F75. ALTERED MENTAL STATES DURING RESTING IN PATIENTS WITH SCHIZOPHRENIA AND BIPOLAR DISORDERS

Autor: Jack R. Foucher, Daniel Gounot, Anne Giersch, Patrik Polgári, José Eduardo Marques Carneiro da Silva, Virginie van Wassenhove
Rok vydání: 2019
Předmět:
Zdroj: Schizophrenia Bulletin. 45:S282-S283
ISSN: 1745-1701
0586-7614
DOI: 10.1093/schbul/sbz018.487
Popis: BACKGROUND: Mental states in schizophrenia have been proposed to be fragmented, based on phenomenology and experimental psychology, whereas bipolar disorder would be characterized by racing thoughts and rumination. Here, we explore ‘mental states’ by analyzing configurations of activated brain regions to calculate how long a given mental state lasts, the variability of mental state durations and the recurrence of a given mental state during a long period of time. We present preliminary data for this new type of analysis in healthy volunteers, in patients with schizophrenia and bipolar disorder. METHODS: We report on 13 healthy volunteers, 6 patients with bipolar disorder and 10 patients with schizophrenia. All were scanned during 15 minutes of open eyes Resting State using a multiband fMRI protocol (TR = 0.5 sec). Functional MRI data were realigned, normalized and smoothed using SPM software. We then performed a group Independent Component Analysis (ICA) to identify functional networks (Group ICA of fMRI Toolbox - GIFT). We classified manually all components into subcortical, auditory, somatomotor, visual, cognitive control, default mode network (DMN), cerebellum, insular and hippocampal complex networks, and we excluded all noise related components. We obtained a total of 251 networks among those 9 categories. In the first mental state analysis, we considered the pattern of activation of all functional networks for each scan, in other words, at a given moment t we took the value of each independent component into account. In this sense, a specific mental state pattern is considered as a pattern of activation at a moment t. The next step was to perform a correlation of the mental state pattern of the moment t with all the subsequent scans (t+1, t+2 … t+x). A cutoff of 0.5 in the correlation map was used to define mental state similarity. Based on that correlation map (a 0/1 matrix), we were able to calculate how long a given mental state was lasted, the variance of this duration (mental state variability) and the recurrence of a given pattern of activation (mental state recurrence). In the second mental state analysis, we repeated the same methodology but for each network class to explore if the differences observed in general mental state duration, variability and recurrence are related to specific network classes. RESULTS: No difference was observed regarding mental state duration among the different groups. However, we found a significantly higher variability of mental state duration in patients with schizophrenia when compared to both, healthy (p=0.037) and bipolar individuals (p=0.032). The results regarding mental states related to specific networks show a higher duration variability of mental states in schizophrenia for subcortical (p=0.044 compared to controls; p=0.022 compared to Bipolar patients), visual (p=0.049 compared to bipolar patients) and insular (p=0.005 compared to controls) networks. Regarding the recurrence of mental states, we observed a significantly higher recurrence of DMN (p=0.042 compared to schizophrenia patients) and insular (p=0.023 compared to controls) networks in patients with bipolar disorder. DISCUSSION: The insular network seems to be involved in both pathologies. In patients with schizophrenia it is characterized by an increased variability, and in patients with bipolar disorder by a higher recurrence of mental states. Results are still preliminary, but it is tempting to interpret the difference in mental state variability observed in schizophrenia patients as a correlate of their subjective mental fragmentation. The increased recurrence of mental states observed in patients with bipolar disorder might be related to the rumination observed in patients.
Databáze: OpenAIRE
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