The search for the brain-basis of atypical development in human infants is challenging because the process of imaging and the generation of the MR signal itself relies on assumptions that reflect biophysical properties of the brain tissue. These assumptions are not inviolate, have been questioned by recent empirical evidence from high risk infant-sibling studies, and to date remain largely underexamined at the between-group level. In particular, I consider recent work showing that infants at High vs. Low familial risk (HR vs. LR, respectively) for developing Autism Spectrum Disorders (ASD) have atypical patterns of head movements during an MR scan that are functionally important-they are linked to future learning trajectories in toddlerhood. Addressing head movement issues in neuroimaging analyses in infant research as well as understanding the causes of these movements from a developmental perspective requires acknowledging the complexity of this endeavor. For example, head movement signatures in infants can interact with experimental task conditions (such as listening to language compared to sleeping), autism risk, and age. How can new knowledge about newborns' individual, subject-specific behavioral differences which may impact MR signal acquisition and statistical inference ignite critical thinking for the field of infant brain imaging across the spectrum of typical and atypical development? Early behavioral differences between HR and LR infant cohorts that are often examples of "artifactual" confounds in MR work provide insight into nascent neurobiological differences, including biophysical tissue properties and hemodynamic response variability, in these and related populations at risk for atypical development. Are these neurobiological drivers of atypical development? This work identifies important knowledge gaps and suggests guidelines at the leading edge of baby imaging science to transform our understanding of atypical brain development in humans. The precise study of the neurobiological underpinnings of atypical development in humans calls for approaches including quantitative MRI (qMRI) pulse sequences, multi-modal imaging (including DTI, MRS, as well as MEG), and infant-specific HRF shapes when modeling BOLD signal.

Objective: The aim of this work was to study the clinical and histopathology features and treatment outcome in retinoblastoma cases presenting at an older age (>6 years).

Design: This was a retrospective study. We recruited 48 retinoblastoma patients who were treated at our institute over 7 consecutive years and were older than 6 years at presentation.

Methods: Medical records were reviewed for data, including age at diagnosis, gender, laterality, family history, lag time, first symptom, misdiagnosis, clinical findings, grade and stage of disease at diagnosis, treatment, outcome, and follow-up status. Histopathology slides were reviewed and assessed for the presence of histopathological high-risk features (HRF) for metastasis. The main outcome measures were the frequency of atypical clinical features like hyphema, pseudohypopyon, glaucoma, cataract, vitreous hemorrhage, and phthisis, and misdiagnosis, prior intervention, stage of disease at presentation, and treatment outcome.

Results: In total, 48/610 (7.8%) patients were older than 6 years, with a median age of 7 years (range 6-31). Retinoblastoma was bilateral in 7 cases. The most common initial symptom was white reflex followed by a decrease in vision. The median lag time was 9 months. Fourteen cases (29.2%) were misdiagnosed, with endophthalmitis the most common misdiagnosis. Twenty-six (54%) patients had intraocular disease, 12 (25%) had locally advanced disease, and 10 (21%) had metastatic disease at presentation. Overall, 67% (14/21) of the eyes that were enucleated upfront for presumed intraocular disease had histopathological HRF. At last follow-up, 31/36 (86%) who were treated were alive and healthy, while 5 (14%) patients had disease progression.

Conclusions: This is the largest study of older age retinoblastoma and shows that it forms a significant percentage of retinoblastoma in developing countries, is misdiagnosed in one-third of cases, and may present at an advanced stage in 46% of cases.

Keywords: Misdiagnosis; Older age; Retinoblastoma.

The activities of hospital-replacing forms (HRF) of medical care are analyzed for the Samara and Tver regions. In the Samara region the number of HRF beds was 13.2 per 10,000 population, 9.3 of these at day-time hospitals of outpatient centers and 3.9 at hospitals. In the Tver region the number of day-time beds was 5.5 at hospitals and 0.97 at outpatient centers per 10,000 population. In the Samara region the estimated number of days of treatment at HRF was 475 per 1000 population, which is sufficient.

