High-resolution Whole-brain Magnetic Resonance Spectroscopic Imaging in youth at risk for psychosis
Poster No:
1950
Submission Type:
Abstract Submission
Authors:
Edgar Celereau1, Federico Lucchetti1, Martin Cleusix2, Raoul Jenni1, Jean-Baptiste Ledoux3, Meritxell Bach-Cuadra4, Patric Hagmann3, Camille Piguet5, Arnaud Merglen6, Philippe Conus2, Kerstin Jessica Plessen7, Antoine Klauser8, Paul Klauser1
Institutions:
1Center for Psychiatric Neuroscience, CHUV / UNIL, Lausanne, Switzerland, 2Service of General Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland, 3Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland, 4CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 5Department de psychiatrie, Hopitaux Universitaires de Genève, Geneva, Switzerland, 6Departement de pédiatrie, Hopitaux Universitaire de Genève, Genève, Switzerland, 7Service of Child and Adolescents Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland, 8Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
First Author:
Co-Author(s):
Martin Cleusix
Service of General Psychiatry, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Service of General Psychiatry, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Jean-Baptiste Ledoux
Department of Radiology, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Department of Radiology, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Camille Piguet, MD PhD
Department de psychiatrie, Hopitaux Universitaires de Genève
Geneva, Switzerland
Department de psychiatrie, Hopitaux Universitaires de Genève
Geneva, Switzerland
Philippe Conus
Service of General Psychiatry, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Service of General Psychiatry, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Kerstin Jessica Plessen
Service of Child and Adolescents Psychiatry, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Service of Child and Adolescents Psychiatry, Lausanne University Hospital (CHUV)
Lausanne, Switzerland
Introduction:
In vivo assessment of brain metabolites may provide new biomarkers to improve early intervention in psychiatry. However, metabolic quantification and distribution in the brain have long been constrained by poor spatial resolution, suboptimal signal quality or prolonged acquisition times. In this context, we have developed and implemented in our cohort of youth at risk for psychosis a novel proton magnetic resonance spectroscopic imaging (3D-MRSI) technique that allows the mapping of brain metabolites with a high resolution (i.e., 5mm isotropic) in the whole brain (Klauser A et al., 2022). Here, we present the first results of voxel-based analyses (VBA) on metabolic concentration maps for N-acetylaspartate (NAA+NAAG), Myo-inositol (Ins), Choline components (Cho), Glutamine+Glutamate (Glx), Creatine+Phosphocreatine (Cre) to test for between-group differences.
Methods:
Patients (n=21) with age and sex-matched healthy controls (n=13) were recruited from the Lausanne Psychosis Cohort in Switzerland (age 16-33, 30% females). Following the Structured Interview for Prodromal Symptoms (SIPS), all the patients met the criteria for Ultra-High-Risk (UHR) for psychosis or schizotypy (SZT).
To test the replicability of our technique , a second cohort of healthy adolescents was also scanned in Geneva, Switzerland (age 13-15, 57% females).
All participants underwent brain scans with the 3D-MRSI customer sequence and an anatomical T1-weighted sequence, both acquired on a 3T MAGNETOM Prisma (in Lausanne) or Trio (in Geneva), Siemens Healthineers, Forcheim, Germany. The resulting metabolic concentration maps were initially registered to each participant's anatomical image and subsequently transformed into MNI standard space (Avants et al, 2008).
First, mean concentrations of each metabolite were examined in predefined cerebral regions in both cohorts to test for consistency and replicability.
Second, voxel-based analyses (VBA) were performed using a non-parametric cluster-based corrections (significant clusters: p < 0.05) as implemented in FSL randomise (Winkler AM et al, 2014), to test for between-group differences. To address concerns regarding voxels with subthreshold quality, we evaluated two strategies: (1) applying an individual voxel-wise mask as a regressor, and (2) perturbing each voxel value based on a random distribution using the Cramér-Rao lower bound as variance. Both approaches produced concordant results.
To test the replicability of our technique , a second cohort of healthy adolescents was also scanned in Geneva, Switzerland (age 13-15, 57% females).
