Publications
119 results found
Hernández-Román J, Montero-Hernández S, Vega R, et al., 2023, Galvanic vestibular stimulation activates the parietal and temporal cortex in humans: A functional near-infrared spectroscopy (fNIRS) study., Eur J Neurosci, Vol: 58, Pages: 2267-2277
Galvanic vestibular stimulation (GVS) helps stabilize subjects when balance and posture are compromised. This work aimed to define the cortical regions that GVS activates in normal subjects. We used functional near-infrared spectroscopy (fNIRS) to test the hypothesis that GVS activates similar cortical areas as a passive movement. We used transcranial current stimulation (cathode in the right mastoid process and anode in the FPz frontopolar point) of bipolar direct current (2 mA), false GVS (sham), vibration (neutral stimulus), and back and forth motion (positive control of vestibular movement) in 18 clinically healthy volunteers. Seventy-two brain scans were performed, applying a crossover-type experimental design. We measured the heart rate, blood pressure, body temperature, head capacitance, and resistance before and after the experiment. The haemodynamic changes of the cerebral cortex were recorded with an arrangement of 26 channels in four regions to perform an ROI-level analysis. The back-and-forth motion produced the most significant oxygenated haemoglobin (HbO2 ) increase. The response was similar for the GVS stimulus on the anterior and posterior parietal and right temporal regions. Sham and vibrational conditions did not produce significant changes ROI-wise. The results indicate that GVS produces a cortical activation coherent with displacement percept.
Goble M, Caddick V, Patel R, et al., 2023, Optical neuroimaging and neurostimulation in surgical training and assessment: a state-of-the-art review, Frontiers in Neuroergonomics, Vol: 4, Pages: 1-8, ISSN: 2673-6195
Introduction: Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to assess surgeons' brain function. The aim of this narrative review is to outline the effect of expertise, stress, surgical technology, and neurostimulation on surgeons' neural activation patterns, and highlight key progress areas required in surgical neuroergonomics to modulate training and performance.Methods: A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks.Results: Novice surgeons exhibit greater haemodynamic responses across the pre-frontal cortex than experts during simple surgical tasks, whilst expert surgical performance is characterized by relative prefrontal attenuation and upregulation of activation foci across other regions such as the supplementary motor area. The association between PFC activation and mental workload follows an inverted-U shaped curve, activation increasing then attenuating past a critical inflection point at which demands outstrip cognitive capacity Neuroimages are sensitive to the impact of laparoscopic and robotic tools on cognitive workload, helping inform the development of training programs which target neural learning curves. FNIRS differs in comparison to current tools to assess proficiency by depicting a cognitive state during surgery, enabling the development of cognitive benchmarks of expertise. Finally, neurostimulation using transcranial direct-current-stimulation may accelerate skill acquisition and enhance technical performance.Conclusion: FNIRS can inform the development of surgical training programs which modulate stress responses, cognitive learning curves, and motor skill performance. Improved data processing with machine learning offers the possibility of live feedback regarding surgeons' cognitive states during operative procedures.
Zaqueros-Martinez J, Rodriguez-Gomez G, Tlelo-Cuautle E, et al., 2023, Fuzzy synchronization of chaotic systems with hidden attractors, Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 25, ISSN: 1099-4300
Chaotic systems are hard to synchronize, and no general solution exists. The presence of hidden attractors makes finding a solution particularly elusive. Successful synchronization critically depends on the control strategy, which must be carefully chosen considering system features such as the presence of hidden attractors. We studied the feasibility of fuzzy control for synchronizing chaotic systems with hidden attractors and employed a special numerical integration method that takes advantage of the oscillatory characteristic of chaotic systems. We hypothesized that fuzzy synchronization and the chosen numerical integration method can successfully deal with this case of synchronization. We tested two synchronization schemes: complete synchronization, which leverages linearization, and projective synchronization, capitalizing on parallel distributed compensation (PDC). We applied the proposal to a set of known chaotic systems of integer order with hidden attractors. Our results indicated that fuzzy control strategies combined with the special numerical integration method are effective tools to synchronize chaotic systems with hidden attractors. In addition, for projective synchronization, we propose a new strategy to optimize error convergence. Furthermore, we tested and compared different Takagi-Sugeno (T-S) fuzzy models obtained by tensor product (TP) model transformation. We found an effect of the fuzzy model of the chaotic system on the synchronization performance.
