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BACKGROUND: The optimal stimulation for brain development in the early academic years remains unclear. Current research suggests that musical training has a more profound impact on children's executive functions (EF) compared to other art forms. What is crucially lacking is a large-scale, long-term genuine randomized controlled trial (RCT) in cognitive neuroscience, comparing musical instrumental training (MIP) to another art form, and a control group (CG). This study aims to fill this gap by using machine learning to develop a multivariate model that tracks the interconnected brain and EF development during the academic years, with or without music or other art training. METHODS: The study plans to enroll 150 children aged 6-8 years and randomly assign them to three groups: Orchestra in Class (OC), Visual Arts (VA), and a control group (CG). Anticipating a 30% attrition rate, each group aims to retain at least 35 participants. The research consists of three analytical stages: 1) baseline analysis correlating EF, brain data, age, gender, and socioeconomic status, 2) comparison between groups and over time of EF brain and behavioral development and their interactions, including hypothesis testing, and 3) exploratory analysis combining behavioral and brain data. The intervention includes intensive art classes once a week, and incremental home training over two years, with the CG receiving six annual cultural outings. DISCUSSION: This study examines the potential benefits of intensive group arts education, especially contrasting music with visual arts, on EF development in children. It will investigate how artistic enrichment potentially influences the presumed typical transition from a more unified to a more multifaceted EF structure around age eight, comparing these findings against a minimally enriched active control group. This research could significantly influence the incorporation of intensive art interventions in standard curricula. TRIAL REGISTRATION: The project was accepted after peer-review by the Swiss National Science Foundation (SNSF no. 100014_214977) on March 29, 2023. The study protocol received approval from the Cantonal Commission for Ethics in Human Research of Geneva (CCER, BASEC-ID 2023-01016), which is part of Swiss ethics, on October 25, 2023. The study is registered at clinicaltrials.gov (NCT05912270).

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Previous research has suggested that certain types of the affective temperament, including depressive, cyclothymic, hyperthymic, irritable, and anxious, are subclinical manifestations and precursors of mental disorders. However, the neural mechanisms that underlie these temperaments are not fully understood. The aim of this study was to identify the brain regions associated with different affective temperaments. We collected the resting-state functional magnetic resonance imaging (fMRI) data from 211 healthy adults and evaluated their affective temperaments using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire. We used intersubject representational similarity analysis to identify brain regions associated with each affective temperament. Brain regions associated with each affective temperament were detected. These regions included the prefrontal cortex, anterior cingulate cortex (ACC), precuneus, amygdala, thalami, hippocampus, and visual areas. The ACC, lingual gyri, and precuneus showed similar activity across several affective temperaments. The similarity in related brain regions was high among the cyclothymic, irritable, and anxious temperaments, and low between hyperthymic and the other affective temperaments. These findings may advance our understanding of the neural mechanisms underlying affective temperaments and their potential relationship to mental disorders and may have potential implications for personalized treatment strategies for mood disorders.

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When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.

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Background: Fatigue is one of the most common symptoms of multiple sclerosis (MS), significantly affecting the functioning of the patients. However, the neural underpinnings of physical and mental fatigue in MS are still vague. The aim of our study was to investigate the functional architecture of resting-state networks associated with fatigue in patients with MS. Methods: The sum of 107 high-functioning patients underwent a resting-state scanning session and filled out the 9-item Fatigue Severity Scale (FSS). Based on the FSS score, we identified patients with different levels of fatigue using the cluster analysis. The low-fatigue group consisted of n = 53 subjects, while the high-fatigue group n = 48. The neuroimaging data were analyzed in terms of functional connectivity (FC) between various resting-state networks as well as amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF). Results: Two-sample t-test revealed between-group differences in FC of posterior salience network (SN). No differences occurred in default mode network (DMN) and sensorimotor network (SMN). Moreover, differences in fALFF were shown in the right middle frontal gyrus and right superior frontal gyrus, however, no ALFF differences took place. Conclusion: Current study revealed significant functional network (FN) architecture between-group differences associated with fatigue. Present results suggest the higher level of fatigue is related to deficits in awareness as well as higher interoceptive awareness and nociception.

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Wistar-Kyoto (WKY) rats as an endogenous depression model partially lack a response to classic selective serotonin reuptake inhibitors (SSRIs). Thus, this strain has the potential to be established as a model of treatment-resistant depression (TRD). However, the SSRI resistance in WKY rats is still not fully understood. In this study, WKY and control rats were subjected to a series of tests, namely, a forced swim test (FST), a sucrose preference test (SPT), and an open field test (OFT), and were scanned in a 7.0-T MRI scanner before and after three-week citalopram or saline administration. Behavioral results demonstrated that WKY rats had increased immobility in the FST and decreased sucrose preference in the SPT and central time spent in the OFT. However, citalopram did not improve immobility in the FST. The amplitude of low-frequency fluctuation (ALFF) analysis showed regional changes in the striatum and hippocampus of WKY rats. However, citalopram partially reversed the ALFF value in the dorsal part of the two regions. Functional connectivity (FC) analysis showed that FC strengths were decreased in WKY rats compared with controls. Nevertheless, citalopram partially increased FC strengths in WKY rats. Based on FC, global graph analysis demonstrated decreased network efficiency in WKY + saline group compared with control + saline group, but citalopram showed weak network efficiency improvement. In conclusion, resting-state fMRI results implied widely affected brain function at both regional and global levels in WKY rats. Citalopram had only partial effects on these functional changes, indicating a potential treatment resistance mechanism.

