scholarly journals S198. SELF-DISTURBANCES AND THEIR NEURAL SIGNATURES IN SCHIZOPHRENIA

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S114-S114
Author(s):  
Yulia Zaytseva ◽  
Eva Kozakova ◽  
Pavel Mohr ◽  
Filip Spaniel ◽  
Aaron Mishara
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Psychotic Symptoms ◽  
Healthy Controls ◽  
Healthy Individuals ◽  
Motor Speech ◽  
Cognitive Domains ◽  
Aberrant Salience ◽  
First Rank Symptoms

Abstract Background The self-disturbances (SDs) concept is considered to be part of the Schneider’s first rank symptoms, i.e., thought-withdrawal, thought-insertion, thought-broadcasting, somatic-passivity experiences, mental/motor automatisms, disrupted unitary self-experience (Mishara et al., 2014). SDs were originally described by W. Mayer-Gross (1920), who observed them in psychotic patients. Methods We classified Mayer-Gross’ findings on SDs into the following categories: experience is new/compelling (aberrant salience), reduced access/importance of autobiographical past, cognitions/emotions occur independently from self’s volition, foreign agents have power over self and developed an SDs scale based on these categories and cognitive domains (perception, motor, speech, thinking etc.). Scale is applied as a measure of the frequency of the experiences. In our current study on phenomenology and neurobiology of psychotic symptoms, we administered the scale to a study group of patients with schizophrenia (N=84) and healthy volunteers (N=170). Further, the resting state fMRI was performed and the group was divided into two subgroups with (N=13) and without self-disturbances (N=10) and in healthy individuals (N=39). Results We found substantial differences in the frequency of self-disturbances in patients with schizophrenia compared to healthy controls (total score differences, Z=-5.83, p< 0.001). On a neural level, patients with self-disturbances experienced a decreased functional brain connectivity of the default mode and salience networks as compared to the patients without self-disturbances and healthy controls. The differences were mainly explained by the factor ‘’foreign agents’’ and the novelty of the experience. Discussion The scale identifies self-disturbances in schizophrenia and confirms self-related processing in patients with schizophrenia to be associated with altered activation in the cortical midline structures. Supported by the grant projects MH CR AZV 17-32957A and MEYS NPU4NUDZ: LO1611.

scholarly journals S245. BRAIN CONNECTIVITY CHANGES AFTER COGNITIVE REMEDIATION: A RESTING-STATE STUDY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S132-S132
Author(s):  
Rafael Penadés ◽  
Barbara Segura ◽  
Anna Inguanzo ◽  
Clemente Garcia-Rizo ◽  
Rosa Catalán ◽  
...  
Keyword(s):  
Social Skills ◽  
Basal Ganglia ◽  
Resting State ◽  
Social Skills Training ◽  
Brain Connectivity ◽  
Cognitive Remediation ◽  
Skills Training ◽  
Resting State Fmri ◽  
Healthy Controls ◽  
Sensorimotor Network

Abstract Background Some studies have showed how Cognitive Remediation is able to improve activation patterns in the frontal lobe. However, only few data on neuroconnectivity has been reported yet. Resting-state fMRI approach seems to be a promising methodology with potentiality for testing neuroconnectivity. Methods A randomized and controlled trial was carried out with three groups: patients receiving Cognitive Remediation Therapy (CRT), patients receiving Social Skills Training (SST) as an active control, and a healthy control (HC) group. A resting-state fMRI data was acquired in part of the sample (n = 26 patients, n = 10 healthy controls) of a partner study (NCT 02341131). A data-driven approach using independent component analysis (ICA) was used to identify functional brain networks, which were compared between groups and group per time using a dual-regression approach. Results ICA results revealed reduced functional connectivity between patients and controls in sensorimotor, basal ganglia, default mode and visual networks at baseline (p<0.05 FEW-corrected). After treatment, time per group analyses evidenced increased connectivity in sensorimotor network. Furthermore, group comparison at follow-up showed similar connectivity patterns between patients and healthy controls in sensorimotor network, but also in default mode and basal ganglia networks. Discussion Cognitive remediation could be able to strengthen some aspects of brain connectivity networks. Our data could be in line of the hypothesis of disconnectivity in schizophrenia. However, cognitive remediation but also social skills training seemed to be able to induce detectable changes in brain functioning in terms of restoring some aspects on the connectivity pattern.

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

2016 ◽  
Vol 11 ◽  
pp. 302-315 ◽  
Author(s):  
Tingting Xu ◽  
Kathryn R. Cullen ◽  
Bryon Mueller ◽  
Mindy W. Schreiner ◽  
Kelvin O. Lim ◽  
...  
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Network Analysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Borderline Personality ◽  
Functional Brain

Influences of Hunger, Satiety and Oral Glucose on Functional Brain Connectivity: A Multimethod Resting-State fMRI Study

Neuroscience ◽  
2018 ◽  
Vol 382 ◽  
pp. 80-92 ◽  
Author(s):  
Arkan Al-Zubaidi ◽  
Marcus Heldmann ◽  
Alfred Mertins ◽  
Kamila Jauch-Chara ◽  
Thomas F. Münte
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Oral Glucose ◽  
Functional Brain ◽  
Fmri Study

High-Order Functional Connectivity Network for Differential Diagnosis of Depression

2019 ◽  
Vol 9 (6) ◽  
pp. 1095-1102
Author(s):  
Jian Yang ◽  
Xu Mao ◽  
Ning Liu ◽  
Ning Zhong
Keyword(s):  
Time Series ◽  
Differential Diagnosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Time ◽  
Time Window ◽  
Resting State Fmri ◽  
High Order ◽  
Healthy Controls ◽  
Resting State Functional Connectivity

Resting-state functional connectivity (FC) changes dynamically and major depressive disorder (MDD) has abnormality in functional connectivity networks (FCNs), but few existing resting-state fMRI study on MDD utilizes the dynamics, especially for identifying depressive individuals from healthy controls. In this paper, we propose a methodological procedure for differential diagnosis of depression, called HN3D, which is based on high-order functional connectivity networks (HFCN). Firstly, HN3D extracts time series by independent component analysis, and partitions them into overlapped short series by sliding time window. Secondly, it constructs a FCN for each time window and concatenates correlation matrices of all FCNs to generate correlation time series. Then, correlation time series are grouped into different clusters and high-order correlations for HFCN is calculated based on their means. Finally, graph based features of HFCNs are extracted and selected for a linear discriminative classifier. Tested on 21 healthy controls and 20 MDD patients, HN3D achieved its best 100% classification accuracy, which is much higher than results based on stationary FCNs. In addition, most discriminative components of HN3D locate in default mode network and visual network, which are consistent with existing stationary-based results on depression. Though HN3D needs to be studied further, it is helpful for the differential diagnosis of depression and might have potentiality in identifying relevant biomarkers.

Functional brain connectivity in resting-state fMRI using phase and magnitude data

2018 ◽  
Vol 293 ◽  
pp. 299-309 ◽  
Author(s):  
Zikuan Chen ◽  
Arvind Caprihan ◽  
Eswar Damaraju ◽  
Srinivas Rachakonda ◽  
Vince Calhoun
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Functional Brain

scholarly journals Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Chronic Tinnitus ◽  
Healthy Controls ◽  
Tinnitus Distress ◽  
The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

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