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling. Since HRF is influenced by non-neural factors, to date it has largely been considered as a confound or has been ignored in many analyses. However, underlying biophysics suggests that the HRF may contain meaningful correlates of neural activity, which might be unavailable through conventional fMRI metrics. Here, we estimated the HRF by performing deconvolution on resting-state fMRI data from a longitudinal sample of 25 healthy controls scanned twice and 44 adults with obsessive-compulsive disorder (OCD) before and after 4-weeks of intensive cognitive-behavioral therapy (CBT). HRF response height, time-to-peak and full-width at half-maximum (FWHM) in OCD were abnormal before treatment and normalized after treatment in regions including the caudate. Pre-treatment HRF predicted treatment outcome (OCD symptom reduction) with 86.4% accuracy, using machine learning. Pre-treatment HRF response height in the caudate head and time-to-peak in the caudate tail were top-predictors of treatment response. Time-to-peak in the caudate tail, a region not typically identified in OCD studies using conventional fMRI activation or connectivity measures, may carry novel importance. Additionally, pre-treatment response height in caudate head predicted post-treatment OCD severity (R = -0.48, P = 0.001), and was associated with treatment-related OCD severity changes (R = -0.44, P = 0.0028), underscoring its relevance. With HRF being a reliable marker sensitive to brain function, OCD pathology, and intervention-related changes, these results could guide future studies towards novel discoveries not possible through conventional fMRI approaches like standard BOLD activation or connectivity.

Keywords: CBT; Cognitive-behavioral therapy; Functional magnetic resonance imaging; HRF; Hemodynamic response function; Machine learning; OCD; Obsessive-compulsive disorder; fMRI.

Finite impulse response (FIR) filters are considered the least constrained option for the blind estimation of the hemodynamic response function (HRF). However, they have a tendency to yield unstable solutions in the case of short-events sequences. There are solutions based on regularization, e.g. smooth FIR (sFIR), but at the cost of a regularization penalty and prior knowledge, thus breaking the blind principle. In this study, we show that spreading codes (scFIR) outperforms FIR and sFIR in short-events sequences, thus enabling the blind and dynamic estimation of the HRF without numerical instabilities and the regularization penalty. The scFIR approach was applied in short-events sequences of simulated and experimental functional magnetic resonance imaging (fMRI) data. In general terms, scFIR performed the best with both simulated and experimental data. While FIR was unable to compute the blind estimation of two simulated target HRFs for the shortest sequences (15 and 31 events) and sFIR yielded shapes barely correlated with the targets, scFIR achieved a normalized correlation coefficient above 0.9. Furthermore, scFIR was able to estimate in a responsive way dynamic changes of the amplitude of a simulated target HRF more accurately than FIR and sFIR. With experimental fMRI data, the ability of scFIR to estimate the real HRF obtained from a training data set was superior in terms of correlation and mean-square error. The use of short-events sequences for the blind estimation of the HRF could benefit patients in terms of scanning time or intensity of magnetic field in clinical tests. Furthermore, short-events sequences could be used, for instance, on the online detection of rapid shifts of visual attention that, according to literature, entails rapid changes in the amplitude of the HRF.

In our previous study, healthy volunteers showed considerable short-term dynamics and patterns of the coherence of high time resolution between respiratory movements (RESP), heart rate fluctuations (HRF), and arterial blood pressure fluctuations (BPF). These are physiological indicators of autonomic short-term coordination mediated mainly by the brainstem which could be impaired in severe brain disorders. We hypothesized a direct or indirect impairment of these functions by these disorders and examined these patterns in 16 patients suffering from severe brain disorders. We calculated partial and ordinary coherence sequences and found almost the same patterns of coherence sequences as in healthy volunteers, but a distinctly reduced frequency of pattern incidence in patients (2.8+/-1.5/10 min/patient and 9.5+/-2.8/10 min/subject, P<0.05). Furthermore, there is a significantly smaller frequency of HRF-related patterns in patients with poor outcome, compared with those in patients with good outcome (1.8+/-0.8/10 min/patient and 4.5+/-2.7/10 min/patient, P<0.05). We conclude that severe brain disorders reduce physiological short-term dynamics of autonomic coordination patterns in the mean values of patients, but not in every patient.

