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Biol Psychiatry. 2015 Dec 1;78(11):794-804. doi: 10.1016/j.biopsych.2015.02.017. Epub 2015 Feb 24.

In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia.

Author information

1
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Brainnetome Center and National Laboratory of Pattern Recognition (JS, TJ), Institute of Automation, Chinese Academy of Sciences, Beijing, China. Electronic address: jing.sui@nlpr.ia.ac.cn.
2
Olin Neuropsychiatry Research Center, Hartford; Department of Psychiatry, Yale University, New Haven, Connecticut; Department of Neurobiology, Yale University, New Haven, Connecticut.
3
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; School of Information and Communication Engineering, North University of China, Taiyuan, China.
4
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.
5
Departments of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, New Mexico.
6
Brainnetome Center and National Laboratory of Pattern Recognition (JS, TJ), Institute of Automation, Chinese Academy of Sciences, Beijing, China.
7
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Department of Psychiatry, Yale University, New Haven, Connecticut; Departments of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, New Mexico; Electronic and Computer Engineering (VDC), University of New Mexico, Albuquerque, New Mexico.

Abstract

BACKGROUND:

The cognitive deficits of schizophrenia are largely resistant to current treatments and thus are a lifelong illness burden. The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) provides a reliable and valid assessment of cognition across major cognitive domains; however, the multimodal brain alterations specifically associated with MCCB in schizophrenia have not been examined.

METHODS:

The interrelationships between MCCB and the abnormalities seen in three types of neuroimaging-derived maps-fractional amplitude of low-frequency fluctuations (fALFF) from resting-state functional magnetic resonance imaging (MRI), gray matter (GM) density from structural MRI, and fractional anisotropy from diffusion MRI-were investigated by using multiset canonical correlation analysis in data from 47 schizophrenia patients treated with antipsychotic medications and 50 age-matched healthy control subjects.

RESULTS:

One multimodal component (canonical variant 8) was identified as both group differentiating and significantly correlated with the MCCB composite. It demonstrated 1) increased cognitive performance associated with higher fALFF (intensity of regional spontaneous brain activity) and higher GM volumes in thalamus, striatum, hippocampus, and the mid-occipital region, with co-occurring fractional anisotropy changes in superior longitudinal fascicules, anterior thalamic radiation, and forceps major; 2) higher fALFF but lower GM volume in dorsolateral prefrontal cortex related to worse cognition in schizophrenia; and 3) distinct domains of MCCB might exhibit dissociable multimodal signatures, e.g., increased fALFF in inferior parietal lobule particularly correlated with decreased social cognition. Medication dose did not relate to these findings in schizophrenia.

CONCLUSIONS:

Our results suggest linked functional and structural deficits in distributed cortico-striato-thalamic circuits may be closely related to MCCB-measured cognitive impairments in schizophrenia.

KEYWORDS:

Diffusion magnetic resonance imaging (dMRI); Functional magnetic resonance imaging (fMRI); Gray matter; MATRICS Consensus Cognitive Battery (MCCB); Multimodal fusion; Schizophrenia

PMID:
25847180
PMCID:
PMC4547923
DOI:
10.1016/j.biopsych.2015.02.017
[Indexed for MEDLINE]
Free PMC Article

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