Cerebrovascular reactivity (CVR) is increasingly being studied as a biomarker of neurodegenerative diseases. For example, Alzheimer’s disease (AD) is characterized by reduced CVR, indicative of symptom severity[1].

This study, conducted at the Fondazione Santa Lucia (Rome), utilized a Siemens MAGNETOM 3T Prisma scanner (Siemens Healthineers, Germany) with a 32-channel head coil and an in-house built gas delivery system.

A total of 43 participants underwent the MRI session, 12 of which were included in this analysis: 4 healthy controls (64.0±7.4 y), 4 MCI (69.5±1.9 y) and 4 AD (72.3±4.5 y). The session included a functional VASO sequence[2] (nominal resolution=2.0×2.0×3.0 mm3, TR/TE=2700/14.3 ms, flip-angle=30°, inversion-delay=550 ms, CAIPIRINHA=3×1, TA=10 min).

The respiratory paradigm alternated every 2 minutes between medical air and gas mixture (5%CO2, 21%O2, 74%N2) through a face mask, starting with air.

VASO time series were denoised using NORDIC[3], realigned with ASLtbx[4] and separated from the BOLD contrast using the LN_BOCO function[5]. The structural image, used for realignment, was segmented into gray-matter (GM), white-matter (WM) and cerebrospinal-fluid (CSF) using SPM12.

BOLD and VASO time series, along with EtCO2 traces were processed with seeVR[6] to generate CVR and lag maps. BOLD-CVR in GM was (0.13±0.03)%BOLD/mmHg for healthy control, (0.12±0.02)%BOLD/mmHg for MCI, (0.10±0.04)%BOLD/mmHg for AD, while the VASO-CVR was (0.047±0.006)%VASO/mmHg for healthy control, (0.039±0.031)%VASO/mmHg for MCI, (0.034±0.015)%VASO/mmHg for AD.

These finding show the feasibility of simultaneous BOLD and VASO acquisition during respiratory manipulation to assess vascular reactivity to hypercapnia. A trend towards decreased CVR from healthy controls to AD was observed in both contrasts.

References

[1] Nielsen, R.B., Egefjord, L., Angleys, H., Mouridsen, K., Gejl, M., Møller, A., Brock, B., Brændgaard, H., Gottrup, H., Rungby, J. and Eskildsen, S.F., 2017. Capillary dysfunction is associated with symptom severity and neurodegeneration in Alzheimer’s disease. Alzheimer’s & Dementia, 13(10), pp.1143-1153.

[2] Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stöcker, T., Fernández-Cabello, S., Poser, B.A. and Kronbichler, M., 2022. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. bioRxiv, pp.2022-07.

[3] Vizioli, L., Moeller, S., Dowdle, L., Akçakaya, M., De Martino, F., Yacoub, E. and Uğurbil, K., 2021. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nature communications, 12(1), p.5181.

[4] Wang, Z., Aguirre, G.K., Rao, H., Wang, J., Fernández-Seara, M.A., Childress, A.R. and Detre, J.A., 2008. Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx. Magnetic resonance imaging, 26(2), pp.261-269.

[5] Huber, L.R., Poser, B.A., Bandettini, P.A., Arora, K., Wagstyl, K., Cho, S., Goense, J., Nothnagel, N., Morgan, A.T., van den Hurk, J. and Müller, A.K., 2021. LayNii: A software suite for layer-fMRI. NeuroImage, 237, p.118091.

[6] Bhogal, A.A., 2023. abhogal-lab/seeVR: 1.53 (v1.53). Zenodo. https://doi.org/10.5281/zenodo.7816690

Acknowledgments

We would like to kindly thank Claudia Marzi and Marco Clemenzi for radiographic assistance; Sabrina Bonarota, Giulia Caruso, Carlotta Di Domenico for participant recruitment; Steve Gazzitano for setup and maintenance of the gas delivery system; Alex Bhogal for helpful discussion and support with the seeVR toolbox.

Funded by the European Union – Next Generation EU – NRRP M6C2 – Investment 2.1 Enhancement and strengthening of biomedical research in the NHS under the grant PNRR-MAD-2022-1237688.

Figure 1

Caption: Maps of BOLD-based CVR for all the participants included in this analysis. HC: healthy controls; MCI: mild-cognitive impairment; AD: Alzheimer’s disease.

Figure 2

Caption: Maps of VASO-based CVR for all the participants included in this analysis. HC: healthy controls; MCI: mild-cognitive impairment; AD: Alzheimer’s disease.