Creative Connections: Computational Semantic Distance Captures Individual Creativity and Resting-State Functional Connectivity

Recent studies of creative cognition have revealed interactions between functional brain networks involved in the generation of novel ideas; however, the neural basis of creativity is highly complex and presents a great challenge in the field of cognitive neuroscience, partly because of ambiguity around how to assess creativity. We applied a novel computational method of verbal creativity assessment—semantic distance—and performed weighted degree functional connectivity analyses to explore how individual differences in assembly of resting-state networks are associated with this objective creativity assessment. To measure creative performance, a sample of healthy adults ( n = 175) completed a battery of divergent thinking (DT) tasks, in which they were asked to think of unusual uses for everyday objects. Computational semantic models were applied to calculate the semantic distance between objects and responses to obtain an objective measure of DT performance. All participants underwent resting-state imaging, from which we computed voxel-wise connectivity matrices between all gray matter voxels. A linear regression analysis was applied between DT and weighted degree of the connectivity matrices. Our analysis revealed a significant connectivity decrease in the visual-temporal and parietal regions, in relation to increased levels of DT. Link-level analyses showed higher local connectivity within visual regions was associated with lower DT, whereas projections from the precuneus to the right inferior occipital and temporal cortex were positively associated with DT. Our results demonstrate differential patterns of resting-state connectivity associated with individual creative thinking ability, extending past work using a new application to automatically assess creativity via semantic distance.

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Abnormal Spontaneous Brain Activity and Functional Connectivity of the Default Mode Network in Patients with Bipolar Disorder

10.21203/rs.3.rs-194210/v1 ◽

2021 ◽

Author(s):

Lei Zhao ◽

Qijing Bo ◽

Zhifang Zhang ◽

Feng Li ◽

Yuan Zhou ◽

...

Keyword(s):

Bipolar Disorder ◽

Functional Connectivity ◽

Default Mode Network ◽

Resting State ◽

Brain Activity ◽

Seed Region ◽

Potential Candidate ◽

Neural Basis ◽

Default Mode ◽

Spontaneous Brain Activity

Abstract Background: No consistent evidence on the specific brain regions is available in the default mode network (DMN), which show abnormal spontaneous activity in bipolar disorder (BD). We aim to identify this region that is particularly impaired in patients with BD by using several different indices measuring spontaneous brain activity and then investigate its functional connectivity (FC).Methods: A total of 56 patients with BD and 71 healthy controls (HC) underwent resting-state functional magnetic resonance imaging. Three commonly used functional indices were used to identify the brain region showing abnormal spontaneous brain activity in BD. Then, this region served as the seed region for resting-state FC analysis to identify its functional networks altered in BD.Results: The BD group exhibited decreased fALFF, ReHo, and DC values in the left precuneus. The BD group had decreased rsFC within the DMN, indicated by decreased resting-state FC within the left precuneus and between the left precuneus and the medial prefrontal cortex. The BD group had decreased negative connectivity between the left precuneus and the left putamen, extending to the left insula.Conclusions: The findings provide convergent evidence for the abnormalities in the DMN of BD, particularly located in the left precuneus. Decreased FC within the DMN and the disruptive anticorrelation between the DMN and the salience network are found in BD. These findings suggest that the DMN is a key aspect for understanding the neural basis of BD, and the altered functional patterns of DMN may be a potential candidate biomarker of BD.

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Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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Resting State Functional MRI in Neonates with Prenatal Opioid Exposure: Analysis of Thalamocortical Functional Connectivity

Proceedings of IMPRS ◽

10.18060/24612 ◽

2020 ◽

Vol 3 ◽

Author(s):

Zoe Guckien ◽

Jonathan Dietrich ◽

Ramana Vishnubhotla ◽

MaKayla Picklesimer ◽

Christina Sparks ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Public Health Issue ◽