Dale R, O’Sullivan TD, Howard S, et al., 2023, High-speed spatial parameter recovery using multi-distance frequency-domain diffuse optical spectroscopy, ISSN: 1605-7422
Frequency domain (FD) diffuse optical spectroscopy (DOS) can be used to recover absolute optical properties of biological tissue, providing valuable clinical feedback, including in diagnosis and monitoring of breast tumours. In this study, tomographic (3D) and topographic (2D) techniques for spatially-varying optical parameter recovery are presented, based on a multi-distance, handheld DOS probe. Processing pipelines and reconstruction quality are discussed and quantitatively compared, demonstrating the trade-offs between depth sensitivity, optical contrast, and computational speed. Together, the two techniques provide both depth sensitive real-time feedback, and high-resolution 3D reconstruction from a single set of measurements, enabling faster and more accurate clinical feedback.
del Rocío Hernández-Castañón V, Castillo-Ávila AA, Reyes-Meza V, et al., 2023, Effect of the level of task abstraction on the transfer of knowledge from virtual environments in cognitive and motor tasks, Frontiers in Behavioral Neuroscience, Vol: 17, ISSN: 1662-5153
Introduction: Virtual environments are increasingly being used for training. It is not fully understood what elements of virtual environments have the most impact and how the virtual training is integrated by the brain on the sought-after skill transference to the real environment. In virtual training, we analyzed how the task level of abstraction modulates the brain activity and the subsequent ability to execute it in the real environment and how this learning generalizes to other tasks. The training of a task under a low level of abstraction should lead to a higher transfer of skills in similar tasks, but the generalization of learning would be compromised, whereas a higher level of abstraction facilitates generalization of learning to different tasks but compromising specific effectiveness. Methods: A total of 25 participants were trained and subsequently evaluated on a cognitive and a motor task following four training regimes, considering real vs. virtual training and low vs. high task abstraction. Performance scores, cognitive load, and electroencephalography signals were recorded. Transfer of knowledge was assessed by comparing performance scores in the virtual vs. real environment. Results: The performance to transfer the trained skills showed higher scores in the same task under low abstraction, but the ability to generalize the trained skills was manifested by higher scores under high level of abstraction in agreement with our hypothesis. Spatiotemporal analysis of the electroencephalography revealed higher initial demands of brain resources which decreased as skills were acquired. Discussion: Our results suggest that task abstraction during virtual training influences how skills are assimilated at the brain level and modulates its manifestation at the behavioral level. We expect this research to provide supporting evidence to improve the design of virtual training tasks.
Re R, Contini L, Contini D, et al., 2023, Cerebral resting state oscillations study with TD fNIRS, ISSN: 0277-786X
This work aims to show the possibility to perform in-vivo acquisitions with high sampling rate (20 Hz) with Time Domain functional Near-Infrared Spectroscopy (TD fNIRS) for studying brain resting state oscillations. Based on numerical simulations, a protocol was designed for acquiring hemodynamics parameters on 13 healthy volunteers during normal and forced respiration. Both the experiments had a length of 15 minutes and during the forced respiration one, subjects were ask to breath at 5 breaths per minute (0.083 Hz) following a metronome. Systemic (UP) and cortical (DW) oxy- (O2Hb) and deoxy- (HHb) hemoglobin concentrations (absolute values) were successfully retrieved with a single measure on the frontal lobe. Temporal series and Power Spectral Density (PSD) were calculated for: physiological signals (electrocardiogram, breath signal, blood volume pulse, skin conductance and temperature), total counts at the two wavelengths (RED = 689.5±0.5 nm and IR=828.5±0.5 nm), counts in temporal gates for RED and IR, absolute values of O2Hb_UP, O2Hb_DW, HHb_UP and HHb_DW. Specific characteristic peaks were evaluated in the cardiac, respiratory, low, and very low frequency bands. The behavior among the subjects was uniform and differences between the two experiments were found.
