(D)
FIGURE 7 Normalized confusion matrices for the best performing
models in the age threshold tests 1 (A), 2 (B) and 3 (C). Due to notable
class imbalance with the age threshold 3 with the best performing model,
the confusion matrix for the best linear SVM model shown additionally
(D).
4 | CONCLUSION
The two key findings of the study are 1) fNIRS has potential to classify
young and old adults to corresponding age groups with good performance,
and 2) the inclusion and exclusion of the subjects in the young,
in-between and old age groups affects the classification performance
significantly, suggesting different aging rate within these three
groups, i.e., young, in-between, and old groups. As the wavelengths used
in our fNIRS device are sensitive to hemoglobin and water dynamics, the
noticed changes are potentially related to the cerebrovascular and
neurohydrodynamic events. However, it is not clear whether water or
hemoglobin change caused effects are more significant. Furthermore, the
fNIRS was measured from Fp1, and thus the detected changes are only
related to the frontal lobe activity and to the hemodynamic and
neurohydrodynamic differences in the brain. The effect of superficial
layers in fNIRS study of brain aging would be of interest to study more
in detail in the future, by utilizing short SD pair in addition to the
long SD pair.
Several factors accelerating and decelerating brain aging process have
been identified in the literature [2]. The existence of these
factors could explain the observed changes in classification
performance, when including the subjects in the in-between age groups.
Although the study population was considered healthy, the brain aging
related accelerative and decelerative factors information, such as
alcohol consumption habits and amount of physical activity per week,
were not considered in this study. The analysis of these factors would
be intriguing, as they may delay or expedite the brain aging process.
Furthermore, inclusion of individuals of age over 70 would be of
interest as it has been suggested that brain aging accelerates
significantly afterwards [36].
Another intriguing prospect is to use fNIRS in the aging study related
to sleep. Recently it was found that the brain’s glymphatic system has
been found to be more active during the sleep [37] and the aging has
been suggested to affect its function negatively [38]. As the system
consists of CSF waste clearance, the use of fNIRS to measure the brain
pulsations, which are one of the drivers of the glymphatic system
[39], and CSF dynamics with water sensitive fNIRS setup could
provide potential for gaining further insight into the function of the
system in humans, and the effects caused by aging. Furthermore, the EEG
of sleep has been successfully utilized in the brain age prediction
task. [28] As EEG measured neural activity and fNIRS measured
hemodynamic response are connected by neurovascular coupling effect, it
could be of interest to study if age-related differences become more
pronounced during sleep.
The key limitation of our study is the used relatively small sample
size, as in general the use of large datasets increases the statistical
power in pattern recognition, [35] and therefore the used datasets
in the brain age studies are usually of large size [40]. It would be
of great interest in the future to conduct fNIRS brain aging studies
with large, well age distributed, sample population to confirm our
findings. Additionally, use of multi-channel fNIRS could give boarded
picture on the global effects of the aging on brain. Although the used
multi-modal measurement setup included different brain measurement
modalities, such as magnetic encephalography (MREG) and EEG [21], in
this study we focused on the analysis of fNIRS and brain aging
specifically due to its novelty in the context. Thus, another area for
future exploration in the fNIRS brain aging studies is to analyse
combination of brain measurement modalities data, such as fNIRS combined
with EEG or MREG. The analysis of different modalities combination would
enhance the information content and provide a comparison of the methods
performance in the brain aging context. However, although suffering from
the lack of data, our study protocol utilized the best practices for ML
research with limited data by utilizing nested cross-validation
framework, with feature and model selection conducted within the inner
loop, for estimating unbiased performance of the models [35].
Additionally, we utilized multiple fNIRS signal features, introduced
previously in the study of brain by different measurement modalities.
The findings of our study demonstrate the high potential for the use of
fNIRS in the study of brain aging.
ACKNOWLEDGMENTS
The authors would like to acknowledge CSC – IT Center for Science,
Finland, for providing the computational resources. The study was
financed by the Academy of Finland Profi6 funding, 6G-Enabling
Sustainable Society (6GESS) programme. Academy of Finland (Terva grant
335723), BF (11146/31/2022), JPND/AoF (357805).
FINANCIAL DISCLOSURE
The authors have no financial conflicts of interest in connection with
this article.
CONFLICT OF INTEREST
The authors have no conflicts of interest in connection with this
article.
