University at Buffalo - The State University of New York
Skip to Content
Prefrontal neuronal integrity predicts symptoms and cognition in schizophrenia and is sensitive to genetic heterogeneity. - PubMed - NCBI
Format

Send to

Choose Destination
See comment in PubMed Commons below
Schizophr Res. 2016 Apr;172(1-3):94-100. doi: 10.1016/j.schres.2016.02.031. Epub 2016 Feb 28.

Prefrontal neuronal integrity predicts symptoms and cognition in schizophrenia and is sensitive to genetic heterogeneity.

Author information

1
Institute for Social and Psychiatric Initiatives (InSPIRES), Department of Psychiatry, New York University School of Medicine, NY, NY, USA. Electronic address: dolores.malaspina@nyumc.org.
2
Skirball Institute of Biomolecular Medicine, Departments of Cell Biology, Physiology & Neuroscience and Psychiatry, New York University, New York, NY 10016, USA.
3
Genome Technology Center, New York University School of Medicine, NY, NY, USA.
4
Department of Psychiatry, New York University School of Medicine, NY, NY, USA.
5
Institute for Social and Psychiatric Initiatives (InSPIRES), Department of Psychiatry, New York University School of Medicine, NY, NY, USA.
6
University at Buffalo, Department of Psychiatry, Buffalo, NY, USA.
7
New York State Psychiatric Institute, Division of Clinical Phenomenology, 1051 Riverside Drive, NY, NY, USA; Columbia University, Department of Psychiatry, NY, NY, USA.
8
Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, NY, NY, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, NY, NY, USA.

Abstract

Schizophrenia is a genetically complex syndrome with substantial inter-subject variability in multiple domains. Person-specific measures to resolve its heterogeneity could focus on the variability in prefrontal integrity, which this study indexed as relative rostralization within the anterior cingulate cortex (ACC). Twenty-two schizophrenia cases and 11 controls underwent rigorous diagnostic procedures, symptom assessments (PANSS, Deficit Syndrome Scale) and intelligence testing. All underwent multivoxel MRSI at 3T to measure concentrations of the neuronal-specific biomarker N-acetylaspartate (NAA) in all of the voxels of the ACC. The concentrations of NAA were separately calculated and then compared across the rostral and caudal subregions to generate a rostralization ratio, which was examined with respect to the study measures and to which cases carried a missense coding polymorphism in PTPRG, SCL39A13, TGM5, NTRK1 or ARMS/KIDINS220. Rostralization significantly differed between cases and controls (χ(2)=18.40, p<.0001). In cases, it predicted verbal intelligence (r=.469, p=.043) and trait negative symptoms (diminished emotional range (r=-.624, p=.010); curbed interests, r=-.558, p=.025). Rostralization was similar to controls for missense coding variants in TGM5 and was significantly greater than controls for the PTPRG variant carrier. This is the first study examining the utility of MRS metrics in describing pathological features at both group and person-specific levels. Rostralization predicted core illness features and differed based on which signaling genes were disrupted. While future studies in larger populations are needed, ACC rostralization appears to be a promising measure to reduce the heterogeneity of schizophrenia for genetic research and selecting cases for treatment studies.

KEYWORDS:

Anterior cingulate cortex; Genotype; Magnetic resonance spectroscopy imaging; N-acetylaspartate; Rostralization; Schizophrenia

PMID:
26925801
PMCID:
PMC4894496
DOI:
10.1016/j.schres.2016.02.031
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science Icon for PubMed Central
    Loading ...
    Support Center