Dale R, O'Sullivan TD, Howard S, et al., 2023, System Derived Spatial-Temporal CNN for High-Density fNIRS BCI, IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY, Vol: 4, Pages: 85-95
Ayaz H, Baker WB, Blaney G, et al., 2022, Optical imaging and spectroscopy for the study of the human brain: status report, Neurophotonics, Vol: 9, Pages: 1-65, ISSN: 2329-423X
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
Kaya K, Zavriyev AI, Orihuela-Espina F, et al., 2022, Intraoperative cerebral hemodynamic monitoring during carotid endarterectomy via diffuse correlation spectroscopy and near-infrared spectroscopy, Brain Sciences, Vol: 12, Pages: 1-17, ISSN: 2076-3425
Objective: This pilot study aims to show the feasibility of noninvasive and real-time cerebral hemodynamic monitoring during carotid endarterectomy (CEA) via diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS). Methods: Cerebral blood flow index (CBFi) was measured unilaterally in seven patients and bilaterally in seventeen patients via DCS. In fourteen patients, hemoglobin oxygenation changes were measured bilaterally and simultaneously via NIRS. Cerebral autoregulation (CAR) and cerebrovascular resistance (CVR) were estimated using CBFi and arterial blood pressure data. Further, compensatory responses to the ipsilateral hemisphere were investigated at different contralateral stenosis levels. Results: Clamping of carotid arteries caused a sharp increase of CVR (~70%) and a marked decrease of ipsilateral CBFi (57%). From the initial drop, we observed partial recovery in CBFi, an increase of blood volume, and a reduction in CVR in the ipsilateral hemisphere. There were no significant changes in compensatory responses between different contralateral stenosis levels as CAR was intact in both hemispheres throughout the CEA phase. A comparison between hemispheric CBFi showed lower ipsilateral levels during the CEA and post-CEA phases (p < 0.001, 0.03). Conclusion: DCS alone or combined with NIRS is a useful monitoring technique for real-time assessment of cerebral hemodynamic changes and allows individualized strategies to improve cerebral perfusion during CEA by identifying different hemodynamic metrics.
Sunwoo J, Zavriyev A, Kaya K, et al., 2022, Diffuse correlation spectroscopy blood flow monitoring for intraventricular hemorrhage vulnerability in extremely low gestational age newborns, Scientific Reports, Vol: 12, Pages: 1-12, ISSN: 2045-2322
In premature infants with an extremely low gestational age (ELGA, < 29 weeks GA), dysregulated changes in cerebral blood flow (CBF) are among the major pathogenic factors leading to germinal matrix/intraventricular hemorrhage (GM/IVH). Continuous monitoring of CBF can guide interventions to minimize the risk of brain injury, but there are no clinically standard techniques or tools for its measurement. We report the feasibility of the continuous monitoring of CBF, including measures of autoregulation, via diffuse correlation spectroscopy (DCS) in ELGA infants using CBF variability and correlation with scalp blood flow (SBF, served as a surrogate measure of systemic perturbations). In nineteen ELGA infants (with 9 cases of GM/IVH) monitored for 6–24 h between days 2–5 of life, we found a strong correlation between CBF and SBF in severe IVH (Grade III or IV) and IVH diagnosed within 72 h of life, while CBF variability alone was not associated with IVH. The proposed method is potentially useful at the bedside for the prompt assessment of cerebral autoregulation and early identification of infants vulnerable to GM/IVH.
Rivas JJ, Lara MDC, Castrejon L, et al., 2022, Multi-label and multimodal classifier for affective states recognition in virtual rehabilitation, IEEE Transactions on Affective Computing, Vol: 13, Pages: 1183-1194, ISSN: 1949-3045
Computational systems that process multiple affective states may benefit from explicitly considering the interaction between the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC)) of CCC with the FSNBC are over 0.940±0.045 ( mean±std.deviation ) for the four states. Relationships of mutual exclusion between engagement and all the other states and co-occurrences between pain and anxiety were detected and discussed.
