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Review
. 2022 Jan 26;12(1):36.
doi: 10.1038/s41398-022-01787-3.

7T ultra-high-field neuroimaging for mental health: an emerging tool for precision psychiatry?

Irene Neuner #  1   2   3 , Tanja Veselinović #  4   5 , Shukti Ramkiran  4   5 , Ravichandran Rajkumar  4   5   6 , Gereon Johannes Schnellbaecher  4 , N Jon Shah  4   6   7   8
Affiliations
Review

7T ultra-high-field neuroimaging for mental health: an emerging tool for precision psychiatry?

Irene Neuner et al. Transl Psychiatry. .

Abstract

Given the huge symptom diversity and complexity of mental disorders, an individual approach is the most promising avenue for clinical transfer and the establishment of personalized psychiatry. However, due to technical limitations, knowledge about the neurobiological basis of mental illnesses has, to date, mainly been based on findings resulting from evaluations of average data from certain diagnostic groups. We postulate that this could change substantially through the use of the emerging ultra-high-field MRI (UHF-MRI) technology. The main advantages of UHF-MRI include high signal-to-noise ratio, resulting in higher spatial resolution and contrast and enabling individual examinations of single subjects. Thus, we used this technology to assess changes in the properties of resting-state networks over the course of therapy in a naturalistic study of two depressed patients. Significant changes in several network property measures were found in regions corresponding to prior knowledge from group-level studies. Moreover, relevant parameters were already significantly divergent in both patients at baseline. In summary, we demonstrate the feasibility of UHF-MRI for capturing individual neurobiological correlates of mental diseases. These could serve as a tool for therapy monitoring and pave the way for a truly individualized and predictive clinical approach in psychiatric care.

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Conflict of interest statement

None of the authors who contributed to this paper have any disclosures/conflicts of interest to declare. All authors declare the absence of any competing interests or personal financial interests related to the work reported in the manuscript.

Figures

Fig. 1
Fig. 1. Individual changes in the network properties in patient 1.
Individual changes in the network properties in patient 1 are shown as bar plots (A–D) and as network depiction (E–H) for the graph measures for which a significant change could be detected in the second measurement compared to the first one: Global Efficiency (A, E), Local Efficiency (B, F), Betweenness Centrality (C, G), Clustering Coefficient (D, H). aITGr Anterior Temporal Gyrus anterior division right, Hippocampus l Hippocampus left, MedFC Frontal Medial Cortex, AC Anterior Cingulate Gyrus, SPLr Superior Parietal Lobule right, FO l Frontal Operculum left, HG l Heschl’s Gyrus left.
Fig. 2
Fig. 2. Individual changes in the network properties in patient 2.
Individual changes in the network properties in patient 2 are shown as bar plots (A–C) and as a network depiction (D–F) for the graph measures for which a significant change could be detected in the second measurement compared to the first one: Local Efficiency (A, D), Betweenness Centrality (B, E), Clustering Coefficient (C, F). PreCG r Precentral Gyrus Right, Hippocampus r Hippocampus right, Cereb8 l Cerebellum 8 left, HG l Heschl’s Gyrus left, Cereb6 l Cerebellum6 left, Cereb6 r Cerebellum6 right, Ver10 Vermal Lobule 10.
Fig. 3
Fig. 3. Global Efficiency (GE) across all brain regions in patient 1.
A: GE in patient 1 during the first (pre-treatment) scan. B: GE in patient 1 during the second (post-treatment) scan.
Fig. 4
Fig. 4. Betweenness Centrality (BC) across all brain regions in patient 2.
A: BC in patient 2 during the first (pre-treatment) scan. B: BC in patient 2 during the second (post-treatment) scan.
See this image and copyright information in PMC

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