Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia

Abstract

IntroductionPatients with schizophrenia exhibit several cognitive deficits, including memory impairment. Problems with recognition memory can hinder socially adaptive behavior. Previous investigations have suggested that altered activation of the frontotemporal area plays an important role in recognition memory impairment. However, the cerebral networks related to these deficits are not known. The aim of this study was to elucidate the brain networks required for recognizing socially relevant information in patients with schizophrenia performing an old–new recognition task.MethodsSixteen patients with schizophrenia and 16 controls participated in this study. First, the subjects performed the theme‐identification task during functional magnetic resonance imaging. In this task, pictures depicting social situations were presented with three words, and the subjects were asked to select the best theme word for each picture. The subjects then performed an old–new recognition task in which they were asked to discriminate whether the presented words were old or new. Task performance and neural responses in the old–new recognition task were compared between the subject groups. An independent component analysis of the functional connectivity was performed.ResultsThe patients with schizophrenia exhibited decreased discriminability and increased activation of the right superior temporal gyrus compared with the controls during correct responses. Furthermore, aberrant network activities were found in the frontopolar and language comprehension networks in the patients.ConclusionsThe functional connectivity analysis showed aberrant connectivity in the frontopolar and language comprehension networks in the patients with schizophrenia, and these aberrations possibly contribute to their low recognition performance and social dysfunction. These results suggest that the frontopolar and language comprehension networks are potential therapeutic targets in patients with schizophrenia.

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

“…Finally, 19 components were identified as functional networks or subnetworks. Similar networks were used in our previous study (Oh et al, 2017 ). Figure 2 depicts some of the 19 selected components; they represent the well-known resting state functional networks: the default mode network, sensorimotor network, visual network, and frontal network.…”

“…This disorder can cause chronic impairments in cognition, perception, reality testing, and emotion among others (Bhugra, 2005 ). Its underlying mechanism is still unclear, even though it is considered to involve structural and functional brain abnormalities (Ho et al, 2003 ; Karlsgodt et al, 2010 ; Oh et al, 2015 , 2017 ). Schizophrenia is usually diagnosed on the basis of history and mental status examination by psychiatrists under the guidelines of specified diagnostic criteria.…”

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

“…Schizophrenia is a severe psychiatric disorder, characterized by delusions, hallucinations and disorganized thinking (Hu et al, ), which affects about 1% of the world's population (Dhindsa & Goldstein, ; Lieberman, Scott Stroup, & Perkins, ; Nowak, Sabariego, Switaj, & Anczewska, ; Rajji, Miranda, & Mulsant, ). Its etiology and neuropathology are not well understood, even though neuroimaging studies have revealed distributed structural and functional brain abnormalities (Karlsgodt, Sun, & Cannon, ; Oh et al, ; Oh et al, ). Schizophrenia is usually diagnosed by a clinical examination carried out by psychiatrists.…”

“…Each dataset comprised high‐resolution T1‐weighted images and rs‐fMRI, allowing us to examine classification accuracy for each modality as well as their integration. Because schizophrenia is considered to involve structural as well as functional brain abnormalities (Karlsgodt et al, ; Oh et al, ; Oh et al, ), we hypothesized that (a) both structural and functional data would allow single‐subject classification with statistically significant accuracy. In addition, given the existing evidence for both regional and network‐level alterations in schizophrenia (Y. Liu et al, ; Micheloyannis et al, ; Shen, Wang, Liu, & Hu, ), we hypothesized that, within each modality, (b) the combination of voxel‐wise images and connectome‐wide based matrices would be superior to the use of either voxel‐wise images or connectome‐wide based matrices by themselves.…”

“…Because schizophrenia is considered to involve structural as well as functional brain abnormalities (Karlsgodt et al, 2010;Oh et al, 2015;Oh et al, 2017), we hypothesized that (a) both structural and functional data would allow single-subject classification with statistically significant accuracy. In addition, given the existing evidence for both regional and network-level alterations in schizophrenia (Y.…”