Garcia-Mendoza J-L, Villasenor-Pineda L, Orihuela-Espina F, 2022, Risks of misinterpretation in the evaluation of Distant Supervision for Relation Extraction, PROCESAMIENTO DEL LENGUAJE NATURAL, Vol: 68, Pages: 71-83, ISSN: 1135-5948
Distant Supervision is frequently used for addressing Relation Extraction. The evaluation of Distant Supervision in Relation Extraction has been attempted through Precision-Recall curves and/or calculation of Precision at N elements. However, such evaluation is challenging because the labeling of the instances results from an automatic process that can introduce noise into the labels. Consequently, the labels are not necessarily correct, affecting the learning process and the interpretation of the evaluation results. Therefore, this research aims to show that the performance of the methods measured with the mentioned evaluation strategies varies significantly if the correct labels are used during the evaluation. Besides, based on the preceding, the current interpretation of the results of these measures is questioned. To this end, we manually labeled a subset of a well-known data set and evaluated the performance of 6 traditional Distant Supervision approaches. We demonstrate quantitative differences in the evaluation scores when considering manually versus automatically labeled subsets. Consequently, the ranking of performance among distant supervision methods is different with both labeled. Keywords: Relation Extraction. Distant Supervision evaluation. Precision-Recall curves. Precision at N.
Garcia-Mendoza J-L, Villasenor-Pineda L, Orihuela-Espina F, et al., 2022, An autoencoder-based representation for noise reduction in distant supervision of relation extraction, Journal of Intelligent and Fuzzy Systems, Vol: 42, Pages: 4523-4529, ISSN: 1064-1246
Distant Supervision is an approach that allows automatic labeling of instances. This approach has been used in Relation Extraction. Still, the main challenge of this task is handling instances with noisy labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). The approaches reported in the literature addressed this problem by employing noise-tolerant classifiers. However, if a noise reduction stage is introduced before the classification step, this increases the macro precision values. This paper proposes an Adversarial Autoencoders-based approach for obtaining a new representation that allows noise reduction in Distant Supervision. The representation obtained using Adversarial Autoencoders minimize the intra-cluster distance concerning pre-trained embeddings and classic Autoencoders. Experiments demonstrated that in the noise-reduced datasets, the macro precision values obtained over the original dataset are similar using fewer instances considering the same classifier. For example, in one of the noise-reduced datasets, the macro precision was improved approximately 2.32% using 77% of the original instances. This suggests the validity of using Adversarial Autoencoders to obtain well-suited representations for noise reduction. Also, the proposed approach maintains the macro precision values concerning the original dataset and reduces the total instances needed for classification.
Zaqueros-Martinez J, Rodriguez-Gomez G, Tlelo-Cuautle E, et al., 2022, Synchronization of Chaotic Electroencephalography (EEG) Signals, Studies in Big Data, Pages: 83-108
Synchronization of chaotic signals often considers a master-slave paradigm where a slave chaotic system is required to follow the master also chaotic. Most times in literature both systems are known, but synchronization to some unknown master has a potentially large range of applications, for example, EEG based authentication. We aim to test the feasibility of fuzzy control to systematically synchronize a chaotic EEG record. In this chapter, we study the suitability of two chaotic systems and the companion fuzzy control strategies under complete and projective synchronization to synchronize to EEG records. We used two public EEG datasets related to the genetic predisposition to alcoholism and with detecting emotions respectively. We present a comparative study among fuzzy control strategies for synchronization of chaotic systems to EEG records on selected datasets. As expected, we observed success and failures alike on the synchronization highlighting the difficulty in achieving this kind of synchronization, but we interpret this as advantageous for purposes of the suggested domain application. With successful synchronizations, we confirm that synchronization is feasible. With unsuccessful synchronizations, we illustrate that synchronization of chaotic systems does not follow a simple one-size-fits-all recipe and we attempt to gain insight for future research. The same chaotic system may succeed or fail depending on its companion type of synchronization and controller design.
