LIONirs: flexible Matlab toolbox for fNIRS data analysis

Relationship between EEG spectral power and dysglycemia with neurodevelopmental outcomes after neonatal encephalopathy

OBJECTIVE

We investigated how electroencephalography (EEG) quantitative measures and dysglycemia relate to neurodevelopmental outcomes following neonatal encephalopathy (NE).

METHODS

This retrospective study included 90 neonates with encephalopathy who received therapeutic hypothermia. EEG absolute spectral power was calculated during post-rewarming and 2-month follow-up. Measures of dysglycemia (hypoglycemia, hyperglycemia, and glycemic lability) and glucose variability were computed for the first 48 h of life. We evaluated the ability of EEG and glucose measures to predict neurodevelopmental outcomes at ≥ 18 months, using logistic regressions (with area under the receiver operating characteristic [AUROC] curves).

RESULTS

The post-rewarming global delta power (average all electrodes), hyperglycemia and glycemic lability predicted moderate/severe neurodevelopmental outcome separately (AUROC = 0.8, 95%CI [0.7,0.9], p < .001) and even more so when combined (AUROC = 0.9, 95%CI [0.8,0.9], p < .001). After adjusting for NE severity and magnetic resonance imaging (MRI) brain injury, only global delta power remained significantly associated with moderate/severe neurodevelopmental outcome (odds ratio [OR] = 0.9, 95%CI [0.8,1.0], p = .04), gross motor delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), global developmental delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), and auditory deficits (OR = 0.9, 95%CI [0.8,1.0], p = .03).

CONCLUSIONS

In NE, global delta power post-rewarming was predictive of outcomes at ≥ 18 months.

SIGNIFICANCE

EEG markers post-rewarming can aid prediction of neurodevelopmental outcomes following NE.

Language brain responses and neurodevelopmental outcome in preschoolers with congenital heart disease: A fNIRS study

Neurodevelopmental disabilities affect up to 50% of survivors of congenital heart disease (CHD). Language difficulties are frequently identified during preschool period and can lead to academic, social, behavioral, and emotional difficulties. Structural brain alterations are associated with poorer neurodevelopmental outcomes in patients with CHD during infancy, childhood, and adolescence. However, evidence is lacking about the functional brain activity in children with CHD and its relationship with neurodevelopment. This study therefore aimed to characterize brain responses during a passive story-listening task in 3-year-old children with CHD, and to investigate the relationship between functional brain patterns of language processing and neurodevelopmental outcomes. To do so, we assessed hemodynamic concentration changes, using functional near-infrared spectroscopy (fNIRS), and neurodevelopmental outcomes, using the Wechsler Preschool and Primary Scale of Intelligence - 4th Edition (WPPSI-IV), in children with CHD (n = 19) and healthy controls (n = 23). Compared to their healthy peers, children with CHD had significantly lower scores on the Verbal comprehension index (VCI), the Vocabulary acquisition index (VAI), the General ability index (GAI), and the Information and the Picture Naming subtests of the WPPSI-IV. During the passive story-listening task, healthy controls showed significant hemodynamic brain responses in the temporal and the temporal posterior regions, with stronger activation in the temporal posterior than in the temporal regions. In contrast, children with CHD showed reduced activation in the temporal posterior regions compared to controls, with no difference of activation between regions. Reduced brain responses in the temporal posterior regions were also correlated with lower neurodevelopmental outcomes in both groups. This is the first study that reveals reduced brain functional responses in preschoolers with CHD during a receptive language task. It also suggests that the temporal posterior activation could be a potential brain marker of cognitive development. These findings provide support for the feasibility of identifying brain correlates of neurodevelopmental vulnerabilities in children with CHD.

Early childhood malnutrition impairs adult resting brain function using near-infrared spectroscopy

Introduction

Early childhood malnutrition affects 200+ million children under 5 years of age worldwide and is associated with persistent cognitive, behavioral and psychiatric impairments in adulthood. However, very few studies have investigated the long-term effects of childhood protein-energy malnutrition (PEM) on brain function using a functional hemodynamic brain imaging technique.

Objective and methods

This study aims to investigate functional brain network alterations using near infrared spectroscopy (NIRS) in adults, aged 45-51 years, from the Barbados Nutrition Study (BNS) who suffered from a single episode of malnutrition restricted to their first year of life (n = 26) and controls (n = 29). A total of 55 individuals from the BNS cohort underwent NIRS recording at rest.

