KEYWORDS
fNIRS, brain aging, aging, machine learning
ABBREVIATIONS AD, Alzheimer’s disease; bacc, balanced accuracy; CSF, cerebrospinal
fluid; CV, cross-validation; DPF, differential path length factor;
dPhaseTE, differential phase transfer entropy; EEG,
electroencephalography; ESSC, envelope-signal spectra correlation; fMRI,
functional magnetic resonance imaging; fAPF, fractional amplitude of
physiological fluctuations; fNIRS, functional near-infrared
spectroscopy; FC, functional connectivity; H2O, water,
HbO, oxygenated-hemoglobin; HbR, deoxygenated-hemoglobin; HbT,
total-hemoglobin; LED, light-emitting diodes; LOOCV,
leave-one-out-cross-validation; MBBL, modified beer-lambert law; MCCV,
Monte Carlo cross-validation; MREG, magnetic resonance encephalography;
MRI, magnetic resonance imaging; MRMR, minimum redundancy maximum
relevance; MCI, mild cognitive-impairment ; NDD, neurodegenerative
disease; NIR, near-infrared; PFC, prefrontal cortex; PSD, power spectral
density; SampEn, sample entropy; SD, source-detector; SVM, support
vector machine; TBI: traumatic brain injury
1 | INTRODUCTION
The life expectancy of the global population is steadily continuing to
increase [1]. As aging possess an increased risk of developing
neurodegenerative diseases (NDD), such as Alzheimer’s disease (AD), the
better understanding of healthy brain aging process is of importance.
The assessment of the brain aging trajectory is of interest to detect of
deviations from the healthy trajectory, and to monitor the efficacy of
the treatments and interventions. The increased understanding of the
brain aging process can lead to development of improved interventions,
with an aim to delay age-related disease onset and to reduce their
severity in the later years of life.
Brain aging has been widely studied, and multiple interconnected
structural and functional changes have been reported. The changes can be
observed in different scales of interest, such as micro or macro scale.
Some notable changes are DNA damage, which is considered as one of the
key hallmarks of aging, cerebrovascular changes observed as decreased
vessel size, reduced number of capillaries, small infractions and
microbleeds, causing overall decreased cerebral perfusion, changes in
functional connectivity (FC), and regional brain atrophy, manifested in
overall decrease of brain volume and weight. [2] In recent years,
neuroimaging methods such as magnetic resonance imaging (MRI) and
electroencephalography (EEG) have been utilized to measure the healthy
brain aging trajectory. The methods commonly aim to quantify the
biological age of the brain, which is assumed to be congruent with the
chronological age of the healthy individuals, while being increased in
the presence of aging related disease and decreased in successful aging
[3]
Functional near-infrared spectroscopy (fNIRS) presents an intriguing
method for the assessment of aging related-changes in the brain due to
its good temporal resolution, affordability, and portable light-weight
equipment. This enables development of brain health monitoring
applications which can be used in a natural environment. The method is
based on applying light source-detector (SD) pairs with minimum distance
of 2.5 cm to the scalp [4], utilizing wavelengths in the optical
window of approximately 650 nm – 1000 nm, where the near-infrared (NIR)
light is able to propagate through the superficial biological layers of
the skin, skull and cerebrospinal fluid (CSF), reaching the cortical
brain layer [5]. Finally, the back scattered light is recorded by
the detector pair. By applying modified beer-lambert law (MBBL), the
method can be used to quantify chromophore relative concentrations
[6]. The method requires selection of at least two wavelengths from
the different sides of the isobestic point of the chromophores of
interest. Commonly the concentration changes of oxygenated-hemoglobin
(HbO) and deoxygenated-hemoglobin (HbR) are monitored, as the cortical
changes are related to the brain metabolism caused by neural activity
[5]. However, the changes in water (H2O) can be
detected in NIR region as well by selecting wavelength accordingly. The
measurement of neurohydrodynamics have been demonstrated recently in the
assessment of glymphatic system by fNIRS [7].
