Initial-Dip Existence and Estimation in Relation to DPF and Data Drift

Motor cortex hemodynamic response to goal-oriented and non-goal-oriented tasks in healthy subjects

Background

Motor disorders are one of the world's major scourges, and neuromotor rehabilitation is paramount for prevention and monitoring plans. In this scenario, exercises and motor tasks to be performed by patients are crucial to follow and assess treatments' progression and efficacy. Nowadays, in clinical environments, quantitative assessment of motor cortex activities during task execution is rare, due to the bulkiness of instrumentation and the need for immobility during measurements [e.g., functional magnetic resonance imaging (MRI)]. Functional near-infrared spectroscopy (fNIRS) can contribute to a better understanding of how neuromotor processes work by measuring motor cortex activity non-invasively in freely moving subjects.

Aim

Exploit fNIRS to measure functional activation of the motor cortex area during arm-raising actions.

Design

All subjects performed three different upper limbs motor tasks: arm raising (non-goal-oriented), arm raising and grasping (goal oriented), and assisted arm raising (passive task). Each task was repeated ten times. The block design for each task was divided into 5 seconds of baseline, 5 seconds of activity, and 15 seconds of recovery.

Population

Sixteen healthy subjects (11 males and 5 females) with an average (+/- standard deviation) of 37.9 (+/- 13.0) years old.

Methods

Cerebral hemodynamic responses have been recorded in two locations, motor cortex (activation area) and prefrontal cortex (control location) exploiting commercial time-domain fNIRS devices. Haemodynamic signals were analyzed, separating the brain cortex hemodynamic response from extracerebral hemodynamic variations.

Results

The hemodynamic response was recorded in the cortical motor area for goal-oriented and not-goaloriented tasks, while no response was noticed in the control location (prefrontal cortex position).

Conclusions

This study provides a basis for canonical upper limb motor cortex activations that can be potentially compared to pathological cerebral responses in patients. It also highlights the potential use of TD-fNIRS to study goal-oriented versus non-goaloriented motor tasks. Impact: the findings of this study may have implications for clinical rehabilitation by providing a better understanding of the neural mechanisms underlying goal-oriented versus non-goal-oriented motor tasks. This may lead to more effective rehabilitation strategies for individuals with motor disorders and a more effective diagnosis of motor dysfunction supported by objective and quantitative neurophysiological readings.

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

Enhanced design matrix for task-related fMRI data analysis

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.

Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study

At vital moments in professional soccer matches, penalties were often missed. Psychological factors, such as anxiety and pressure, are among the critical causes of the mistakes, commonly known as choking under pressure. Nevertheless, the factors have not been fully explored. In this study, we used functional near-infrared spectroscopy (fNIRS) to investigate the influence of the brain on this process. An in-situ study was set-up (N = 22), in which each participant took 15 penalties under three different pressure conditions: without a goalkeeper, with an amiable goalkeeper, and with a competitive goalkeeper. Both experienced and inexperienced soccer players were recruited, and the brain activation was compared across groups. Besides, fNIRS activation was compared between sessions that participants felt anxious against sessions without anxiety report, and between penalty-scoring and -missing sessions. The results show that the task-relevant brain region, the motor cortex, was more activated when players were not experiencing performance anxiety. The activation of task-irrelevant areas was shown to be related to players experiencing anxiety and missing penalties, especially the prefrontal cortex (PFC). More particularly, an overall higher activation of the PFC and an increase of PFC lateral asymmetry were related to anxious players and missed penalties, which can be caused by players' worries about the consequences of scoring or missing the penalty kicks. When experienced players were feeling anxious, their left temporal cortex activation increased, which could be an indication that experienced overthink the situation and neglect their automated skills. Besides, the left temporal cortex activation is higher when inexperienced players succeeded to score a penalty. Overall, the results of this study are in line with the neural efficiency theory and demonstrate the feasibility and ecological validity to detect neurological clues relevant to anxiety and performance from fNIRS recordings in the field.

Approach to optimize 3-dimensional brain functional activation image with high resolution: a study on functional near-infrared spectroscopy

In this study, 3-dimensional (3-D) enhanced brain-function-map generation and estimation methodology is presented. Optical signals were modelled in the form of numerical optimization problem to infer the best existing waveform of canonical hemodynamic response function. Inter-channel activity patterns were also estimated. The estimation of activation of inter-channel gap depends on the minimization of generalized cross-validation. 3-D brain activation maps were produced through inverse discrete cosine transform. The proposed algorithm acquired significant results for 3-D functional maps with high resolution, in comparison with that of 2-D functional t-maps. A comprehensive analysis by exhibiting images corresponding to several layers has also been appended.