring smartphone application

Kong NA, Moy FM, Ong SH, Tahir GA, Loo CK

Digit Health, 2023;9:20552076221149320.
PMID: 36644664 DOI: 10.1177/20552076221149320

BACKGROUND: Diet monitoring has been linked with improved eating habits and positive health outcomes such as prevention of obesity. However, this is often unsustainable as traditional methods place a high burden on both participants and researchers through pen and paper recordings and manual nutrient coding respectively. The digitisation of dietary monitoring has greatly reduced these barriers. This paper proposes a diet application with a novel food recognition feature with a usability study conducted in the real world.
METHODS: This study describes the development of a mobile diet application (MyDietCam) targeted at healthy Malaysian adults. Focus group discussions (FGD) were carried out among dietitians and potential users to determine ideal features in a diet application. Thirty participants were recruited from a local university to log their meals through MyDietCam for six days and submit the Malay mHealth Application Usability Questionnaire (M-MAUQ) at the end of the study.
RESULTS: The findings from the FGD led to the implementation of the main features: individualised recommendations, food logging through food recognition to reduce steps for data entry and provide detailed nutrient analyses through visuals. An average overall usability score of 5.13 out of a maximum of seven was reported from the M-MAUQ which is considered acceptable.
CONCLUSION: The development of a local (Malaysian) mobile diet application with acceptable usability may be helpful in sustaining the diet monitoring habit to improve health outcomes. Future work should focus on improving the issues raised before testing the effectiveness of the application for improving health outcomes.
Fulltext The accuracy of international and national fetal growth charts in detecting small-for-gestational-age infants using the Lambda-Mu-Sigma method

Saw SN, Lim MC, Liew CN, Ahmad Kamar A, Sulaiman S, Saaid R, et al.

Front Surg, 2023;10:1123948.
PMID: 37114151 DOI: 10.3389/fsurg.2023.1123948

OBJECTIVE: To construct a national fetal growth chart using retrospective data and compared its diagnostic accuracy in predicting SGA at birth with existing international growth charts.
METHOD: This is a retrospective study where datasets from May 2011 to Apr 2020 were extracted to construct the fetal growth chart using the Lambda-Mu-Sigma method. SGA is defined as birth weight <10th centile. The local growth chart's diagnostic accuracy in detecting SGA at birth was evaluated using datasets from May 2020 to Apr 2021 and was compared with the WHO, Hadlock, and INTERGROWTH-21st charts. Balanced accuracy, sensitivity, and specificity were reported.
RESULTS: A total of 68,897 scans were collected and five biometric growth charts were constructed. Our national growth chart achieved an accuracy of 69% and a sensitivity of 42% in identifying SGA at birth. The WHO chart showed similar diagnostic performance as our national growth chart, followed by the Hadlock (67% accuracy and 38% sensitivity) and INTERGROWTH-21st (57% accuracy and 19% sensitivity). The specificities for all charts were 95-96%. All growth charts showed higher accuracy in the third trimester, with an improvement of 8-16%, as compared to that in the second trimester.
CONCLUSION: Using the Hadlock and INTERGROWTH-21st chart in the Malaysian population may results in misdiagnose of SGA. Our population local chart has slightly higher accuracy in predicting preterm SGA in the second trimester which can enable earlier intervention for babies who are detected as SGA. All growth charts' diagnostic accuracies were poor in the second trimester, suggesting the need of improvising alternative techniques for early detection of SGA to improve fetus outcomes.
Electroconvulsive Therapy With Brain Cyst: A Simulation Study

Ahmad Bakir A, Martin DM, Alduraywish A, Dokos S, Loo CK

J ECT, 2024 Dec 01;40(4):277-285.
PMID: 39024187 DOI: 10.1097/YCT.0000000000001045

INTRODUCTION: Electroconvulsive therapy (ECT) is effective in treating severe depression and other neuropsychiatric disorders, but how the presence of an anatomical anomaly affects the electrical pathways between the electrodes remains unclear. We investigate the difference in electric field (E-field) distribution during ECT in the brain of a patient with an arachnoid cyst relative to hypothetical condition where the cyst was not present.
METHODS: Magnetic resonance imaging scans of the head of a patient with a large left frontal cyst were segmented to construct a finite element model to study the E-field distribution during ECT. Five electrode configurations were investigated: right unilateral, left unilateral, bifrontal, and bitemporal and left anterior right temporal. The E-field distributions for all montages were compared with a hypothetical condition where brain tissue and electrical conductivity from the right frontal region was mirrored across the longitudinal fissure into the cyst.
RESULTS: Differences in mean E-field and 90th percentile E-fields were mainly observed in brain regions closest to the cyst including the left inferior frontal gyrus and left middle frontal gyrus. This trend was most pronounced in montages where the electrodes were closest to the cyst such as left unilateral and bitemporal.
CONCLUSION: The presence of a highly conductive cyst close to the ECT electrode tended to attract current into the cyst region, altering current pathways, with potential implications for therapeutic efficacy and safety. Placing electrodes farther away from the cyst is likely to minimize any effects on the E-field distribution and potentially clinical outcomes.
Fulltext Effects of modifying the electrode placement and pulse width on cognitive side effects with unilateral ECT: A pilot randomised controlled study with computational modelling

Martin DM, Bakir AA, Lin F, Francis-Taylor R, Alduraywish A, Bai S, et al.

Brain Stimul, 2021 10 06;14(6):1489-1497.
PMID: 34626843 DOI: 10.1016/j.brs.2021.09.014

BACKGROUND: The electrode placement and pulse width for electroconvulsive therapy (ECT) are important treatment parameters associated with ECT related retrograde memory side-effects. Modification of these parameters with right unilateral (RUL) ECT may have utility for further reducing these side-effects.
OBJECTIVE: This study explored use of the frontoparietal (FP) placement for reducing retrograde memory side effects with ECT. We hypothesised that superior retrograde memory outcomes would occur with FP compared to temporoparietal (TP) placement and with ultrabrief (UB: 0.3 ms) compared to brief pulse (BP: 1.0 ms) width ECT.
METHODS: In this randomised cross-over, double-blinded study, participants received a single treatment of BP TP, BP FP, UB TP and UB FP ECT. Neuropsychological testing was conducted prior to and immediately following each treatment. Computational modelling was conducted to explore associations between E-fields in regions-of-interest associated with memory.
RESULTS: Nine participants completed the study. The FP placement was not superior to TP for retrograde memory outcomes. For both electrode placements UB pulse width was associated with significantly better visual retrograde memory compared to BP (p

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