García-Mendoza J-L, Villaseñor-Pineda L, Buscaldi D, et al., 2022, Evaluation of a new representation for noise reduction in Distant Supervision, 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Publisher: Springer Nature Switzerland, Pages: 101-113, ISSN: 0302-9743
Distant Supervision is a relation extraction approach that allows automatic labeling of a dataset. However, this labeling introduces noise in the labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). Noise in labels makes difficult the relation extraction task. This noise is precisely one of the main challenges of this task. Until now, the methods that incorporate a previous noise reduction step do not evaluate the performance of this step. This paper evaluates the noise reduction using a new representation obtained with autoencoders. In addition, it was incoporated more information to the input of the autoencoder proposed in the state-of-the-art to improve the representation over which the noise is reduced. Also, three methods were proposed to select the instances considered as real. As a result, it was obtained the highest values of the area under the ROC curves using the improved input combined with state-of-the-art anomaly detection methods. Moreover, the three proposed selection methods significantly improve the existing method in the literature.
Zaqueros-Martinez J, Rodríguez-Gómez G, Tlelo-Cuautle E, et al., 2022, Trigonometric polynomials methods to simulate oscillating chaotic systems, International Conference on Numerical Analysis and Applied Mathematics 2020 (ICNAAM-2020), Publisher: AIP Publishing, Pages: 1-4, ISSN: 0094-243X
Del Angel Arrieta F, Rojas Cisneros M, Rivas JJ, et al., 2021, Characterization of a raspberry Pi as the core for a low-cost multimodal EEG-fNIRS platform., 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Publisher: IEEE, Pages: 1288-1291, ISSN: 1557-170X
Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation.
Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, et al., 2021, Dealing with a missing sensor in a multilabel and multimodal automatic affective states recognition system, 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Publisher: IEEE, Pages: 1-8, ISSN: 2156-8103
Data from multiple sensors can boost the automatic recognition of multiple affective states in a multilabel and multimodal recognition system. At any time, the streaming from any of the contributing sensors can be missing. This work proposes a method for dealing with a missing sensor in a multilabel and multimodal automatic affective states recognition system. The proposed method, called Hot Deck using Conditional Probability Tables (HD-CPT), is incorporated into a multimodal affective state recognition system for compensating the loss of a sensor using the recorded historical information of the sensor and its interaction with the other available sensors. In this work, we consider a multilabel classifier, named Circular Classifier Chain, for the automatic recognition of four states: tiredness, anxiety, pain, and engagement; combined with a multimodal classifier based on three sensors: fingers pressure, hand movements, and facial expressions; which was adapted for coping with the problem of a missing sensor in a virtual rehabilitation platform for post-stroke patients. A dataset of five post-stroke patients who attended ten longitudinal rehabilitation sessions was used for the evaluation. The inclusion of HD-CPT compensated for the loss of one sensor with results above those obtained with only the remaining sensors available. HD-CPT prevents the system from collapsing when a sensor fails, providing continuity of operation with results that attenuate the loss of the sensor. The proposed method HD-CPT can provide robustness for the naturalistic everyday use of an affective states recognition system.
Borrego G, Morán AL, Meza V, et al., 2021, Key factors that influence the UX of a dual-player game for the cognitive stimulation and motor rehabilitation of older adults, Universal Access in the Information Society, Vol: 20, Pages: 767-783, ISSN: 1615-5289
In this work, the results of usability and user experience (UX) evaluation of a serious video game for the cognitive stimulation and motor rehabilitation of the upper limb of the elderly are presented. The serious game includes features that allow (1) performing cooperative therapy exercises between two patients, (2) remote session configuration therapy, and (3) monitoring/analyzing the sessions’ results by the therapist. An evaluation of the game with 16 older adults provides evidence about how the tool is perceived by participants, who embraced it as stimulating, useful, usable and even fun, and which impacts in therapy compliance and acceptability by the elderly. In addition, through an in depth analysis of the participants’ performance and observed emotions, as well as their self-report, we determined which engagement attributes are present in the game. Finally, we also found evidence that suggests that the participants’ skill level and the game difficulty level rather than just a good performance on the game are key factors that influence their enjoyment and frustration.