Results and discussion

Using functional connectivity and permutation analysis, we found patterns of increased Pearson's correlation with a specific vulnerability of the frontal cortex in the PEM group (ps < 0.05). Using a graph theoretical approach, mixed ANCOVAs showed increased segregation (ps = 0.0303 and 0.0441) and decreased integration (p = 0.0498) in previously malnourished participants compared to healthy controls. These results can be interpreted as a compensatory mechanism to preserve cognitive functions, that could also be related to premature or pathological brain aging. To our knowledge, this study is the first NIRS neuroimaging study revealing brain function alterations in middle adulthood following early childhood malnutrition limited to the first year of life.

Relationship between 4-month functional brain network topology and 24-month neurodevelopmental outcome in children with congenital heart disease

Survivors of complex forms of congenital heart disease (CHD)∗ are at high risk of neurodevelopmental disabilities. Neuroimaging studies have pointed to brain anomalies and immature networks in infants with CHD, yet less is known about their functional network topology and associations with neurodevelopment. To characterize the functional network topology in 4-month-old infants with repaired CHD, we compared graph theory metrics measured using resting-state functional near-infrared spectroscopy (rs-fNIRS) between infants with CHD (n = 22) and healthy controls (n = 30). We also investigated the moderating effect of graph theory metrics on the relationship between group (CHD vs. Controls) and developmental outcomes at 24 months. At 4 months, both groups presented similar functional brain network topology. At 24 months, children with CHD had lower scores on the language scale and the expressive communication subscale of the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III), as well as lower scores on the Grammatical Form scale of the MacArthur-Bates Communicative Development Inventory (MBCDI). The relationship between group and expressive language was moderated by the normalized characteristic path length (λ) and the degree (k). Although infants with CHD have functional brain topology similar to that of healthy controls, our findings suggest that they do not benefit from an optimal functional brain organization in comparison with healthy infants.

Functional brain connectivity after corrective cardiac surgery for critical congenital heart disease: a preliminary near-infrared spectroscopy (NIRS) report

Patients with congenital heart disease (CHD) requiring cardiac surgery in infancy are at high risk for neurodevelopmental impairments. Neonatal imaging studies have reported disruptions of brain functional organization before surgery. Yet, the extent to which functional network alterations are present after cardiac repair remains unexplored. This preliminary study aimed at investigating cortical functional connectivity in 4-month-old infants with repaired CHD, using resting-state functional near-infrared spectroscopy (fNIRS). After fNIRS signal frequency decomposition, we compared values of magnitude-squared coherence as a measure of connectivity strength, between 21 infants with corrected CHD and 31 healthy controls. We identified a subset of connections with differences between groups at an uncorrected statistical level of p < .05 while controlling for sex and maternal socioeconomic status, with most of these connections showing reduced connectivity in infants with CHD. Although none of these differences reach statistical significance after FDR correction, likely due to the small sample size, moderate to large effect sizes were found for group-differences. If replicated, these results would therefore suggest preliminary evidence that alterations of brain functional connectivity are present in the months after cardiac surgery. Additional studies involving larger sample size are needed to replicate our data, and comparisons between pre- and postoperative findings would allow to further delineate alterations of functional brain connectivity in this population.

Brain language networks and cognitive outcomes in children with frontotemporal lobe epilepsy

Introduction

Pediatric frontal and temporal lobe epilepsies (FLE, TLE) have been associated with language impairments and structural and functional brain alterations. However, there is no clear consensus regarding the specific patterns of cerebral reorganization of language networks in these patients. The current study aims at characterizing the cerebral language networks in children with FLE or TLE, and the association between brain network characteristics and cognitive abilities.

Methods

Twenty (20) children with FLE or TLE aged between 6 and 18 years and 29 age- and sex-matched healthy controls underwent a neuropsychological evaluation and a simultaneous functional near-infrared spectroscopy and electroencephalography (fNIRS-EEG) recording at rest and during a receptive language task. EEG was used to identify potential subclinical seizures in patients. We removed these time intervals from the fNIRS signal to investigate language brain networks and not epileptogenic networks. Functional connectivity matrices on fNIRS oxy-hemoglobin concentration changes were computed using cross-correlations between all channels.

Results and discussion

Group comparisons of residual matrices (=individual task-based matrix minus individual resting-state matrix) revealed significantly reduced connectivity within the left and between hemispheres, increased connectivity within the right hemisphere and higher right hemispheric local efficiency for the epilepsy group compared to the control group. The epilepsy group had significantly lower cognitive performance in all domains compared to their healthy peers. Epilepsy patients' local network efficiency in the left hemisphere was negatively associated with the estimated IQ (p = 0.014), suggesting that brain reorganization in response to FLE and TLE does not allow for an optimal cognitive development.

Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data

SIGNIFICANCE

Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields.

AIM

We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength).

APPROACH

We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions.

RESULTS

PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics.

CONCLUSIONS

This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.