Control Group ◽

Seed Region ◽

Subcortical Structures ◽

Neural Basis ◽

Neurodevelopmental Outcomes ◽

Caudate Nuclei ◽

The Right

Background/Objective: Prenatal opioid exposure (POE) is a growing public health issue that can result in premature birth, Neonatal Abstinence Syndrome (NAS), and adverse neurodevelopmental outcomes. However, the neural basis for these findings remains relatively unknown. In this study, we aimed to investigate the neural correlates of POE based on neonatal thalamocortical functional connectivity using resting state functional magnetic resonance imaging (rs-fMRI). Methods: In this prospective, IRB-approved study, nineteen neonates with POE and twenty opioid naive (ON) controls underwent non-invasive MRI during natural sleep at mean post-menstrual age (PMA) of 44.7 ± 2.6 and 44.6 ± 2.6 weeks respectively. MR imaging included anatomic T2-weighted images and rs-fMRI. General Linear Model (GLM) seed-based whole brain functional connectivity analysis was performed for each subject, with the right and left thalamus as distinct seed regions. Unpaired mixed-effects group analyses between POE and ON groups were conducted for each seed region corrected for PMA and sex. Results: Thalamic connectivity to cortical and subcortical structures differed in the POE group compared to the ON control group. The POE group exhibited higher functional connectivity to deep gray structures, frontal, medial prefrontal, parietal, occipital, and anterior temporal cortices compared to controls. The POE group exhibited lower connectivity to the nuclei accumbentes, bilateral caudate nuclei, posterior cingulate gyri, superior frontal gyri, insular, and dorsolateral prefrontal cortices. Conclusion and Potential Impact: Overall, these novel results suggest the presence of opioid exposure-related alterations in thalamic functional connectivity. Given that the thalamus plays a crucial role in early brain development, the described alterations in thalamocortical and thalamic-subcortical connectivity may have implications in stratifying risk and informing treatment for the adverse neurodevelopmental outcomes associated with POE. Future studies should explore the relationship between POE-associated disruptions in thalamic connectivity and developmental outcomes.

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M158. ASSOCIATIONS OF NEUROLOGICAL SOFT SIGNS AND CEREBELLAR-CEREBRAL FUNCTIONAL CONNECTIVITY IN PATIENTS WITH FIRST-EPISODE SCHIZOPHRENIA AND THEIR UNAFFECTED SIBLINGS

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa030.470 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S196-S196

Author(s):

Xinlu Cai ◽

Yongming Wang ◽

Hanyu Zhou ◽

Jia Huang ◽

Simon S Y Lui ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Sensory Integration ◽

Motor Coordination ◽

Linear Regression Analysis ◽

First Episode ◽

Schizophrenia Spectrum Disorders ◽

Sensorimotor Network ◽

Potential Biomarker ◽

First Episode Schizophrenia

Abstract Background Neurological softs signs (NSS) are defined as subtle neurological abnormalities with manifestations of motor coordination, sensory integration and disinhibition. Evidence has suggested NSS as one of the most promising endophenotypes for schizophrenia spectrum disorders. Moreover, accumulating evidence also suggest that NSS may be associated with specific functional connectivity. The present study aimed to examine the cerebellar-cerebral resting-state functional connectivity (rsFC) of NSS in patients with first-episode schizophrenia (FES) and their unaffected siblings (SB). Methods We administered the abridge version of the Cambridge Neurological Inventory (CNI) to 51 FES patients, 20 unaffected SB, and 50 healthy controls (HC) to assess the severity of NSS. All the participants also underwent a resting-state functional magnetic resonance imaging (MRI) scan. Ten regions of interest (ROIs) in the cerebellum were selected to represent cerebellar motor network (MN) and cerebellar executive control network (EN), which corresponded to the “sensorimotor-cognitive” dichotomy of NSS. rsFC between each ROI and the whole brain voxels were constructed, and the linear regression analysis was conducted to examine the cerebellar-cerebral rsFC patterns of NSS in each group. Results Regarding the cerebellar MN, there were positive correlations observed between the rsFC of the cerebellar MN with the default mode network (DMN) and NSS in FES patients group (CNI total score and the motor coordination subscale) and the SB group (CNI total score and the motor coordination and sensory integration subscales). The rsFC of the cerebellar MN and the sensorimotor network were significantly and positively correlated with NSS (CNI total score and the motor coordination and sensory integration subscales) in the SB group. Regarding the cerebellar EN, we found that both the FES and the SB groups exhibited significantly negative correlations between NSS (CNI total score and the motor coordination subscale) and the rsFC of the cerebellar EN with the DMN. Moreover, the rsFC between the cerebellar EN and the sensorimotor network was positively correlated with NSS (CNI total score and the motor coordination and disinhibition subscales) in the SB group. Discussion We found inverse correlations between NSS and the rsFC of the cerebellar EN/MN and the DMN in both FES patients and their unaffected SB, suggesting that altered cerebellar-cerebral rsFC between these networks is correlated with the NSS. Moreover, the SB group exhibited a unique correlational pattern that NSS were correlated with the cerebellar-sensorimotor network rsFC, suggesting that such a network connectivity may serve as a potential biomarker for schizophrenia.

