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

Fourdain, Solène; Provost, Sarah; Tremblay, Julie; Vannasing, Phetsamone; Doussau, Amélie; Caron-Desrochers, Laura; Gaudet, Isabelle; Roger, Kassandra; Hüsser, Alejandra; Dehaes, Mathieu; Martı́nez-Montes, Eduardo; Poirier, Nancy; Gallagher, Anne

doi:10.1080/09297049.2023.2170340

Cited by 5 publications

(4 citation statements)

References 82 publications

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“…The optical intensity signal was preprocessed using the LIONirs toolbox ( Tremblay et al, 2022 ), running on SPM12 (Wellcome Trust Centre for Neuroimaging, UK) in MATLAB (Version 2021b, MathWorks, Natick, MA, USA). We first performed a phase coherence-based-cardiac detection to assess the signal quality and reject noisy channels ( Fourdain et al, 2023 ). We excluded channels with a mean phase coherence below 0.85, indicating insufficient heartbeat presence compared to the other channels and, thus, poor signal quality.…”

Section: Methodsmentioning

confidence: 99%

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

Roger,

Vannasing,

Tremblay

et al. 2024

Front. Hum. Neurosci.

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IntroductionEarly 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 methodsThis 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 discussionUsing 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.

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Section: Methodsmentioning

confidence: 99%

“…It reflects the balance between network integration and segregation in comparison to random networks. For more information on graph theory metrics, see Rubinov and Sporns (2010) or Fornito et al (2016).…”

Section: Functional Connectivity and Graph Theorymentioning

confidence: 99%

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

Roger,

Vannasing,

Tremblay

et al. 2024

Front. Hum. Neurosci.

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“…Raw fNIRS data was imported into the LIONirs toolbox ( Tremblay et al, 2022 ), which is embedded in SPM12 (Statistical Parametric Mapping) and operates in MATLAB (The MathWorks, Inc., 2019b, Natick, MA, United States). Signal coherence among channels for the peak frequency commonly related to the cardiac beat (0.8–2.3 Hz) allowed to exclude channels with poor optode-to-scalp coupling ( Yücel et al, 2021 ; Fourdain et al, 2023 ). Intervals with aberrant signal variation based on moving average criteria were identified using an automatic algorithm.…”

Section: Methodsmentioning

confidence: 99%

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

Hüsser,

Vannasing,

Tremblay

et al. 2023

Front. Hum. Neurosci.

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IntroductionPediatric 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.MethodsTwenty (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 discussionGroup 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.

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“…This technique holds tremendous promise as a bedside monitoring tool, particularly for children and neonates impacted by critical illness, where minimizing invasive imaging procedures involving exposure to radiation or even loud noises is a key priority. Functional NIRS has been successfully introduced in clinical settings in both adult and paediatric populations [1][2][3][4]. However, one major drawback is that fNIRS can only detect changes at the cortical level, as it is limited by penetration depth of near-infrared light.…”

Section: Introductionmentioning

confidence: 99%

Predicting cortical-thalamic connectivity using functional near-infrared spectroscopy and graph convolutional networks

Tang,

Kebaya,

Vahidi

et al. 2024

Preprint

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Functional near-infrared spectroscopy (fNIRS) measures cortical changes in hemoglobin concentrations, yet cannot collected this information from the subcortices. To address this drawback, we propose a machine-learning-based approach to predict cortical-thalamic functional connectivity using cortical fNIRS data. We applied graph convolutional networks (GCN) on two datasets obtained from adults and neonates, respectively. Each dataset contained fNIRS data as input to the predictive models and connectivities of functional magnetic resonance imaging (fMRI) as training targets. GCN models performed better compared to conventional methods, on both identifying the connections, and regressing the quantified strengths of connections. We also propose the addition of inter-subject connections into the GCN kernels could improve performance. Our results show it is feasible to indicate subcortical activity from cortical fNIRS recordings.

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

Cited by 5 publications

References 82 publications

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

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

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

Predicting cortical-thalamic connectivity using functional near-infrared spectroscopy and graph convolutional networks

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