Prevalence of misophonia and correlates of Its symptoms among inpatients with depression

Misophonia is an underexplored condition that significantly decreases the quality of life of those who suffer from it. It has neurological and physiological correlates and is associated with a variety of psychiatric symptoms; however, a growing body of data suggests that it is a discrete disorder. While comorbid diagnoses among people with misophonia have been a matter of research interest for many years there is no data on the frequency of misophonia among people with psychiatric disorders. This could be the next step to reveal additional mechanisms underlying misophonia. Until recently, the use of a variety of non-validated questionnaires and the dominance of internet-based studies have been also a major obstacles to a proper definition of misophonia. A total of 94 inpatients diagnosed with depression were assessed for misophonia with face-to-face interviews as well as with MisoQuest - a validated misophonia questionnaire. The prevalence of misophonia among these patients and the congruence of MisoQuest with face-to-face interviews were evaluated. Additionally, the patients filled in a series of questionnaires that measured a variety of psychiatric symptoms and psychological traits. Anxiety, depression, impulsivity, somatic pain, vegetative symptoms, post-traumatic stress disorder (PTSD) symptoms, gender, and age were analyzed in relation to the severity of symptoms of misophonia. Between 8.5 to 12.76% of inpatients with depression were diagnosed with misophonia (depending on measurement and inclusion criteria). MisoQuest accuracy was equal to 92.55%, sensitivity-66.67% and specificity-96.34%. Severity of misophonia symptoms was positively correlated to the greatest extent with anxiety. Moderate positive correlation was also found between severity of misophonia symptoms and depressive symptoms, intrusions, and somatic pain; a weak positive correlation was found between severity of misophonia and non-planning impulsivity, motor impulsivity, avoidance, and vegetative symptoms. There was no relationship between the severity of misophonia symptoms and attentional impulsivity or the age of participants

Machine learning-based identification of suicidal risk in patients with schizophrenia using multi-level resting-state fMRI features

Background: Some studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR. Methods: Fifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine. Results: All groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (p < 0.05). Significant classification ability was also reached for GB and LR using fALFF and ALFF measures. Conclusion Our findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI

Diurnal variations of resting-state fMRI data : a graph-based analysis

Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments

Diurnal variations of resting-state fMRI data : a graph-based analysis

Circadian rhythms (lasting approximately 24 h) control and entrain various physiological processes, ranging from neural activity and hormone secretion to sleep cycles and eating habits. Several studies have shown that time of day (TOD) is associated with human cognition and brain functions. In this study, utilizing a chronotype-based paradigm, we applied a graph theory approach on resting-state functional MRI (rs-fMRI) data to compare whole-brain functional network topology between morning and evening sessions and between morning-type (MT) and evening-type (ET) participants. Sixty-two individuals (31 MT and 31 ET) underwent two fMRI sessions, approximately 1 hour (morning) and 10 h (evening) after their wake-up time, according to their declared habitual sleep-wake pattern on a regular working day. In the global analysis, the findings revealed the effect of TOD on functional connectivity (FC) patterns, including increased small-worldness, assortativity, and synchronization across the day. However, we identified no significant differences based on chronotype categories. The study of the modular structure of the brain at mesoscale showed that functional networks tended to be more integrated with one another in the evening session than in the morning session. Local/regional changes were affected by both factors (i.e., TOD and chronotype), mostly in areas associated with somatomotor, attention, frontoparietal, and default networks. Furthermore, connectivity and hub analyses revealed that the somatomotor, ventral attention, and visual networks covered the most highly connected areas in the morning and evening sessions: the latter two were more active in the morning sessions, and the first was identified as being more active in the evening. Finally, we performed a correlation analysis to determine whether global and nodal measures were associated with subjective assessments across participants. Collectively, these findings contribute to an increased understanding of diurnal fluctuations in resting brain activity and highlight the role of TOD in future studies on brain function and the design of fMRI experiments

Beyond the low frequency fluctuations : morning and evening differences in human brain

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-h-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns. Although the basic mechanism of the 24-h clock is conserved across evolution, there are interindividual differences in the timing of sleep-wake cycles, subjective alertness and functioning throughout the day. The study of circadian typology differences has increased during the last few years, especially research on extreme chronotypes, which provide a unique way to investigate the effects of sleep-wake regulation on cerebral mechanisms. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on resting-state functional connectivity. Twenty-nine extreme morning- and 34 evening-type participants underwent two fMRI sessions: about 1 h after wake-up time (morning) and about 10 h after wake-up time (evening), scheduled according to their declared habitual sleep-wake pattern on a regular working day. Analysis of obtained neuroimaging data disclosed only an effect of time of day on resting-state functional connectivity; there were different patterns of functional connectivity between morning (MS) and evening (ES) sessions. The results of our study showed no differences between extreme morning-type and evening-type individuals. We demonstrate that circadian and homeostatic influences on the resting-state functional connectivity have a universal character, unaffected by circadian typology