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Fatigue in multiple sclerosis: The contribution of resting-state functional connectivity reorganization

Multiple Sclerosis Journal ◽

10.1177/1352458517730932 ◽

2017 ◽

Vol 24 (13) ◽

pp. 1696-1705 ◽

Cited By ~ 23

Author(s):

Alvino Bisecco ◽

Federica Di Nardo ◽

Renato Docimo ◽

Giuseppina Caiazzo ◽

Alessandro d’Ambrosio ◽

...

Keyword(s):

Multiple Sclerosis ◽

Regression Analysis ◽

Functional Connectivity ◽

Motor Cortex ◽

Resting State ◽

Primary Motor Cortex ◽

Linear Regression Analysis ◽

Posterior Cingulate Cortex ◽

Resting State Functional Connectivity ◽

Sensorimotor Network

Objectives: To investigate resting-state functional connectivity (RS-FC) of the default-mode network (DMN) and of sensorimotor network (SMN) network in relapsing remitting (RR) multiple sclerosis (MS) patients with fatigue (F) and without fatigue(NF). Methods: In all, 59 RRMS patients and 29 healthy controls (HC) underwent magnetic resonance imaging (MRI) protocol including resting-state fMRI (RS-fMRI). Functional connectivity of the DMN and SMN was evaluated by independent component analysis (ICA). A linear regression analysis was performed to explore whether fatigue was mainly driven by changes observed in the DMN or in the SMN. Regional gray matter atrophy was assessed by voxel-based morphometry (VBM). Results: Compared to HC, F-MS patients showed a stronger RS-FC in the posterior cingulate cortex (PCC) and a reduced RS-FC in the anterior cingulated cortex (ACC) of the DMN. F-MS patients, compared to NF-MS patients, revealed (1) an increased RS-FC in the PCC and a reduced RS-FC in the ACC of the DMN and (2) an increased RS-FC in the primary motor cortex and in the supplementary motor cortex of the SMN. The regression analysis suggested that fatigue is mainly driven by RS-FC changes of the DMN. Conclusions: Fatigue in RRMS is mainly associated to a functional rearrangement of non-motor RS networks.

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Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence

10.31234/osf.io/tzd3w ◽

2021 ◽

Author(s):

Roberto A. Abreu-Mendoza ◽

Melanie Pincus ◽

Yaira Chamorro ◽

Dietsje Jolles ◽

Esmeralda Matute ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Math Achievement ◽

Resting State Fmri ◽

Brain Regions ◽

Math Performance ◽

Resting State Functional Connectivity ◽

Neural Basis ◽

Math Ability ◽

Control Seed

Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age=16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporooccipital regions, were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as connectivity of Heschl’s gyrus, a control seed not involved in math cognition, was not modulated by math performance. Together, our results point to adolescence as a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability in this period.

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Multivoxel Neural Reinforcement Changes Resting-State Functional Connectivity Within the Threat Regulation Network

10.1101/2020.04.03.021956 ◽

2020 ◽

Cited By ~ 1

Author(s):

Vincent Taschereau-Dumouchel ◽

Toshinori Chiba ◽

Ai Koizumi ◽

Mitsuo Kawato ◽

Hakwan Lau

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Temporal Cortex ◽

Physiological Reactivity ◽

Resting State Functional Connectivity ◽

Double Blind ◽

Brain Responses ◽

Regulation Network ◽

Before And After ◽

Ventral Temporal Cortex

AbstractUsing neural reinforcement, participants can be trained to pair a reward with the activation of specific multivoxel patterns in their brains. In a double-blind placebo-controlled experiment, we previously showed that this intervention can decrease the physiological reactivity associated with naturally feared animals. However, the mechanisms behind the effect remain incompletely understood and its usefulness for treatment remains unclear. If the intervention fundamentally changed the brain responses, we might expect to observe relatively stable changes in the functional connectivity within the threat regulation network. To evaluate this possibility, we conducted functional magnetic resonance imaging (fMRI) sessions while subjects were at rest, before and after neural reinforcement, and quantified the changes in resting-state functional connectivity accordingly. Our results indicate that neural reinforcement increased the connectivity of prefrontal regulatory regions with the amygdala and the ventral temporal cortex (where the visual representations of phobic targets are). Surprisingly, we found no evidence of Hebbian-like learning during neural reinforcement, contrary to what one may expect based on previous neurofeedback studies. These results suggest that multivoxel neural reinforcement, also known as decoded neurofeedback (DecNef), may operate via unique mechanisms, distinct from those involved in conventional neurofeedback.