Sharma C, Singh H, Orihuela-Espina F, et al., 2021, Visual gaze patterns reveal surgeons' ability to identify risk of bile duct injury during laparoscopic cholecystectomy, HPB, Vol: 23, Pages: 715-722, ISSN: 1365-182X
BACKGROUND: Bile duct injury is a serious surgical complication of laparoscopic cholecystectomy. The aim of this study was to identify distinct visual gaze patterns associated with the prompt detection of bile duct injury risk during laparoscopic cholecystectomy. METHODS: Twenty-nine participants viewed a laparoscopic cholecystectomy that led to a serious bile duct injury ('BDI video') and an uneventful procedure ('control video') and reported when an error was perceived that could result in bile duct injury. Outcome parameters include fixation sequences on anatomical structures and eye tracking metrics. Surgeons were stratified into two groups based on performance and compared. RESULTS: The 'early detector' group displayed reduced common bile duct dwell time in the first half of the BDI video, as well as increased cystic duct dwell time and Calot's triangle glances count during Calot's triangle dissection in the control video. Machine learning based classification of fixation sequences demonstrated clear separability between early and late detector groups. CONCLUSION: There are discernible differences in gaze patterns associated with early recognition of impending bile duct injury. The results could be transitioned into real time and used as an intraoperative early warning system and in an educational setting to improve surgical safety and performance.
Wu K-C, Sunwoo J, Sheriff F, et al., 2021, Validation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients, Journal of Biomedical Optics, Vol: 26, Pages: 036008-1-036008-14, ISSN: 1083-3668
SIGNIFICANCE: Intracranial pressure (ICP), variability in perfusion, and resulting ischemia are leading causes of secondary brain injury in patients treated in the neurointensive care unit. Continuous, accurate monitoring of cerebral blood flow (CBF) and ICP guide intervention and ultimately reduce morbidity and mortality. Currently, only invasive tools are used to monitor patients at high risk for intracranial hypertension. AIM: Diffuse correlation spectroscopy (DCS), a noninvasive near-infrared optical technique, is emerging as a possible method for continuous monitoring of CBF and critical closing pressure (CrCP or zero-flow pressure), a parameter directly related to ICP. APPROACH: We optimized DCS hardware and algorithms for the quantification of CrCP. Toward its clinical translation, we validated the DCS estimates of cerebral blood flow index (CBFi) and CrCP in ischemic stroke patients with respect to simultaneously acquired transcranial Doppler ultrasound (TCD) cerebral blood flow velocity (CBFV) and CrCP. RESULTS: We found CrCP derived from DCS and TCD were highly linearly correlated (ipsilateral R2 = 0.77, p = 9 × 10 - 7; contralateral R2 = 0.83, p = 7 × 10 - 8). We found weaker correlations between CBFi and CBFV (ipsilateral R2 = 0.25, p = 0.03; contralateral R2 = 0.48, p = 1 × 10 - 3) probably due to the different vasculature measured. CONCLUSION: Our results suggest DCS is a valid alternative to TCD for continuous monitoring of CrCP.
Andrea B-M, Felipe O-E, Alberto R-GC, 2021, Ordering of functions according to multiple fuzzy criteria: application to denoising electroencephalography, Soft Computing, Vol: 25, Pages: 8573-8593, ISSN: 1432-7643
We introduce a new relation of order over functions according to multiple fuzzy criteria. Proof of the complied properties for relations of partial orders is given. Convergent and divergent validity of the new membership functions is established. Tolerance to noise of the relation of order is evaluated by corrupting synthetic prototypes and observing changes in the retrieved ordering. The effect of weighting strategies is evaluated in terms of Jaccard and XOR indices. The performance of the ordering algorithm is quantified in terms of richness of the resulting Hasse diagram. Applicability is demonstrated in the context of de-noising electroencephalographic (EEG) signals exemplified over two datasets and evaluated by classification wrapping.