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Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

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To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.

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Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.

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PURPOSE: To investigate cerebral amplitude of low-frequency fluctuations (ALFF) changes during a single hemodialysis (HD) in end-stage renal disease (ESRD) patients who need maintenance HD. MATERIALS AND METHODS: A total of 24 patients and 27 healthy subjects were included. The patients underwent neuropsychological tests and took twice resting-state fMRI (rs-fMRI) (before and after HD). Healthy group had one rs-fMRI. The zALFF based on rs-fMRI was calculated. Paired t and independent t test was applied to compare zALFF among groups. The associations between zALFF and duration of HD, ultrafiltration volume, and neuropsychological tests was calculated by partial correlation. RESULTS: Compared to healthy group, patients before HD showed significant worse performances on digit symbol test (DST) and serial dotting test (SDT). Patients after HD performed DST better than before HD. The patients after HD showed higher zALFF in left putamen than before HD. Multiple regions of both HD groups showed significant lower zALFF than healthy group. The zALFF of left putamen of patients after HD was significant negative correlated with the ultrafiltration volume (R = -0.679). The zALFF in patients before HD exhibited significantly positive or negative correlations with DST and SDT in multiple regions. The zALFF of patients after HD significantly negative correlated with DST in right temporal, positive and negative correlated with ultrafiltration volume in right frontal, left putamen respectively. CONCLUSION: ESRD patients showed changed spontaneous brain activity and cognitive impairments. After a single HD session, patients performed better in neuropsychological test, and spontaneous brain activity changed in left putamen. Ultrafiltration volume might be associated with activity of left putamen.

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Several previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has rarely been considered in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon's entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.

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Magnetic resonance imaging (MRI) has the potential to serve as a biomarker for Parkinson's disease (PD). However, the type or types of biomarker it could provide remain to be determined. At this time there is not sufficient sensitivity or specificity for MRI to serve as an early diagnostic biomarker, i.e., it is unproven in its ability to determine if a single individual is normal, has mild PD, or has some other forms of degenerative parkinsonism. However there is accumulating evidence that MRI may be useful in staging and monitoring disease progression (staging biomarker), and also possibly as a means to monitor pathophysiological aspects of disease and associated response to treatments, i.e., theranostic marker. As there are increasing numbers of manuscripts that are dedicated to diffusion- and neuromelanin-based imaging methods, this review will focus on these topics cursorily and will delve into pharmacodynamic imaging as a means to get at theranostic aspects of PD.

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BACKGROUND: Previous empirical research has treated regional neural responses and network architecture separately. However, anecdotal reports have suggested a close relationship between the two. This study aims to investigate the influence of structural connectivity on regional spontaneous activities. METHODS: Datasets of structural magnetic resonance imaging (sMRI), resting state functional MRI (rs-fMRI) and diffusion weighted imaging (DWI) of 36 right-handed healthy subjects (average age 27.4) were selected from the NKI Rockland sample. In the sMRI data, the cerebral cortex was parcellated into 70 regions of interest (ROIs) according to an anatomical atlas. Two indices were calculated from rs-fMRI for each ROI: the regional homogeneity (ReHo) and the amplitude of low frequency fluctuation (ALFF). Diffusion tensor imaging was computed from DWI and was converted to tractography. Four graph indices of structural connectivity were retrieved from the tractography results and the 70 ROIs, as follows: nodal degree, clustering coefficient, local efficiency and betweenness centrality. RESULTS: ReHo values were significantly correlated with all 4 graph features, whereas ALFF values were significantly correlated with nodal degrees and clustering coefficients. Both ReHo and ALFF tended to increase with segregation (clustering coefficient and local efficiency) and decrease with centrality (nodal degree and betweenness centrality). DISCUSSION: Though derived from local spontaneous activities, ReHo and ALFF may reflect the network properties of the underlying anatomical architecture. The results supported the hypothesis that the properties of the network structure may shape the regional neural response profiles.

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OBJECT: To study memory impairment mechanisms of the medial temporal lobe epilepsy (MTLE) by analyzing the functional connectivity (FC) through resting state functional magnetic resonance imaging (rs-fMRI). METHODS: Rs-fMRI data were acquired from 13 patients with left MTLE and 11 patients with right MTLE. Another 13 healthy volunteers were selected as controls. The altered FC pattern between the unilateral hippocampus and other regions of the brain in MTLE patients was compared to that of the normal control group. Then the correlation between the strength of FC and the clinical memory scale scores in patients with MTLE was determined. RESULTS: The scores of the following check points of MTLE patients including point to memory, recognition of nonsense figure, associative learning, the image free recall, portrait characteristic recall and memory quotient were significantly lower than those in the normal group (all P<0.05). The scores of point to memory and recognition of nonsense figure were different between R-MTLE and L-MTLE patients. Different correlations between the strength of FC and the clinical memory scale scores were detected between R-MTLE and L-MTLE patients. CONCLUSIONS: The memory function of patients with MTLE was impaired. Patients with L-MTLE showed lower score on pure verbal memory test and those with R-MTLE showed weaker performance on pure visual memory test. Patients with MTLE showed extensive abnormal FC between hippocampus and particular encephalic regions.

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