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Dysbalanced Resting-State Functional Connectivity Within the Praxis Network Is Linked to Gesture Deficits in Schizophrenia

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa008 ◽

2020 ◽

Vol 46 (4) ◽

pp. 905-915 ◽

Cited By ~ 2

Author(s):

Florian Wüthrich ◽

Petra V Viher ◽

Katharina Stegmayer ◽

Andrea Federspiel ◽

Stephan Bohlhalter ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Superior Temporal Gyrus ◽

Brain Regions ◽

Precentral Gyrus ◽

Healthy Controls ◽

Resting State Functional Connectivity ◽

Neural Basis ◽

Course Of Illness ◽

Parietal Lobule

Abstract Patients with schizophrenia frequently present deficits in gesture production and interpretation, greatly affecting their communication skills. As these gesture deficits can be found early in the course of illness and as they can predict later outcomes, exploring their neural basis may lead to a better understanding of schizophrenia. While gesturing has been reported to rely on a left lateralized network of brain regions, termed praxis network, in healthy subjects and lesioned patients, studies in patients with schizophrenia are sparse. It is currently unclear whether within-network connectivity at rest is linked to gesture deficit. Here, we compared the functional connectivity between regions of the praxis network at rest between 46 patients and 44 healthy controls. All participants completed a validated test of hand gesture performance before resting-state functional magnetic resonance imaging (fMRI) was acquired. Patients performed gestures poorer than controls in all categories and domains. In patients, we also found significantly higher resting-state functional connectivity between left precentral gyrus and bilateral superior and inferior parietal lobule. Likewise, patients had higher connectivity from right precentral gyrus to left inferior and bilateral superior parietal lobule (SPL). In contrast, they exhibited lower connectivity between bilateral superior temporal gyrus (STG). Connectivity between right precentral gyrus and left SPL, as well as connectivity between bilateral STG, correlated with gesture performance in healthy controls. We failed to detect similar correlations in patients. We suggest that altered resting-state functional connectivity within the praxis network perturbs correct gesture planning in patients, reflecting the gesture deficit often seen in schizophrenia.

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Belief in Communism and Theory of Mind

Frontiers in Psychology ◽

10.3389/fpsyg.2021.697251 ◽

2021 ◽

Vol 12 ◽

Author(s):

Outong Chen ◽

Fang Guan ◽

Yu Du ◽

Yijun Su ◽

Hui Yang ◽

...

Keyword(s):

Functional Connectivity ◽

Theory Of Mind ◽

Resting State ◽

Brain Activity ◽

Neural Correlates ◽

Resting State Functional Connectivity ◽

Neural Basis ◽

Spontaneous Brain Activity ◽

Multiple Regions ◽

Left Thalamus

A belief in communism refers to the unquestionable trust and belief in the justness of communism. Although former studies have discussed the political aim and social value of communism, the cognitive neural basis of a belief in communism remains largely unknown. In this study, we determined the behavioral and neural correlates between a belief in communism and a theory of mind (ToM). For study 1, questionnaire scores were measured and for study 2, regional homogeneity (ReHo) and resting-state functional connectivity (rsFC) were used as an index for resting-state functional MRI (rs-fMRI), as measured by the Belief in Communism Scale (BCS). The results showed that a belief in communism is associated with higher ReHo in the left thalamus and lower ReHo in the left medial frontal gyrus (MFG). Furthermore, the results of the rsFC analysis revealed that strength of functional connectivity between the left thalamus and the bilateral precuneus is negatively associated with a belief in communism. Hence, this study provides evidence that spontaneous brain activity in multiple regions, which is associated with ToM capacity, contributes to a belief in communism.

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Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.667881 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ayumu Yamashita ◽

Yuki Sakai ◽

Takashi Yamada ◽

Noriaki Yahata ◽

Akira Kunimatsu ◽

...

Keyword(s):

Major Depressive Disorder ◽

Functional Connectivity ◽

Depressive Disorder ◽

Prediction Model ◽

Resting State ◽

Validation Dataset ◽

Depression Symptoms ◽

Resting State Functional Connectivity ◽

Neural Basis ◽

Major Depressive

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

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