Hernandez-Franco J, Orihuela-Espina F, Palafox L, et al., 2021, Remote central effects of botulinum toxin type A as adjuvant to intense occupational therapy in the early stage of stroke: A Type II fMRI randomised controlled trial, TOXINS Conference on Basic Science and Clinical Aspects of Botulinum and other Neurotoxins, Publisher: Elsevier, Pages: S33-S33, ISSN: 0041-0101
Introduction: Improvements in motor function following interventions incorporating botulinum toxin type A (BTX-A) remain controversial, with existing studies yielding contrasting results.1-3 The mechanisms underlying BTX-A remote central effects are still under investigation. It is hypothesized that the toxin administration strategy may play a role in producing such differing outcomes. We tested a strategy based on modulating muscle synergies.Aim: The aim of the study was to investigate the clinical and remote central effects of an occupational therapy intervention combined with adjunctive BTX-A compared to the same occupational therapy without the adjuvant application of the toxin.Methods: A two-group, parallel, pre-post, randomized controlled trial was performed. The clinical effects of occupational therapy when performed following BTX-A injections to disinhibit finger flexors (n=5) was compared to those of an equal dose of occupational therapy alone (n=6). Motor dexterity and function were assessed using the Fugl-Meyer Scale, Motor Index, Arm Activity Measure, 9-Hole Peg Test, and Box and Block Test, and differences were analysed using ANCOVA. Brain activity was examined using functional magnetic resonance imaging (fMRI), and between-group differences were analysed using contrast statistical parametric mapping.Results: Both groups started in statistically similar conditions. Both treatments provided significant clinical improvements compared to baseline. The total differences in change score on the Fugl-Meyer Scale and Motor Index were larger, though not significantly, in the toxin-treated group than in the control group (Figure). When the toxin is administered, activity in the brain is more localised and appears more in the right hemisphere in subjects in the toxin-treated group and more in the left in those in the control group.Conclusions: Functional improvements were observed in the toxin-treated group, but the effect size compared to the control group was to
Rojas-Cisneros M, Montero-Hernandez SA, Orihuela-Espina F, 2021, Analysis in the broader, medium, and narrow autism phenotypes using fNIRS
Characterizing the brain activity of autistic phenotypes (AP) could inform an endophenotypic understanding of ASD. We present possibly the first study comparing brain responses in AP using fNIRS. Syllabic stress may unveil a potential biomarker.
Orihuela-Espina F, Rojas-Cisneros M, Montero-Hernández SA, et al., 2021, Physics augmented classification of fNIRS signals, Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms, and Applications, Pages: 375-405, ISBN: 9780128201251
Background. Predictive classification favors performance over semantics. In traditional predictive classification pipelines, feature engineering is often oblivious to the underlying phenomena. Hypothesis. In applied domains, such as functional near infrared spectroscopy (fNIRS), the exploitation of physical knowledge may improve the discriminative quality of our observation set. Aim. Give exemplary evidence that intervening the physical observation process can augment classification. Methods. We manipulate the observation process in four ways independently. First, sampling and quantization are designed to enhance class-related contrast. Second, we show how selection of optical filters affects the cross-talk, in turn, affecting classification. Third, we regularize the inverse problem to maximize sensitivity to any gradient that would later support the classification. And fourth, we introduce a catalyst covariate during experiment design to exacerbate response differences. Results. For each of the proposed manipulations, we show that the performance of the classification exercise is altered in some way or another. Conclusions. Exploitation knowledge of physics even before acquisition can support classification, alleviating otherwise blind feature engineering. This can also enhance interpretability of the classification model.
Ávila-Sansores S-M, Rodríguez-Gómez G, Tachtsidis I, et al., 2020, Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level, Neurophotonics, Vol: 7, Pages: 045009-045009, ISSN: 2329-423X
Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist,but groupwise explorative analysis with classical solutions is often cumbersome. Manifoldbased solutions excel at data exploration, but there are infinite surfaces crossing the observationscloud of points.Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error.Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated ΔcHbO2 and reduced ΔcHbRhemoglobin species by means of radial basis functions (RBF). We evaluate the root mean squareerror (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability inan experimental dataset of surgical neuroergonomics.Results: Lowest interpolation RMSE was 1.26e − 4 1.32e − 8 for ΔcHbO2 [A.U.] and4.30e − 7 2.50e − 13 [A.U.] for ΔcHbR. Agreement with classical group analysis was JI ¼0.89 0.01 for ΔcHbO2. Agreement with PPI analysis was JI ¼ 0.83 0.07 for ΔcHbO2 andJI ¼ 0.77 0.06 for ΔcHbR. IFM successfully decoded group differences [ANOVA: ΔcHbO2:Fð2;117Þ ¼ 3.07; p < 0.05; ΔcHbR: Fð2;117Þ ¼ 3.35; p < 0.05].Conclusions: IFM provides a pragmatic solution to the problem of choosing the manifold associated to a cloud of points, facilitating the use of manifold-based solutions for the group analysisof fNIRS datasets.