DATA AVAILABILITY STATEMENT
Research data are not shared due to the hospital policy.
REFERENCES
[1] Kontis, V., Bennett, J.E., Mathers, C.D., Li, G., Foreman, K.,
Ezzati, M., The Lancet , 2017, 389 , 1323.
[2] Turrini, S., Wong, B., Eldaief, M., Press, D.Z., Sinclair, D.A.,
Koch, G., Avenanti, A., Santarnecchi, E., Ageing Research
Reviews , 2023, 88 , 101939.
[3] Higgins-Chen, A.T., Thrush, K.L., Levine, M.E., Seminars
in Cell and Developmental Biology , 2021, 116 , 180.
[4] Korhonen, V.O., Myllylä, T.S., Kirillin, M.Y., Popov, A.P.,
Bykov, A.V., Gorshkov, A.V., Sergeeva, E.A., Kinnunen, M., Kiviniemi,
V., IEEE Journal of Selected Topics in Quantum Electronics , 2014,20 , 289.
[5] Ferrari, M., Quaresima, V., NeuroImage , 2012,63 , 921.
[6] Boas, D.A., Gaudette, T., Strangman, G., Cheng, X., Marota,
J.J.A., Mandeville, J.B., NeuroImage , 2001, 13 , 76.
[7] Myllylä, T., Harju, M., Korhonen, V., Bykov, A., Kiviniemi, V.,
Meglinski, I., Journal of Biophotonics , 2018, 11 ,
e201700123.
[8] Liang, J., Huang, J., Luo, Z., Wu, Y., Zheng, L., Tang, Z., Li,
W., Ou, H., Frontiers in Human Neuroscience , 2023, 17 .
[9] Nguyen, T., Kim, M., Gwak, J., Lee, J.J., Choi, K.Y., Lee, K.H.,
Kim, J.G., Journal of Biophotonics , 2019, 12 .
[10] Boutouyrie, P., Chowienczyk, P., Humphrey, J.D., Mitchell,
G.F., Circulation Research , 2021, 128 , 864.
[11] Ferdinando, H., Myllylä, T., in Saratov Fall Meeting
2019: Optical and Nano-Technologies for Biology and Medicine , SPIE,
2020, 11457 , 22.
[12] Mohammadi, H., Peng, K., Kassab, A., Nigam, A., Bherer, L.,
Lesage, F., Joanette, Y., Neurobiology of Aging , 2021,106 , 103.
[13] Stojan, R., Mack, M., Bock, O., Voelcker-Rehage, C.,NeuroImage , 2023, 273 .
[14] Huang, W., Li, X., Xie, H., Qiao, T., Zheng, Y., Su, L., Tang,
Z.-M., Dou, Z., Frontiers in Aging Neuroscience , 2022,14 .
[15] Nóbrega-Sousa, P., Gobbi, L.T.B., Orcioli-Silva, D., Conceição,
N.R.D., Beretta, V.S., Vitório, R., Neurorehabilitation and Neural
Repair , 2020, 34 , 915.
[16] Lucas, M., Wagshul, M.E., Izzetoglu, M., Holtzer, R.,Journals of Gerontology - Series A Biological Sciences and Medical
Sciences , 2019, 74 , 435.
[17] Zeller, J.B.M., Katzorke, A., Müller, L.D., Breunig, J.,
Haeussinger, F.B., Deckert, J., Warrings, B., Lauer, M., Polak, T.,
Herrmann, M.J., Brain Imaging and Behavior , 2019, 13 ,
283.
[18] Ferdinando, H., Moradi, S., Korhonen, V., Helakari, H.,
Kiviniemi, V., Myllylä, T., The European Physical Journal Special
Topics , 2022, 232 , 655.
[19] Kober, S.E., Spörk, R., Bauernfeind, G., Wood, G.,Neurobiology of Aging , 2019, 81 , 127.
[20] Ilvesmäki, M., Assessment of human brain ageing by functional
near-infrared spectroscopy. University of Oulu, 2023.
[21] Korhonen, V., Hiltunen, T., Myllylä, T., Wang, X., Kantola, J.,
Nikkinen, J., Zang, Y.-F., LeVan, P., Kiviniemi, V., Brain
Connectivity , 2014, 4 , 677.
