A network-based cognitive training induces cognitive improvements and neuroplastic changes in patients with relapsing-remitting multiple sclerosis:an exploratory case-control study

Cognitive impairments are commonly observed in patients with multiple sclerosis and are associated with lower levels of quality of life.No consensus has been reached on how to tackle effectively cognitive decline in this clinical population non-pharmacologically.This exploratory case-control study aims to investigate the effectiveness of a hypothesis-based cognitive training designed to target multiple domains by promoting the synchronous co-activation of different brain areas and thereby improve cognition and induce changes in functional connectivity in patients with relapsing-remitting multiple sclerosis.Forty-five patients(36 females and 9 males,mean age 44.62±8.80 years)with clinically stable relapsing-remitting multiple sclerosis were assigned to either a standard cognitive training or to control groups(sham training and nonactive control).The standard training included twenty sessions of computerized exercises involving various cognitive functions supported by distinct brain networks.The sham training was a modified version of the standard training that comprised the same exercises and number of sessions but with increased processing speed load.The non-active control group received no cognitive training.All patients underwent comprehensive neuropsychological and magnetic resonance imaging assessments at baseline and after 5 weeks.Cognitive and resting-state magnetic resonance imaging data were analyzed using repeated measures models.At reassessment,the standard training group showed significant cognitive improvements compared to both control groups in memory tasks not specifically targeted by the training:the Buschke Selective Reminding Test and the Semantic Fluency test.The standard training group showed reductions in functional connectivity of the salience network,in the anterior cingulate cortex,associated with improvements on the Buschke Selective Reminding Test.No changes were observed in the sham training group.These findings suggest that multi-domain training that stimulates multiple brain areas synchronously may improve cognition in people with relapsing-remitting multiple sclerosis if sufficient time to process training material is allowed.The associated reduction in functional connectivity of the salience network suggests that training-induced neuroplastic functional reorganization may be the mechanism supporting performance gains.This study was approved by the Regional Ethics Committee of Yorkshire and Humber(approval No.12/YH/0474)on November 20,2013.

The Dynamic Relationship of Brain Networks Across Time Windows During Product-Based Creative Thinking

Consensus of creativity research suggests that the measurement of both originality and valuableness is necessary when designing creativity tasks.However,few studies have emphasized valuableness when exploring underlying neural substrates of creative thinking.The present study employs product-based creativity tasks that measure both originality and valuableness in an exploration of the dynamic relationship between the default mode(DMN),executive control(ECN),and salience(SN)networks through time windows.This methodology highlights relevance,or valuableness,in creativity evaluation as opposed to divergent thinking tasks solely measuring originality.The researchers identified seven brain regions belonging to the ECN,DMN,and SN as regions of interest(ROIs),as well as four representative seeds to analyze functional connectivity in 25 college student participants.Results showed that all of the identified ROIs were involved during the creative task.The insula,precuneus,and ventrolateral prefrontal cortex(vlPFC)remained active across all stages of product-based creative thinking.Moreover,the connectivity analyses revealed varied interaction patterns of DMN,ECN,and SN at different thinking stages.The integrated findings of the whole brain,ROI,and connectivity analyses suggest a trend that the DMN and SN(which relate to bottom-up thinking)attenuate as time proceeds,whereas the vlPFC(which relates to top-down thinking)gets stronger at later stages;these findings reflect the nature of our creativity tasks and decision-making of valuableness in later stages.Based on brain region activation throughout execution of the task,we propose that product-based creative process may include three stages:exploration and association,incubation and insight,and finally,evaluation and decision making.This model provides a thinking frame for further research and classroom instruction.

Dynamic Organization of Large-scale Functional Brain Networks Supports Interactions Between Emotion and Executive Control

Emotion and executive control are often conceptualized as two distinct modes of human brain functioning.Little,however,is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control,especially their interactions.The amygdala and prefrontal systems have long been thought to play crucial roles in these processes.Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion,executive control,and their interactions.Here,we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches.In this model,dynamic interactions among the executive control network,the salience network,the default mode network,and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes;neural oscillations across multiple frequency bands and the locus coeruleus−norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks.This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.

Differential aberrant connectivity of precuneus and anterior insula may underpin the diagnosis of schizophrenia and mood disorders

BACKGROUND Over the past decade,resting-state functional magnetic resonance imaging(rsfMRI)has concentrated on brain networks such as the default mode network(DMN),the salience network(SN),and the central executive network(CEN),allowing for a better understanding of cognitive deficits observed in mental disorders,as well as other characteristic psychopathological phenomena such as thought and behavior disorganization.AIM To investigate differential patterns of effective connectivity across distributed brain networks involved in schizophrenia(SCH)and mood disorders.METHODS The sample comprised 58 patients with either paranoid syndrome in the context of SCH(n=26)or depressive syndrome(Ds)(n=32),in the context of major depressive disorder or bipolar disorder.The methods used include rs-fMRI and subsequent dynamic causal modeling to determine the direction and strength of connections to and from various nodes in the DMN,SN and CEN.RESULTS A significant excitatory connection from the dorsal anterior cingulate cortex to the anterior insula(aI)was observed in the SCH patient group,whereas inhibitory connections from the precuneus to the ventrolateral prefrontal cortex and from the aI to the precuneus were observed in the Ds group.CONCLUSION The results delineate specific patterns associated with SCH and Ds and offer a better explanation of the underlying mechanisms of these disorders,and inform differential diagnosis and precise treatment targeting.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Interhemispheric functional connectivity for Alzheimer's disease and amnestic mild cognitive impairment based on the triple network model

The purpose of this study was to explore the differences in interhemispheric functional connectivity in patients with Alzheimer’s disease(AD) and amnestic mild cognitive impairment(aMCI) based on a triple network model consisting of the default mode network(DMN), salience network(SN), and executive control network(ECN). The technique of voxel-mirrored homotopic connectivity(VMHC) analysis was applied to explore the aberrant connectivity of all patients. The results showed that:(1) the statistically significant connections of interhemispheric brain regions included DMN-related brain regions(i.e. precuneus, calcarine, fusiform, cuneus, lingual gyrus, temporal inferior gyrus, and hippocampus), SN-related brain regions(i.e. frontoinsular cortex), and ECN-related brain regions(i.e. frontal middle gyrus and frontal inferior);(2) the precuneus and frontal middle gyrus in the AD group exhibited lower VMHC values than those in the aMCI and healthy control(HC) groups, but no significant difference was observed between the a MCI and HC groups; and(3) significant correlations were found between peak VMHC results from the precuneus and Mini Mental State Examination(MMSE) and Montreal Cognitive Scale(MOCA) scores and their factor scores in the AD, a MCI, and AD plus aMCI groups, and between the results from the frontal middle gyrus and MOCA factor scores in the a MCI group. These findings indicated that impaired interhemispheric functional connectivity was observed in AD and could be a sensitive neuroimaging biomarker for AD. More specifically, the DMN was inhibited, while the SN and ECN were excited. VMHC results were correlated with MMSE and MOCA scores, highlighting that VMHC could be a sensitive neuroimaging biomarker for AD and the progression from aMCI to AD.