Heart rate fluctuations (HRF) were investigated in 18 adult female rabbits (Weisses Grosssilber) in chronical experiments at rest, during motor activity, passive and active avoidance. Pharmacological blockades of the neurovegetative transmission were used to determine the characteristics of neurovegetative mediation during the behavioural states examined. Mean heart rate was always increased during vagal blockade and decreased during beta-adrenergic blockade. At rest the HRF in rabbits are mainly vagally mediated. Vagal blockade reduced the overall heart rate variability by more than 50%. Vagal blockade did not abolish respiratory heart rate fluctuations. If the mean respiratory frequency is greater than half of the mean heart rate frequently occurring in rabbits, the respiratory HRF are generated not synchronously with respiration but in a slower frequency range according to the physiologic effect of "cardiac aliasing". The movement related heart rate increase was absent if the movements were performed during combined beta adrenergic and vagal blockade. The heart rate reaction during passive avoidance was characterized by a heart rate deceleration abolished by vagal blockade. Respiratory HR were increased. Furthermore, the heart rate was returned to the previous mean level and the slow HRF not induced by respiration increased, mediated by beta adrenergic activation. Vagal blockade and beta adrenergic blockade diminished the heart rate acceleration and reduced the overall heart rate variability by more than 50% during active avoidance. Combined vagal and beta-adrenergic blockade fully abolished the heart rate acceleration effect during active avoidance. The ratio of the vagal to sympathetic reaction components differed in behavioural types.

Hemodynamic response function (HRF) estimation in noisy functional magnetic resonance imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Nonparametric methods which allow more flexibility in the estimation by inferring the HRF at each time sample have provided improved performance in comparison to the parametric methods. In this paper, the mixed-effects model is used to derive a new algorithm for nonparametric maximum likelihood HRF estimation. In this model, the random effect is used to better account for the variability of the drift. Contrary to the usual approaches, the proposed algorithm has the benefit of considering an unknown and therefore flexible drift matrix. This allows the effective representation of a broader class of drift signals and therefore the reduction of the error in approximating the drift component. Estimates of the HRF and the hyperparameters are derived by iterative minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the mixed-effects model and a desired family of probability distributions constrained to be concentrated on the observed data. The performance of proposed method is demonstrated on simulated and real fMRI data, the latter originating from both event-related and block design fMRI experiments.

A total of 156 soil samples collected from parts of Northern Nigeria were examined for heat-resistant fungi (HRF) which could cause spoilage of heat processed fruits and fruit products. Each sample was subjected to 70°C for 1 h before plating on potato sucrose agar. Approximately 77% of all the samples contained HRF which were identified as Neosartorya fischeri , N. fischeri var. spinosus , N. quadricincta and Aspergillus fischeri . Other HRF which occurred infrequently were Aspergillus fumigatus and Penicillium spp. The fungal counts in the positive samples generally ranged from 18-300 propagules/10g of soil.

The study was performed in 8 patients suffering from mild asthma. Bronchial allergen challenges with Dermatophagoides pretonyssinus were performed according to Chai et al method. Nonspecific bronchial reactivity to histamine (PC20H in mg/ml) were determined before the allergen challenge and in 90 min. and 24 hrs. after allergen provocation. Blood samples for mononuclear cells culture were collected before the trial and after the allergen challenge in 15, 90 min. and 8 and 24 hrs. The supernatants were assayed for HRF activity with basophils from a single donor. The significant increase of PC20H was observed xgPC20H in mg/ml 0.43 before allergen challenge and 0.08 and 0.125 after allergen provocation. Blood samples did not reveal any significant changes in HRF % activity--mean and SD before challenge test 47.7 16.2 and 49.9 15.0 in 15 min, 53.3 19.0 in 90 min, 56.6 15.8 in 8 hr, 52.5 20.4 in 24 hr after allergen provocation. The correlation between postallergen, nonspecific bronchial reactivity and spontaneous production of HRF activity was not found. The authors discuss the role of HRF in pathogenesis of nonspecific bronchial reactivity of asthmatic patients.

A new structure for polarization-selective elements, consisting of two holographic gratings and a Dove prism coupler, is proposed. The absence of a multistage waveguide and the benefits of compact size and lightweight volume are the outstanding features of the new structure. Based on the coupled-wave theory, the analysis and design of the structure are discussed in detail to calculate the required index modulation. Several parameters, such as the recording intensity, the exposure time, and the recording angles for the fabrication of the proposed element, are determined. Under these conditions, the element is fabricated in Dupont photopolymer HRF-150-38 material and with an operating wavelength of 532 nm. A simplified pickup head is constructed to evaluate the performance of the fabricated element.

