Real-Time Semi-Automated and Automated Voxel Placement using fMRI Targets for Repeated Acquisition Magnetic Resonance Spectroscopy

James H Bishop; Andrew Geoly; Naushaba Khan; Claudia Tischler; Ruben Krueger; Poorvi Keshava; Heer Amin; Laima Baltusis; Hua Wu; David Spiegel; Nolan Williams; Matthew D Sacchet

doi:10.1016/j.jneumeth.2023.109853

Real-Time Semi-Automated and Automated Voxel Placement using fMRI Targets for Repeated Acquisition Magnetic Resonance Spectroscopy

J Neurosci Methods. 2023 May 15:392:109853. doi: 10.1016/j.jneumeth.2023.109853. Epub 2023 Apr 7.

Authors

Affiliations

¹ Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA.
² Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
³ Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁴ Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA.
⁵ Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: sacchetadmin@mgh.harvard.edu

PMID: 37031764
PMCID: PMC10249508 (available on 2024-05-15)
DOI: 10.1016/j.jneumeth.2023.109853

Abstract

Background: Currently, magnetic resonance spectroscopy (MRS) is dependent on the investigative team to manually prescribe, or demarcate, the desired tissue volume-of-interest. The need for a new method to automate precise voxel placements is warranted to improve the utility and interpretability of MRS data.

New method: We propose and validate robust and real-time methods to automate MRS voxel placement using functionally defined coordinates within the prefrontal cortex. Data were collected and analyzed using two independent prospective studies: 1) two independent imaging days with each consisting of a multi-session sandwich design (MRS data only collected on one of the days determined based on scan time) and 2) a longitudinal design. Participants with fibromyalgia syndrome (N = 50) and major depressive disorder (N = 35) underwent neuroimaging. MRS acquisitions were acquired at 3-tesla. Evaluation of the reproducibility of spatial location and tissue segmentation was assessed for: 1) manual, 2) semi-automated, and 3) automated voxel prescription approaches RESULTS: Variability of voxel grey and white matter tissue composition was reduced using automated placement protocols. Spatially, post- to pre-voxel center-of-gravity distance was reduced and voxel overlap increased significantly across datasets using automated compared to manual procedures COMPARISON WITH EXISTING METHODS: Manual prescription, the current standard in the field, can produce inconsistent data across repeated acquisitions. Using automated voxel placement, we found reduced variability and more consistent voxel placement across multiple acquisitions CONCLUSIONS: These results demonstrate the within subject reliability and reproducibility of a method for reducing variability introduced by spatial inconsistencies during MRS acquisitions. The proposed method is a meaningful advance toward improved consistency of MRS data in neuroscience and can be utilized for multi-session and longitudinal studies.

Keywords: Data acquisition; Longitudinal; Magnetic resonance spectroscopy (MRS); Reliability; Reproducibility; Voxel placement.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Depressive Disorder, Major*
Humans
Magnetic Resonance Imaging* / methods
Magnetic Resonance Spectroscopy / methods
Prospective Studies
Reproducibility of Results

Abstract

Publication types

MeSH terms

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