Deep learning-enabled high-speed, multi-parameter diffuse optical tomography.

Significance: Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT. Aim: We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe. Approach: A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data. Results: Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by 12 % ± 40 % and 23 % ± 40 % , increased the spatial similarity by 17 % ± 17 % and 9 % ± 15 % , increased the anomaly contrast accuracy by 9 % ± 9 % ( µ a ), and reduced the crosstalk by 5 % ± 18 % and 7 % ± 11 % , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8 min to 0.02 s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms. Conclusions: There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.

Objective Assessment of Cognitive Workload in Surgery: A Systematic Review.

OBJECTIVE: To systematically review technologies that objectively measure CWL in surgery, assessing their psychometric and methodological characteristics. SUMMARY BACKGROUND DATA: Surgical tasks involving concurrent clinical decision-making and the safe application of technical and non-technical skills require a substantial cognitive demand and resource utilization. Cognitive overload leads to impaired clinical decision-making and performance decline. Assessing cognitive workload (CWL) could enable interventions to alleviate burden and improve patient safety. METHODS: Ovid MEDLINE, OVID Embase, the Cochrane Library and IEEE Xplore databases were searched from inception to August 2023. Full-text, peer-reviewed original studies in a population of surgeons, anesthesiologists or interventional radiologists were considered, with no publication date constraints. Study population, task paradigm, stressor, Cognitive Load Theory (CLT) domain, objective and subjective parameters, statistical analysis and results were extracted. Studies were assessed for a) definition of CWL, b) details of the clinical task paradigm, and c) objective CWL assessment tool. Assessment tools were evaluated using psychometric and methodological characteristics. RESULTS: 10790 studies were identified; 9004 were screened; 269 full studies were assessed for eligibility, of which 67 met inclusion criteria. The most widely used assessment modalities were autonomic (32 eye studies and 24 cardiac). Intrinsic workload (e.g. task complexity) and germane workload (effect of training or expertize) were the most prevalent designs investigated. CWL was not defined in 30 of 67 studies (44.8%). Sensitivity was greatest for neurophysiological instruments (100% EEG, 80% fNIRS); and across modalities accuracy increased with multi-sensor recordings. Specificity was limited to cardiac and ocular metrics, and was found to be sub-optimal (50% and 66.67%). Cardiac sensors were the least intrusive, with 54.2% of studies conducted in naturalistic clinical environments (higher ecological validity). CONCLUSION: Physiological metrics provide an accessible, objective assessment of CWL, but dependence on autonomic function negates selectivity and diagnosticity. Neurophysiological measures demonstrate favorable sensitivity, directly measuring brain activation as a correlate of cognitive state. Lacking an objective gold standard at present, we recommend the concurrent use of multimodal objective sensors and subjective tools for cross-validation. A theoretical and technical framework for objective assessment of CWL is required to overcome the heterogeneity of methodological reporting, data processing, and analysis.

Effect of the level of task abstraction on the transfer of knowledge from virtual environments in cognitive and motor tasks.

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.

System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.

Galvanic vestibular stimulation activates the parietal and temporal cortex in humans: A functional near-infrared spectroscopy (fNIRS) study.

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.

Fuzzy Synchronization of Chaotic Systems with Hidden Attractors.

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.

Optical neuroimaging and neurostimulation in surgical training and assessment: A state-of-the-art review.

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.

Optical imaging and spectroscopy for the study of the human brain: status report.

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.

Intraoperative Cerebral Hemodynamic Monitoring during Carotid Endarterectomy via Diffuse Correlation Spectroscopy and Near-Infrared Spectroscopy.

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.

Diffuse correlation spectroscopy blood flow monitoring for intraventricular hemorrhage vulnerability in extremely low gestational age newborns.

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.

Characterization of a Raspberry Pi as the Core for a Low-cost Multimodal EEG-fNIRS Platform.

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.

Validation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients.

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.

Visual gaze patterns reveal surgeons' ability to identify risk of bile duct injury during laparoscopic cholecystectomy.

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.

Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level.

Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifold-based solutions excel at data exploration, but there are infinite surfaces crossing the observations cloud 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 Δ c HbO 2 and reduced Δ c HbR hemoglobin species by means of radial basis functions (RBF). We evaluate the root mean square error (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 in an experimental dataset of surgical neuroergonomics. Results: Lowest interpolation RMSE was 1.26 e - 4 ± 1.32 e - 8 for Δ c HbO 2 [A.U.] and 4.30 e - 7 ± 2.50 e - 13 [A.U.] for Δ c HbR . Agreement with classical group analysis was JI = 0.89 ± 0.01 for Δ c HbO 2 . Agreement with PPI analysis was JI = 0.83 ± 0.07 for Δ c HbO 2 and JI = 0.77 ± 0.06 for Δ c HbR . IFM successfully decoded group differences [ANOVA: Δ cHbO 2 : F ( 2,117 ) = 3.07 ; p < 0.05 ; Δ c HbR : F ( 2,117 ) = 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 analysis of fNIRS datasets.

Association of Residents' Neural Signatures With Stress Resilience During Surgery.

Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC). Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. Design, Setting, and Participants: Cohort study conducted from July 2015 to September 2016 at the Imperial College Healthcare National Health Service Trust, England. One hundred two surgical residents (postgraduate year 1 and greater) were invited to participate, of which 33 agreed to partake. Exposures: Participants performed a laparoscopic suturing task under 2 conditions: self-paced (SP; without time-per-knot restrictions), and time pressure (TP; 2-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics (task progression score [arbitrary units], error score [millimeters], leak volume [milliliters], and knot tensile strength [newtons]). Based on the composite score, quartiles were computed reflecting performance stability (quartile 1 [Q1]) and decline (quartile 4 [Q4]). Changes in PFC oxygenated hemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Of the 33 participants, the median age was 33 years, the range was 29 to 56 years, and 27 were men (82%). The Q1 residents demonstrated task-induced increases in HbO2 across the bilateral ventrolateral PFC (VLPFC) and right dorsolateral PFC in the SP condition and in the VLPFC in the TP condition. In contrast, Q4 residents demonstrated decreases in HbO2 in both conditions. The magnitude of PFC activation (change in HbO2) was significantly greater in Q1 than Q4 across the bilateral VLPFC during both SP (mean [SD] left VLPFC: Q1, 0.44 [1.30] µM; Q4, -0.21 [2.05] µM; P < .001; right VLPFC: Q1, 0.46 [1.12] µM; Q4, -0.15 [2.14] µM; P < .001) and TP (mean [SD] left VLPFC: Q1, 0.44 [1.36] µM; Q4, -0.03 [1.83] µM; P = .001; right VLPFC: Q1, 0.49 [1.70] µM; Q4, -0.32 [2.00] µM; P < .001) conditions. There were no significant between-group differences in Surgical Task Load Index or heart rate in either condition. Conclusions and Relevance: Performance stability within TP is associated with sustained prefrontal activation indicative of preserved attention and concentration, whereas performance decline is associated with prefrontal deactivation that may represent task disengagement.

Gaze patterns hold key to unlocking successful search strategies and increasing polyp detection rate in colonoscopy.

BACKGROUND: The adenoma detection rate (ADR) is an important quality indicator in colonoscopy. The aim of this study was to evaluate the changes in visual gaze patterns (VGPs) with increasing polyp detection rate (PDR), a surrogate marker of ADR. METHODS: 18 endoscopists participated in the study. VGPs were measured using eye-tracking technology during the withdrawal phase of colonoscopy. VGPs were characterized using two analyses - screen and anatomy. Eye-tracking parameters were used to characterize performance, which was further substantiated using hidden Markov model (HMM) analysis. RESULTS: Subjects with higher PDRs spent more time viewing the outer ring of the 3 × 3 grid for both analyses (screen-based: r = 0.56, P = 0.02; anatomy: r = 0.62, P < 0.01). Fixation distribution to the "bottom U" of the screen in screen-based analysis was positively correlated with PDR (r = 0.62, P = 0.01). HMM demarcated the VGPs into three PDR groups. CONCLUSION: This study defined distinct VGPs that are associated with expert behavior. These data may allow introduction of visual gaze training within structured training programs, and have implications for adoption in higher-level assessment.

Temporal Stress in the Operating Room: Brain Engagement Promotes "Coping" and Disengagement Prompts "Choking".

OBJECTIVE: To investigate the impact of time pressure (TP) on prefrontal activation and technical performance in surgical residents during a laparoscopic suturing task. BACKGROUND: Neural mechanisms enabling surgeons to maintain performance and cope with operative stressors are unclear. The prefrontal cortex (PFC) is implicated due to its role in attention, concentration, and performance monitoring. METHODS: A total of 33 residents [Postgraduate Year (PGY)1-2 = 15, PGY3-4 = 8, and PGY5 = 10] performed a laparoscopic suturing task under "self-paced" (SP) and "TP" conditions (TP = maximum 2 minutes per knot). Subjective workload was quantified using the Surgical Task Load Index. PFC activation was inferred using optical neuroimaging. Technical skill was assessed using progression scores (au), error scores (mm), leak volumes (mL), and knot tensile strengths (N). RESULTS: TP led to greater perceived workload amongst all residents (mean Surgical Task Load Index score ±â€ŠSD: PGY1-2: SP = 160.3 ±â€Š24.8 vs TP = 202.1 ±â€Š45.4, P < 0.001; PGY3-4: SP = 123.0 ±â€Š52.0 vs TP = 172.5 ±â€Š43.1, P < 0.01; PGY5: SP = 105.8 ±â€Š55.3 vs TP = 159.1 ±â€Š63.1, P < 0.05). Amongst PGY1-2 and PGY3-4, deterioration in task progression, error scores and knot tensile strength (P < 0.05), and diminished PFC activation was observed under TP. In PGY5, TP resulted in inferior task progression and error scores (P < 0.05), but preservation of knot tensile strength. Furthermore, PGY5 exhibited less attenuation of PFC activation under TP, and greater activation than either PGY1-2 or PGY3-4 under both experimental conditions (P < 0.05). CONCLUSIONS: Senior residents cope better with temporal demands and exhibit greater technical performance stability under pressure, possibly due to sustained PFC activation and greater task engagement. Future work should seek to develop training strategies that recruit prefrontal resources, enhance task engagement, and improve performance under pressure.

Imperial College near infrared spectroscopy neuroimaging analysis framework.

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.