Non-linear Functional Brain Co-activations in Short-Term Memory Distortion Tasks

Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 ± 3.56 y.o.) performed in MRscanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the nonlinear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.Fil: Ceglarek, Anna. Jagiellonian University; PoloniaFil: Ochab, Jeremi K.. Jagiellonian University; PoloniaFil: Cifre, Ignacio. Universitat Ramon Llull; EspañaFil: Fafrowicz, Magdalena. Jagiellonian University; PoloniaFil: Sikora Wachowicz, Barbara. Jagiellonian University; PoloniaFil: Lewandowska, Koryna. Jagiellonian University; PoloniaFil: Bohaterewicz, Bartosz. Jagiellonian University; PoloniaFil: Marek, Tadeusz. Jagiellonian University; PoloniaFil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias Físicas. - Universidad Nacional de San Martín. Instituto de Ciencias Físicas; Argentin

Relationship between neurological and cerebellar soft signs, and implicit motor learning in schizophrenia and bipolar disorder

Background: Schizophrenia (SZ) and bipolar disorder (BD) patients share deficits in motor functions in the form of neurological (NSS) and cerebellar soft signs (CSS), and implicit motor learning disturbances. Here, we use cluster analysis method to assess (1) the relationship between those abnormalities in SZ and BD and (2) the differences between those groups. Methods: 33 SZ patients, 33 BD patients as well as 31 healthy controls (HC) took part in the study. We assessed CSS with the International Cooperative Ataxia Rating Scale (ICARS) and NSS with the Neurological Evaluation Scale (NES). Implicit motor learning was evaluated with the Serial Reaction Time Task (SRTT). Participants were divided into clusters (Ward's method) based on the mean response time and mean error rate in SRTT. The difference in ICARS and NES scores, and SRTT variables between clusters were evaluated. We have measured associations between SRTT parameters and both ICARS and NES total scores and subscores. Results: Cluster analysis based on the SRTT parameters allowed to extract three clusters. Those were characterized by the increasing disruption of motor functioning (psychomotor retardation, the severity of NSS and CSS) regardless of the diagnosis. Cluster 1 covered almost all of HC and was characterized by faster reaction times and small number of errors. BD and SZ patients represented in cluster 1, although fully functional in performing the SRTT, showed higher rates of NSS and CSS. Patients with BD and SZ were set apart in clusters 2 and 3 in a similar proportion. Cluster 2 presented significantly slower reaction times but with the comparable number of errors to cluster 1. Cluster 3 consisted of participants with normal or decreased reaction time and significantly increased number of errors. None of the clusters were predominantly composed of the patients representing one psychiatric diagnosis. Conclusions: To our best knowledge, we are presenting the first data indicating the relationship between implicit motor learning and NSS and CSS in SZ and BD patients' groups. Lack of clusters predominantly represented by patients with the diagnosis of SZ or BD may refer to the model of schizophrenia-bipolar disorder boundary, pointing out the similarities between those two disorders

Identifying diurnal variability of brain connectivity patterns using graph theory

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks

Clinical and Psychosocial Characteristics of Adolescent Pediatric Patients Hospitalized after Different Types of Suicidal Behaviors—A Preliminary Study

The objective of this study was to examine the clinical characteristics of adolescents hospitalized after a suicide attempt or instrumental suicide-related behavior. Participants included thirty-six adolescents from the pediatric unit of a Polish hospital who made a nonfatal suicide attempt (SAA) or engaged in instrumental suicide-related behavior (IBA), as well as a general population sample (GPS). Psychosocial features were measured using the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS), the Social and Occupational Functioning Assessment Scale (SOFAS), the Suicide Behaviors Questionnaire&ndash;Revised (SBQ-R), the Psychache Scale (TPS), the State&ndash;Trait Anxiety Inventory (STAI), the Center of Epidemiological Studies Depression Scale for Children (CES-DC), and the Prodromal Questionnaire (PQ-16). The SAA group scored significantly higher than the IBA group and the GPS in modules related to irritability and anhedonia, voice hallucinations and delusions, suicidal acts, thoughts and ideation, and medical lethality. Additionally, the SAA scored higher on the SBQ-R and PQ-16 compared to the IBA group and the GPS. Although anxiety, mental pain, and depressive symptoms could not independently distinguish between the SAA and IBA groups, psychotic symptoms were more frequently present within the SAA group. The above symptoms may be important to consider when screening for suicide risk in the general population