Casillas-Figueroa R, Morán AL, Meza-Kubo V, et al., 2020, ReminiScentia: shaping olfactory interaction in a personal space for multisensory stimulation therapy, Personal and Ubiquitous Computing, ISSN: 1617-4909
Recently, multimodal interfaces are incorporating smell as an additional means of interaction. Devices called olfactory displays have been designed to improve applications with various objectives, such as notifying or alerting through scents, increasing immersion in virtual or augmented reality applications, or learning and enhancement of mental functions. Based on the potential of olfactory memory to evoke memories and emotions to benefit health and well-being, we propose ReminiScentia as an olfactory display to generate and deliver scents. This work presents an evaluation of the effectiveness of ReminiScentia in evoking brain responses similar to those generated by manually delivered scents. To achieve this, we monitored the hemodynamic responses during manual and ReminiScentia olfactory stimulation over the prefrontal cortex (PFC) by using a functional near-infrared spectroscopy (fNIRS) device in 33 healthy subjects. Among the results, it was found that when ReminiScentia was used to deliver the olfactory stimuli, there is no statistically significant difference in the magnitude of concentration changes of OxyHb in the PFC between manual deliver and ReminiScentia (Wilcoxon: p > 0.05). The effectiveness of the use of ReminiScentia has allowed us not only its application for the evocation of memories in a multisensory therapy but also to propose an olfactory interaction design space where olfactory stimuli are used to provide feedback or instructions in multisensory stimulation activities that could support the training of higher mental functions such as memory and learning in patients with cognitive disabilities.
Rivas JJ, Orihuela-Espina F, Palafox L, et al., 2020, Unobtrusive inference of affective states in virtual rehabilitation from upper limb motions: a feasibility study, IEEE Transactions on Affective Computing, Vol: 11, Pages: 470-481, ISSN: 1949-3045
Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength -relevant for rehabilitation-. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.
Zavriyev AI, Kaya K, Farzam P, et al., 2020, Diffuse correlation spectroscopy used to monitor cerebral blood flow during adult hypothermic circulatory arrests, Publisher: Cold Spring Harbor Laboratory
Real-time noninvasive monitoring of cerebral blood flow during surgery could improve the morbidity and mortality rates associated with hypothermic circulatory arrests (HCA) in adult cardiac patients. In this study, we used a combined frequency domain near-infrared spectroscopy (FDNIRS) and diffuse correlation spectroscopy (DCS) system to measure cerebral oxygen saturation (SO2) and an index of blood flow (CBFi) in 12 adults going under cardiac surgery with HCA. Our measurements revealed that a negligible amount of blood is delivered to the brain during HCA with retrograde cerebral perfusion (RCP), indistinguishable from HCA-only cases (CBFi drops of 91% ± 3% and 96% ± 2%, respectively) and that CBFi drops for both are significantly higher than drops during HCA with antegrade cerebral perfusion (ACP) (p = 0.003). We conclude that FDNIRS-DCS can be a powerful tool to optimize cerebral perfusion, and that RCP needs to be further examined to confirm its efficacy, or lack thereof.
Sunwoo J, Nair V, Steele T, et al., 2020, Assessment of cerebral autoregulation in extremely low gestational age newborns using diffuse correlation spectroscopy
We use diffuse correlation spectroscopy to safely quantify cerebral blood flow response to spontaneous fluctuations in autonomic and respiratory activities to help characterize the elevated risk of intraventricular hemorrhage in extremely premature newborns.
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