Mast cells are effector cells in immunoglobulin E (IgE)-mediated immediate hypersensitivity and allergic diseases such as asthma and food allergy. Mast cells are activated by the aggregation of the IgE-bound high-affinity IgE receptor FcεRI with multivalent antigen. Activated mast cells secrete proinflammatory mediators such as histamine, serotonin, and proteases and produce cytokines and chemokines. However, it has been reported that mast cells are activated by crosslinking of FcεRI with monomeric IgE in the absence of antigen. We have recently demonstrated that histamine-releasing factor (HRF) is involved in IgE-mediated mast cell activation both in vitro and in vivo. HRF binds to a subset of IgE and IgG molecules [HRF-reactive antibodies (Abs)]. The Fab, but not Fc, portions of the IgE and IgG molecules are HRF-binding sites, and the N-terminal 19-residue (N19) and H3 portions of HRF are HRF-reactive Ab-binding sites. We observed that both N19 and H3 tagged with glutathione S transferase (GST) (GST-N19 and GST-H3) can inhibit the interaction between HRF and HRF-reactive Abs. Using acute- and late-phase passive cutaneous anaphylaxis mouse models, it was shown that HRF initiates mast cell activation through HRF-reactive, but not HRF-nonreactive, IgE in vivo. Antigen-induced passive cutaneous anaphylaxis was inhibited by pretreatment with GST-N19 and GST-H3. We demonstrated that pretreatment with GST-N19 before antigen challenge inhibited antigen-induced mast cell-dependent airway inflammation. In addition, GST-N19 partially inhibited Aspergillus fumigatus extract-induced IgE-dependent airway inflammation. However, GST-N19 did not inhibit T cell-dependent airway inflammation. These results suggest that mast cells are target cells for HRF to initiate IgE- and mast cell-dependent airway inflammation.

Inhaled NO (iNO) has been shown to have beneficial effects on decreasing pulmonary inflammation, increasing function of surfactant and improving lung growth in prematurely born animal models. iNO has been gradually applied in the neonatal intensive care unit since its first use for persistent pulmonary hypertension (PPHN) in the early 1990's. Although many research findings have shown the benefits of iNO for hypoxic respiratory failure (HRF) of preterm infants, there is no certain evidence to support the routine use of iNO in premature infants. According to recent literature, the mechanism of iNO therapy, treatment scheme, iNO effectiveness and safety in premature infants were reviewed in this article, so as to provide bases for the clinical use of this treatment.

A modified Delphi technique was used to obtain group consensus among 31 rehabilitation professionals (RPs) from nine rehabilitation centres throughout the province of Québec (Canada) to ascertain their expert opinion on the health risk factors (HRF) to be verified prior to beginning an exercise programme or evaluation for patients with traumatic brain injury (TBI). From the initial survey 87 items were generated, which were later regrouped into 27 HRFs. The relative importance of each HRF in regard to being screened before exercise in a population with TBI was then assessed by each RP using a five-point ordinal scale (1 = not important to 5 = extremely important). HRFs that were considered extremely important by at least 50% of HPs include: angina pectoris, aortic stenosis, exertional syncope, musculoskeletal sequelae which are exacerbated by exercise, outward aggressivity, pulmonary embolism, uncontrolled epilepsy (seizures), and ventricular arrhythmias. Professionals involved in exercising patients with TBI may find these factors useful to the efficient conduct of their rehabilitation programme.

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions.

Purpose: To design a robust and automated hyperreflective foci (HRF) segmentation framework for spectral-domain optical coherence tomography (SD-OCT) volumes, especially volumes with low HRF-background contrast.

Methods: HRF in retinal SD-OCT volumes appear with low-contrast characteristics that results in the difficulty of HRF segmentation. Therefore to effectively segment the HRF we proposed a fully automated method for HRF segmentation in SD-OCT volumes with diabetic retinopathy (DR). First, we generated the enhanced SD-OCT images from the denoised SD-OCT images with an enhancement method. Then the enhanced images were cascaded with the denoised images as the two-channel input to the network against the low-contrast HRF. Finally, we replaced the standard convolution with slice-wise dilated convolution in the last layer of the encoder path of 3D U-Net to obtain long-range information.

Results: We evaluated our method using two-fold cross-validation on 33 SD-OCT volumes from 27 patients. The average dice similarity coefficient was 70.73%, which was higher than that of the existing methods with significant difference (P < 0.01).

Conclusions: Experimental results demonstrated that the proposed method is faster and achieves more reliable segmentation results than the current HRF segmentation algorithms. We expect that this method will contribute to clinical diagnosis and disease surveillance.

Translational relevance: Our framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR.

Keywords: 3D U-Net; SD-OCT; hyperreflective foci segmentation; image enhancement; slice-wise dilated convolution.

OBJECTIVE

A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs.

METHODS

The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy.

