Whole-cortical graphical networks at wakeful rest in young and older adults revealed by functional near-infrared spectroscopy

Abstract

A good understanding of age-dependent changes and modifications in brain networks is crucial for fully exploring the effects of aging on the human brain. Few reports have been found in studies of functional brain networks using functional near-infrared spectroscopy (fNIRS). Moreover, little is known about the feasibility of using fNIRS to assess age-related changes in brain connectomes. This study applied whole brain fNIRS measurement, combined with graph theory analysis, to assess the age-dependent changes in resting-state brain networks. Five to eight minutes of resting-state brain hemodynamic signals were recorded from 48 participants (18 young adults and 30 older adults) with 133 optical channels covering the majority of the cortical regions. Both local and global graph metrics were computed to identify the age-related changes of topographical brain networks. Older adults showed an overall decline of both global and local efficiency compared to young adults, as well as the decline of small-worldness. In addition, young adults showed the abundance of hubs in the prefrontal cortex, whereas older adults revealed the hub shifts to the sensorimotor cortex. These obvious shifts of hubs may potentially indicate decreases of the decision-making, memory, and other high-order functions as people age. Our results showed consistent findings with published literature and also demonstrated the feasibility of whole-head fNIRS measurements to assess age-dependent changes in resting-state brain networks.

fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases

“…GE is also shown to be affected by age. 64 , 71 The GE based findings in this paper are in alignment with the literature, where a decline in GE was observed for neuropsychiatric patients. On average across all stimulus types, there was a 17% reduction in the of migraineurs, 20% of OCD, and 48% of schizophrenics from controls as can be inferred from Table 6 .…”

Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex

“…Graph theory has been used commonly to examine such brain networks at resting state. In the field of NIRS, task-driven brain activations and GTA-derived network connectivity are often studied separately (Niu et al, 2012 ; Niu and He, 2014 ; Li et al, 2018 ). A recent study revealed that resting-state properties and task-evoked networks in the human brain can have significant correlations with the activation of brain regions during tasks of executive functions (Hu et al, 2019 ).…”

“…Graph theory is a renowned mathematical model to study characteristics of a network system (Erdös, 1959 ; Bondy and Murty, 1976 ), and it has been applied to investigate resting state functional connectivity of the human brain as measured with fMRI, Electroencephalography (EEG), and Magnetoencephalographic (MEG) (Salvador et al, 2005 ; Achard et al, 2006 ; Bassett et al, 2006 ; Iturria-Medina et al, 2008 ), as well as fNIRS (Niu et al, 2012 ; Niu and He, 2014 ; Li et al, 2018 ). A recent study demonstrated that based on GTA, brain networks were altered when the human brain transitioned from resting state to task-evoked states (Hu et al, 2019 ).…”

“…Our analysis based on GTA was performed following similar steps to those in (Niu et al, 2012 ; Niu and He, 2014 ; Cai et al, 2018 ; Li et al, 2018 ), as follows: (1) The processed Δ[HbO] time series for all channels were analyzed to create Pearson's correlation coefficient (PCC) matrices in the rest and decision-making phases. Only positive correlations were considered for calculations.…”

“…It can provide an easy and yet powerful mathematical means to characterize the topological properties of the brain networks (Sporns et al, 2005 ; Pavlopoulos et al, 2011 ; Vecchio et al, 2017 ). In particular, a few recent studies have combined GTA with channel-wise fNIRS, and have revealed the topological organization and architecture of large-scale, resting-state human brain cortical networks (Niu et al, 2012 ; Niu and He, 2014 ; Li et al, 2018 ). Furthermore, since the human brain is a dynamic organ, numerous alterations take place in the brain at fast time scales.…”