Homa Vahidi

All Publications

• Investigating Task-Free Functional Connectivity Patterns in Newborns Using Functional Near-Infrared Spectroscopy. Brain and behavior Vahidi, H., Kowalczyk, A., Stubbs, K., Musabi, M., Roychaudhuri, S., Kent, M., Bhattacharya, S., de Ribaupierre, S., Lawrence, K. S., Mohsenzadeh, Y., Duerden, E. G. 2024; 14 (12): e70180

  Abstract

  BACKGROUND: Resting-state networks (RSNs), particularly the sensorimotor network, begin to strengthe in the third trimester of pregnancy and mature extensively by term age. The integrity and structure of these networks have been repeatedly linked to neurological health outcomes in neonates, highlighting the importance of understanding the normative variations in RSNs in healthy development. Specifically, robust bilateral functional connectivity in the sensorimotor RSN has been linked to optimal neurodevelopmental outcomes in neonates.AIM: In the current study, we aimed to map the developmental trajectory of the sensorimotor RSN in awake neonates using functional near-infrared spectroscopy (fNIRS).MATERIALS & METHODS: We acquired fNIRS resting-state data from 41 healthy newborns (17 females, gestational age ranging from 36+0 to 42+1weeks) within the first week after birth. We performed both single channel and hemispheric analyses to investigate the relationship between functional connectivity and both gestational and postnatal age.RESULTS: We observed robust positive connectivity in numerous channel-pairs across the sensorimotor network, especially in the left hemisphere. Next, we examined the relationship between functional connectivity, gestational age, and postnatal age, while controlling for sex and subject effects. We found both gestational and postnatal age to be significantly associated with changes in functional connectivity in the sensorimotor RSN. In our hemispheric analysis (Ninterhemispheric=10, Nleft intrahemispheric=15, and Nright intrahemispheric=9), we observed a significant positive relationship between interhemispheric connectivity and postnatal age.DISCUSSION AND CONCLUSION: In summary, our findings demonstrate the utility of fNIRS for monitoring early developmental changes in functional networks in awake newborns.

  View details for DOI 10.1002/brb3.70180

  View details for PubMedID 39690863

• Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks. Scientific reports Tang, L., Kebaya, L. M., Vahidi, H., Meyerink, P., de Ribaupierre, S., Bhattacharya, S., St Lawrence, K., Duerden, E. G. 2024; 14 (1): 29794

  Abstract

  Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. 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) to two datasets obtained from healthy adults and neonates with early brain injuries, respectively. Each dataset contained fNIRS connectivity data as input to the predictive models, while the connectivity from functional magnetic resonance imaging (fMRI) served as training targets. GCN models performed better compared to conventional methods, such as support vector machine and feedforward fully connected artificial neural networks, on both identifying the connections as binary classification tasks, and regressing the quantified strengths of connections. We also propose the addition of inter-subject connections into the GCN kernels could improve performance and that GCN models are resilient to noise in fNIRS data. Our results show it is feasible to identify subcortical activity from cortical fNIRS recordings. The findings can potentially extend the use of fNIRS in clinical settings for brain monitoring in critically ill patients.

  View details for DOI 10.1038/s41598-024-79390-3

  View details for PubMedID 39616218

  View details for PubMedCentralID 9982436

• Altered functional connectivity in preterm neonates with intraventricular hemorrhage assessed using functional near-infrared spectroscopy. Scientific reports Kebaya, L. M., Tang, L., Altamimi, T., Kowalczyk, A., Musabi, M., Roychaudhuri, S., Vahidi, H., Meyerink, P., de Ribaupierre, S., Bhattacharya, S., de Moraes, L. T., Lawrence, K. S., Duerden, E. G. 2024; 14 (1): 22300

  Abstract

  Intraventricular hemorrhage (IVH) is a common neurological injury following very preterm birth. Resting-state functional connectivity (RSFC) using functional magnetic resonance imaging (fMRI) is associated with injury severity; yet, fMRI is impractical for use in intensive care settings. Functional near-infrared spectroscopy (fNIRS) measures RSFC through cerebral hemodynamics and has greater bedside accessibility than fMRI. We evaluated RSFC in preterm neonates with IVH using fNIRS and fMRI at term-equivalent age, and compared fNIRS connectivity between healthy newborns and those with IVH. Sixteen very preterm born neonates were scanned with fMRI and fNIRS. Additionally, fifteen healthy newborns were scanned with fNIRS. In preterms with IVH, fNIRS and fMRI connectivity maps were compared using Euclidean and Jaccard distances. The severity of IVH in relation to fNIRS-RSFC strength was examined using generalized linear models. fNIRS and fMRI RSFC maps showed good correspondence. Connectivity strength was significantly lower in healthy newborns (p-value = 0.023) and preterm infants with mild IVH (p-value = 0.026) compared to infants with moderate/severe IVH. fNIRS has potential to be a new bedside tool for assessing brain injury and monitoring cerebral hemodynamics, as well as a promising biomarker for IVH severity in very preterm born infants.

  View details for DOI 10.1038/s41598-024-72515-8

  View details for PubMedID 39333278

  View details for PubMedCentralID PMC11437059

• Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy SCIENTIFIC REPORTS Tang, L., Kebaya, L. N., Altamimi, T., Kowalczyk, A., Musabi, M., Roychaudhuri, S., Vahidi, H., Meyerink, P., de Ribaupierre, S., Bhattacharya, S., de Moraes, L., St. Lawrence, K., Duerden, E. G. 2024; 14 (1): 3176

  Abstract

  Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health. Functional near-infrared spectroscopy (fNIRS) can assess resting-state functional connectivity (RSFC) at the bedside. We acquired resting-state fNIRS data from 21 neonates with HIE (postmenstrual age [PMA] = 39.96), in 19 neonates the scans were acquired post-therapeutic hypothermia (TH), and from 20 term-born healthy newborns (PMA = 39.93). Twelve HIE neonates also underwent resting-state functional magnetic resonance imaging (fMRI) post-TH. RSFC was calculated as correlation coefficients amongst the time courses for fNIRS and fMRI data, respectively. The fNIRS and fMRI RSFC maps were comparable. RSFC patterns were then measured with graph theory metrics and compared between HIE infants and healthy controls. HIE newborns showed significantly increased clustering coefficients, network efficiency and modularity compared to controls. Using a support vector machine algorithm, RSFC features demonstrated good performance in classifying the HIE and healthy newborns in separate groups. Our results indicate the utility of fNIRS-connectivity patterns as potential biomarkers for HIE and fNIRS as a new bedside tool for newborns with HIE.

  View details for DOI 10.1038/s41598-024-53256-0

  View details for Web of Science ID 001158921600068

  View details for PubMedID 38326455

  View details for PubMedCentralID PMC10850364