ArticleNeuroImage2015
Dynamic causal modelling for functional near-infrared spectroscopy.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review.Frontiers in neurology · 2025Pooled it
- Shedding light on imagined scents: an fNIRS feasibility study of olfactory mental imagery and semantic cueing.Frontiers in neuroscience · 2026Article
- Anodal tDCS over the supplementary motor area increases motor overflow during imagined aiming movement.Experimental brain research · 2025Article
- Supplementary motor area enhanced hemodynamic responses to loading after inhibitory cTBS.Scientific reports · 2025Article
- Comparative study of brain functional imaging of brain in patients with mild to moderate Alzheimer's disease based on functional near infrared spectroscopy.BMC neurology · 2025Article
- Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.Network neuroscience (Cambridge, Mass.) · 2024Article
- Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges.Neurophotonics · 2023Article
- Impact of Anatomical Variability on Sensitivity Profile in fNIRS-MRI Integration.Sensors (Basel, Switzerland) · 2023Article
- Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data.Neurophotonics · 2022Article
- Identification of the Cognitive Interference Effect Related to Stroop Stimulation: Using Dynamic Causal Modeling of Effective Connectivity in Functional Near-Infrared Spectroscopy (fNIRS).Journal of biomedical physics & engineering · 2020Article
- The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience.Annals of the New York Academy of Sciences · 2020Review
- Atypical Dynamic-Connectivity Recruitment in Attention-Deficit/Hyperactivity Disorder Children: An Insight Into Task-Based Dynamic Connectivity Through an fNIRS Study.Frontiers in human neuroscience · 2020Article
- Maternal speech shapes the cerebral frontotemporal network in neonates: A hemodynamic functional connectivity study.Developmental cognitive neuroscience · 2019Article
- Functional near-infrared spectroscopy for monitoring macaque cerebral motor activity during voluntary movements without head fixation.Scientific reports · 2018Article
- Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset.NeuroImage · 2018Article
- Imperial College near infrared spectroscopy neuroimaging analysis framework.Neurophotonics · 2018Article
- Mapping effective connectivity within cortical networks with diffuse optical tomography.Neurophotonics · 2017Article
- Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study.Neurophotonics · 2017Article
- Sensor space group analysis for fNIRS data.Journal of neuroscience methods · 2016Article
- Supplementary motor area deactivation impacts the recovery of hand function from severe peripheral nerve injury.Neural regeneration research · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.