Default mode network activity in schizophrenia studied at resting state using probabilistic ICA

• Published in: Schizophrenia research 2012-07, Vol.138 (2), p.143-149
• Main Authors: Mingoia, Gianluca, Wagner, Gerd, Langbein, Kerstin, Maitra, Raka, Smesny, Stefan, Dietzek, Maren, Burmeister, Hartmut P, Reichenbach, Jürgen R, Schlösser, Ralf G.M, Gaser, Christian, Sauer, Heinrich, Nenadic, Igor
• Format: Article
• Language: English
• Subjects: Adult; Adult and adolescent clinical studies; Amygdala; Amygdala - pathology; Amygdala - physiopathology; Biological and medical sciences; Brain Mapping; Case-Control Studies; Default mode network (DMN); Female; Frontal Lobe - pathology; Frontal Lobe - physiopathology; Functional magnetic resonance imaging (fMRI); Functional Neuroimaging; Gyrus Cinguli - pathology; Gyrus Cinguli - physiopathology; Humans; Image Processing, Computer-Assisted; Independent component analysis (ICA); Magnetic Resonance Imaging; Male; Medical sciences; Middle Aged; Models, Statistical; Neural Pathways - pathology; Neural Pathways - physiopathology; Parietal Lobe - pathology; Parietal Lobe - physiopathology; Prefrontal cortex (PFC); Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Psychoses; Resting state; Schizophrenia; Schizophrenia - pathology; Schizophrenia - physiopathology; Temporal Lobe - pathology; Temporal Lobe - physiopathology
• ISSN: 0920-9964; 1573-2509
• DOI: 10.1016/j.schres.2012.01.036

Abstract Alterations in brain function in schizophrenia and other neuropsychiatric disorders are evident not only during specific cognitive challenges, but also from functional MRI data obtained during a resting state. Here we apply probabilistic independent component analysis (pICA) to resting state fMRI series in 25 schizophrenia patients and 25 matched healthy controls. We use an automated algorithm to extract the ICA component representing the default mode network (DMN) as defined by a DMN-specific set of 14 brain regions, resulting in z-scores for each voxel of the (whole-brain) statistical map. While goodness of fit was found to be similar between the groups, the region of interest (ROI) as well as voxel-wise analysis of the DMN showed significant differences between groups. Healthy controls revealed stronger effects of pICA-derived connectivity measures in right and left dorsolateral prefrontal cortices, bilateral medial frontal cortex, left precuneus and left posterior lateral parietal cortex, while stronger effects in schizophrenia patients were found in the right amygdala, left orbitofrontal cortex, right anterior cingulate and bilateral inferior temporal cortices. In patients, we also found an inverse correlation of negative symptoms with right anterior prefrontal cortex activity at rest and negative symptoms. These findings suggest that aberrant default mode network connectivity contributes to regional functional pathology in schizophrenia and bears significance for core symptoms.