RESULTS

For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%.

CONCLUSIONS

The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.

Objective: To evaluate whether combined surfactant with inhaled nitric oxide (iNO) use will prevent newborns with hypoxemic respiratory failure (HRF) from developing an Oxygenation Index (OI) > 40.

Methods: 100 term newborns with acute HRF (OI ≥ 20) were randomized to: Surfactant+iNO: received iNO plus up to two doses of surfactant or iNO-Controls: received iNO+placebo. Main outcome was the development of severe HRF (OI > 40) despite iNO use.

Results: Baseline mean ± SD OI was 37.4 ± 14 for the Surfactant+iNO group and 38.2 ± 16 for the controls. Infants receiving surfactant+iNO improved their oxygenation faster, resulting in lower OI at 24 h: 12.9 ± 9 vs 18.7 ± 11 of controls, p < 0.05; and a lower proportion developing OI > 40: 24%(12/50) vs 50%(25/50) of controls, p < 0.02. Fewer infants receiving surfactant+iNO presented the combined outcome of death or ECMO: 16%(8/50) compared to 36%(18/50) of controls, p < 0.05.

Conclusions: Early use of combined surfactant+iNO improves oxygenation preventing the progression to severe HRF. This may reduce mortality and ECMO need.

The objective of this study was to examine the predictive relationships between employee health risk factors (HRFs) and workers' compensation (WC) claim occurrence and costs.

Logistic regression and generalised linear models were used to estimate the predictive association between HRFs and claim occurrence and cost among a cohort of 16 926 employees from 314 large, medium and small businesses across multiple industries. First, unadjusted (HRFs only) models were estimated, and second, adjusted (HRFs plus demographic and work organisation variables) were estimated.

Unadjusted models demonstrated that several HRFs were predictive of WC claim occurrence and cost. After adjusting for demographic and work organisation differences between employees, many of the relationships previously established did not achieve statistical significance. Stress was the only HRF to display a consistent relationship with claim occurrence, though the type of stress mattered. Stress at work was marginally predictive of a higher odds of incurring a WC claim (p<0.10). Stress at home and stress over finances were predictive of higher and lower costs of claims, respectively (p<0.05).

The unadjusted model results indicate that HRFs are predictive of future WC claims. However, the disparate findings between unadjusted and adjusted models indicate that future research is needed to examine the multilevel relationship between employee demographics, organisational factors, HRFs and WC claims.

Hydroxyl radical footprinting-mass spectrometry (HRF-MS) is a powerful technique for measuring protein structure by quantitating the solvent accessibility of amino acid side-chains; and when used in comparative analysis, HRF-MS data can provide detailed information on changes in protein structure. However, consistently controlling the amount of hydroxyl radical labeling of a protein requires the precise understanding of both the amount of radicals generated and half-life of the radicals in solution. The latter is particularly important for applications such as protein-protein and protein-ligand interactions, which may have different characteristics such as intrinsic reactivity and buffer components, which can cause differences in radical scavenging (termed "scavenging potential") between samples. To address this inherent challenge with HRF-MS analysis, we describe the comprehensive implementation of an internal standard (IS) dosimeter peptide leucine enkephalin (LeuEnk) for measuring the scavenging potential of pharmaceutically relevant proteins and formulation components. This further enabled evaluation of the critical method parameters affecting the scavenging potential of samples subjected to HRF-MS using Fast Photochemical Oxidation of Proteins (FPOP). We demonstrate a direct correlation between the oxidation of the IS peptide and biotherapeutic target proteins, and show the oxidation of the IS can be used as a guide for ensuring equivalent scavenging potentials when comparing multiple samples. Establishing this strategy enables optimization of sample parameters, a system suitability approach, normalization of data, and comparison/harmonization of HRF-MS analysis across different laboratories.

Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal components analysis (PCA), correlation-based signal improvement (CBSI), wavelet filtering, and spline interpolation. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Brigadoi et al. compared motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Given that fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. This study addresses that problem by evaluating motion correction algorithms implemented in HomER2. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response. Results showed that targeted PCA (tPCA), spline, and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using quantitative metrics. The CBSI method corrected many of the artifacts present in our data; however, this approach produced sometimes unstable HRFs. The targeted PCA and spline methods proved to be the most robust, performing well across all comparison metrics. When compared head to head, tPCA consistently outperformed spline. We conclude, therefore, that tPCA is an effective technique for correcting motion artifacts in fNIRS data from young children.