Linking brain activation to topological organization in the frontal lobe as a synergistic indicator to characterize the difference between various cognitive processes of executive functions

Irritability Moderates the Association between Cognitive Flexibility Task Performance and Related Prefrontal Cortex Activation in Young Children

The association between cognitive flexibility and related neural functioning has been inconsistent. This is particularly true in young children, where previous studies have found heterogenous results linking behavior and neural function, raising the possibility of unexplored moderators. The current study explored the moderating role of dimensional irritability in the association between cognitive flexibility task performance and prefrontal activation in young children. A total of 106 3- to 7-year-old children were recruited to complete a custom-designed, child-adapted, cognitive flexibility task, and 98 of them were included in the data analysis. The children's dorsolateral prefrontal cortex activation was monitored using functional near-infrared spectroscopy, and their levels of irritability were reported by parents using the MAP-DB Temper Loss subscale. Results indicated that the mean reaction time of the cognitive flexibility task was negatively correlated with concurrent prefrontal activation. No evidence was found for the association between task accuracy and prefrontal activation. Moreover, irritability moderated the association between the mean reaction time and prefrontal activation. Children high in irritability exhibited a stronger negative association between the mean reaction time and related prefrontal activation than children low in irritability. The moderating model suggested a novel affective-cognitive interaction to investigate the associations between cognitive task performance and their neural underpinnings.

Multi-band network fusion for Alzheimer's disease identification with functional MRI

INTRODUCTION

The analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Previous studies usually estimate FBNs using full band Blood Oxygen Level Dependent (BOLD) signal. However, a single band is not sufficient to capture the diagnostic and prognostic information contained in multiple frequency bands.

METHOD

To address this issue, we propose a novel multi-band network fusion framework (MBNF) to combine the various information (e.g., the diversification of structural features) of multi-band FBNs. We first decompose the BOLD signal adaptively into two frequency bands named high-frequency band and low-frequency band by the ensemble empirical mode decomposition (EEMD). Then the similarity network fusion (SNF) is performed to blend two networks constructed by two frequency bands together into a multi-band fusion network. In addition, we extract the features of the fused network towards a better classification performance.

RESULT

To verify the validity of the scheme, we conduct our MBNF method on the public ADNI database for identifying subjects with AD/MCI from normal controls.

DISCUSSION

Experimental results demonstrate that the proposed scheme extracts rich multi-band network features and biomarker information, and also achieves better classification accuracy.

Narcissistic Traits and Executive Functions

Several personality disorders have been associated with cognitive impairment, including executive functions like working memory. Yet, it is unclear whether subclinical expression in non-clinical persons is associated with cognitive functioning. Recent studies indicate that non-clinical subjects might, in fact, perform better with increasing moderate to mild expressions of narcissistic features. We tested working memory performance in a cohort of n=70 psychiatrically and neurologically healthy subjects using Wechsler Adult Intelligence Scale (WAIS/WIE) subtests Arithmetic, Digit Span and Letter-Number Sequencing, and assessed narcissistic features using three different inventories: the widely used Narcissistic Personality Inventory (NPI), as well as two clinically used measures of narcissistic traits and states, respectively, derived from schema-focused therapy, i.e., the Young Schema Questionnaire (YSQ) entitlement/grandiosity subscale and the Schema Mode Inventory (SMI) self-aggrandizer subscale. In accordance with our hypothesis, we found nominally significant positive correlations of WIE Arithmetic performance with NPI total score (Spearman's rho=0.208; p=0.043) and SMI self-aggrandizer scale (Spearman's rho=0.231; p=0.027), but findings did not survive false discovery rate (FDR) adjustment for multiple comparisons (pFDR=0.189 and pFDR=0.243, respectively). While our findings add to recent studies on cognitive performance in subclinical narcissism, they fail to demonstrate an association of cognitive performance with narcissistic traits across multiple working memory tests, indicating the need for additional study, including complementary executive functions in larger cohorts and ranges of phenotype expression.

Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex

While many publications have reported brain hemodynamic responses to decision-making under various conditions of risk, no inventory management scenarios, such as the newsvendor problem (NP), have been investigated in conjunction with neuroimaging. In this study, we hypothesized (I) that NP stimulates the dorsolateral prefrontal cortex (DLPFC) and the orbitofrontal cortex (OFC) joined with frontal polar area (FPA) significantly in the human brain, and (II) that local brain network properties are increased when a person transits from rest to the NP decision-making phase. A 77-channel functional near infrared spectroscopy (fNIRS) system with wide field-of-view (FOV) was employed to measure frontal cerebral hemodynamics in response to NP in 27 healthy human subjects. NP-induced changes in oxy-hemoglobin concentration, Δ[HbO], were investigated using a general linear model (GLM) and graph theory analysis (GTA). Significant activation induced by NP was shown in both DLPFC and OFC+FPA across all subjects. Specifically, higher risk NP with low-profit margins (LM) activated left-DLPFC but deactivated right-DLPFC in 14 subjects, while lower risk NP with high-profit margins (HM) stimulated both DLPFC and OFC+FPA in 13 subjects. The local efficiency, clustering coefficient, and path length of the network metrics were significantly enhanced under NP decision making. In summary, multi-channel fNIRS enabled us to identify DLPFC and OFC+FPA as key cortical regions of brain activations when subjects were making inventory-management risk decisions. We demonstrated that challenging NP resulted in the deactivation within right-DLPFC due to higher levels of stress. Also, local brain network properties were increased when a person transitioned from the rest phase to the NP decision-making phase.

Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy

Significance: Attention-deficit/hyperactivity disorder (ADHD) is the most common psychological disease in childhood. Currently, widely used neuroimaging techniques require complete body confinement and motionlessness and thus are extremely hard for brain scanning of ADHD children. Aim: We present resting-state functional near-infrared spectroscopy (fNIRS) as an imaging technique to record spontaneous brain activity in children with ADHD. Approach: The brain functional connectivity was calculated, and the graph theoretical analysis was further applied to investigate alterations in the global and regional properties of the brain network in the patients. In addition, the relationship between brain network features and core symptoms was examined. Results: ADHD patients exhibited significant decreases in both functional connectivity and global network efficiency. Meanwhile, the nodal efficiency in children with ADHD was also found to be altered, e.g., increase in the visual and dorsal attention networks and decrease in somatomotor and default mode networks, compared to the healthy controls. More importantly, the disrupted functional connectivity and nodal efficiency significantly correlated with dimensional ADHD scores. Conclusions: We clearly demonstrate the feasibility and potential of fNIRS-based connectome technique in ADHD or other neurological diseases in the future.