All participants underwent brain scans with the 3D-MRSI customer sequence and an anatomical T1-weighted sequence, both acquired on a 3T MAGNETOM Prisma (in Lausanne) or Trio (in Geneva), Siemens Healthineers, Forcheim, Germany. The resulting metabolic concentration maps were initially registered to each participant's anatomical image and subsequently transformed into MNI standard space (Avants et al, 2008).
First, mean concentrations of each metabolite were examined in predefined cerebral regions in both cohorts to test for consistency and replicability.
Second, voxel-based analyses (VBA) were performed using a non-parametric cluster-based corrections (significant clusters: p < 0.05) as implemented in FSL randomise (Winkler AM et al, 2014), to test for between-group differences. To address concerns regarding voxels with subthreshold quality, we evaluated two strategies: (1) applying an individual voxel-wise mask as a regressor, and (2) perturbing each voxel value based on a random distribution using the Cramér-Rao lower bound as variance. Both approaches produced concordant results.
Results:
Our findings reveal consistent concentration variations across brain structures in standard space (Figure 1). Owing to the use of two different MRI systems (Prima or Trio 3T Siemens), both absolute and ratio-based metabolite concentrations were assessed.
Across both cohorts, VBA revealed a significantly higher Cho/Cre ratio in male participants relative to females. These two results illustrate the consistency and replicability of our techniques.
In the group of patients at risk for psychosis, we observed an increased NAA+NAAG absolute and relative concentration within gray matter compared to controls (VBA analyses corrected for age and sex) (Figure 2). This elevation in NAA+NAAG could be a potential prognosis biomarker for patients at risk for psychosis since none of our patients did transition to a full psychotic disorder within their 3 years follow-up. Moreover, in a 3-group analysis that compares patients with SZT (n=9), patients with a UHR status (n=12) and controls (n=13), Ins, Glx and Cho were higher in SZT group relative to others. This could suggest higher alterations in more chronic patients.
Across both cohorts, VBA revealed a significantly higher Cho/Cre ratio in male participants relative to females. These two results illustrate the consistency and replicability of our techniques.
In the group of patients at risk for psychosis, we observed an increased NAA+NAAG absolute and relative concentration within gray matter compared to controls (VBA analyses corrected for age and sex) (Figure 2). This elevation in NAA+NAAG could be a potential prognosis biomarker for patients at risk for psychosis since none of our patients did transition to a full psychotic disorder within their 3 years follow-up. Moreover, in a 3-group analysis that compares patients with SZT (n=9), patients with a UHR status (n=12) and controls (n=13), Ins, Glx and Cho were higher in SZT group relative to others. This could suggest higher alterations in more chronic patients.
·Figure 1. Concentrations of brain metabolites through brain regions in the 2 cohorts. (Cohort 1 : Lausanne, Cohort 2: Geneva)
·Figure 2. Voxel-based analysis results of NAA+NAAG concentration in At-risk for psychosis patients versus controls. Displayed clusters have a corrected p value < .05
Conclusions:
These results illustrate the good consistency, replicability and sensitivity of our approach combining 3D-MRSI and VBA to detect neurometabolic differences in health and early psychosis.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Methods Development
Other Methods
Novel Imaging Acquisition Methods:
MR Spectroscopy 1
Keywords:
Magnetic Resonance Spectroscopy (MRS)
MRI
Psychiatric
Psychiatric Disorders
Schizophrenia
Other - Ultra-High-Risk patients; Schizotypy; Voxel-based analysis; Magnetic Resonance Spectroscopic Imaging (MRSI)
1|2Indicates the priority used for review
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Provide references using APA citation style.
Klauser, A., Klauser, P., Grouiller, F., Courvoisier, S., & Lazeyras, F. (2022). Whole‐brain high‐resolution metabolite mapping with 3D compressed‐sensing SENSE low‐rank 1 H FID‐MRSI. NMR in Biomedicine, 35(1). https://doi.org/10.1002/nbm.4615
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92(100), 381–397. https://doi.org/10.1016/j.neuroimage.2014.01.060