fNIRS has been used in multiple aging studies showing evidence for the
use of measurement method in the brain aging assessment. In FC studies
fNIRS has been used successfully to confirm the finding of increased
brain region co-operation, assumed to be due to brain’s adaptation to
the structural changes. [8] Furthermore, Nguyen et al. found
evidence of age-related decreased FC detected during verbal fluency
task, although evidence of age-related changes during oddball and
resting state was not found. [9]
Arterial stiffness has been associated with aging and is one of the
leading risk factors for hypertension [10]. As younger subject’s
arteries are more compliant, the mechanical stress to the brain caused
by pulsatility is reduced in comparison to older adults with stiffer
arteries, causing damage to the brain’s microvasculature. The arterial
stiffness can be detected from the changed pulse shape form, and the
distinguishing of younger and older adults has been shown to be
successful by fNIRS by using pulse shape parameters. [11]
Additionally, fNIRS has been used with MRI to show association between
aging related cortical thinning and regional pulsatility. [12]
fNIRS has been used widely to study aging related changes in prefrontal
cortex (PFC) activation, which is involved in the executive function.
The results have shown age related differences in the PFC activation
[13–16]. Furthermore, fNIRS has been used to characterize
differences between younger and older adults, and mild
cognitive-impairment (MCI) and AD groups [17,18], and to show
age-related differences in within-session trainability of hemodynamic
response. [19]
The results of the previous findings suggest potential for the use of
fNIRS in the study of aging. To investigate this further, the use of
single channel resting state fNIRS to detect aging caused differences in
healthy adults is studied by utilizing machine learning (ML) methods in
an age group classification task. In addition to traditional analysis of
HbO, HbR and total hemoglobin (HbT) changes, the effects of relative
H2O concentration changes and raw fNIRS signals are
used. Multiple features based on the latest fNIRS, EEG and functional
magnetic resonance imaging (fMRI) studies are applied. The results
presented are based on the research conducted for the master’s thesis of
Martti Ilvesmäki [20].
2 | EXPERIMENTAL
2.1 | fNIRS measurements and data acquisition
The data collection followed the guidelines established by the
Declaration of Helsinki and the study was approved by the regional
Ethical Committee of Northern Ostrobothnia Hospital District in Oulu
University Hospital. All participants of the study signed informed
consent letters before the measurements. The data was collected with
hospital multi-modal MRI compatible frequency coded fNIRS utilizing high
power light-emitting diodes (LED) coupled to optical fibres, with
modifiable wavelength selection [21,22].
Total of 56 healthy controls participated in the study. The participants
restrained from the use of alcohol 12 hours prior the measurements. With
the exception of one individual, the participants were non-smoking. Data
from single optode with 3 cm source detector (SD) separation distance,
placed on the left side of the forehead corresponding to the Fp1 of the
10-20 system of the international federation for EEG electrode
placement, was used with sampling frequency of 800 Hz. The subjects were
in a supine position in resting state during the measurement, which
lasted for approximately 5.5 – 10 minutes in total. After visual
inspection, the data of six subjects was discarded, as the data of four
subjects had gross movement artefacts and for two subjects the length of
the measurement was insufficient. The final dataset consisted of 50
subjects with mean age of 42.3 ± 15.2 and age range of 23 - 67 (22F,
28M). The signal lengths were unified to the length 5.5 minutes by
selecting signal of corresponding length from the start of the
measurement. It is noteworthy, that the chronological and biological age
of the brain may differ as pathologies, such as schizophrenia, traumatic
brain injury (TBI) and AD, have been shown to affect the aging
trajectory of the brain [23–25]. Thus, although the subjects in the
study were considered as healthy controls and thus the chronological age
is assumed to be similar to the subject’s brain age, the true brain age
is not known. Therefore, a potential undiagnosed pathology could affect
the results of the study.
The used wavelengths of 690 nm, 830 nm and 980 nm were selected due to
the absorption being dominated by HbR, HbO and H2O
respectively in the corresponding wavelength. Additionally, wavelength
of 810 nm was used, which is close to the isobestic point of the
hemoglobin.