[22] H.s.s, S., Myllylä, T.S., Kirillin, M.Y., Sergeeva, E.A.,
Myllylä, R.A., Elseoud, A.A., Nikkinen, J., Tervonen, O., Kiviniemi, V.,Quantum Electronics , 2010, 40 , 1067.
[23] Lieslehto, J., Jääskeläinen, E., Kiviniemi, V., Haapea, M.,
Jones, P.B., Murray, G.K., Veijola, J., Dannlowski, U., Grotegerd, D.,
Meinert, S., Hahn, T., Ruef, A., Isohanni, M., Falkai, P., Miettunen,
J., Dwyer, D.B., Koutsouleris, N., npj Schizophrenia , 2021,7 , 1.
[24] Cole, J.H., Leech, R., Sharp, D.J., Initiative, for the A.D.N.,Annals of Neurology , 2015, 77 , 571.
[25] Löwe, L.C., Gaser, C., Franke, K., Initiative, for the A.D.N.,PLOS ONE , 2016, 11 , e0157514.
[26] Cope, M., The Application of Near Infrared Spectroscopy to Non
Invasive Monitoring of Cerebral Oxygenation in the Newborn Infant.
University College London, 1991.
[27] Helakari, H., Kananen, J., Huotari, N., Raitamaa, L., Tuovinen,
T., Borchardt, V., Rasila, A., Raatikainen, V., Starck, T., Hautaniemi,
T., Myllylä, T., Tervonen, O., Rytky, S., Keinänen, T., Korhonen, V.,
Kiviniemi, V., Ansakorpi, H., NeuroImage: Clinical , 2019,22 , 101763.
[28] Sun, H., Paixao, L., Oliva, J.T., Goparaju, B., Carvalho, D.Z.,
van Leeuwen, K.G., Akeju, O., Thomas, R.J., Cash, S.S., Bianchi, M.T.,
Westover, M.B., Neurobiology of Aging , 2019, 74 , 112.
[29] Ferdinando, H., Moradi, S., Korhonen, V., Kiviniemi, V.,
Myllylä, T., in Neural Imaging and Sensing 2023 , SPIE, 2023,12365 , 18.
[30] Lobier, M., Siebenhühner, F., Palva, S., Palva, J.M.,NeuroImage , 2014, 85 , 853.
[31] Matteo, F., Arjan, H., Phase Transfer Entropy in Matlab.
figshare, 2017.
[32] Richman, J.S., Moorman, J.R., American Journal of
Physiology-Heart and Circulatory Physiology , 2000, 278 , H2039.
[33] Flood, M.W., Grimm, B., PLOS ONE , 2021, 16 ,
e0259448.
[34] Borchardt, V., Korhonen, V., Helakari, H., Nedergaard, M.,
Myllylä, T., Kiviniemi, V., The European Physical Journal Plus ,
2021, 136 , 497.
[35] Vabalas, A., Gowen, E., Poliakoff, E., Casson, A.J., PLOS
ONE , 2019, 14 , e0224365.
[36] Scahill, R.I., Frost, C., Jenkins, R., Whitwell, J.L., Rossor,
M.N., Fox, N.C., Archives of Neurology , 2003, 60 , 989.
[37] Xie, L., Kang, H., Xu, Q., Chen, M.J., Liao, Y., Thiyagarajan,
M., O’Donnell, J., Christensen, D.J., Nicholson, C., Iliff, J.J.,
Takano, T., Deane, R., Nedergaard, M., Science , 2013,342 , 373.
[38] Kress, B.T., Iliff, J.J., Xia, M., Wang, M., Wei, H.S.,
Zeppenfeld, D., Xie, L., Kang, H., Xu, Q., Liew, J.A., Plog, B.A., Ding,
F., Deane, R., Nedergaard, M., Annals of Neurology , 2014,76 , 845.
[39] Bohr, T., Hjorth, P.G., Holst, S.C., Hrabětová, S., Kiviniemi,
V., Lilius, T., Lundgaard, I., Mardal, K.-A., Martens, E.A., Mori, Y.,
Nägerl, U.V., Nicholson, C., Tannenbaum, A., Thomas, J.H., Tithof, J.,
Benveniste, H., Iliff, J.J., Kelley, D.H., Nedergaard, M.,iScience , 2022, 25 .
[40] Cole, J.H., Franke, K., Trends in Neurosciences , 2017,40 , 681.