Structural Integrity in the Genu of Corpus Callosum Predicts Conflict-induced Functional Connectivity Between Medial Frontal Cortex and Right Posterior Parietal Cortex

Connectome-based prediction modeling of cognitive control using functional and structural connectivity

BACKGROUND

Cognitive control involves flexibly configuring mental resources and adjusting behavior to achieve goal-directed actions. It is associated with the coordinated activity of brain networks, although it remains unclear how both structural and functional brain networks can predict cognitive control. Connectome-based predictive modeling (CPM) is a powerful tool for predicting cognitive control based on brain networks.

METHODS

The study used CPM to predict cognitive control in 102 healthy adults from the UCLA Consortium for Neuropsychiatric Phenomics dataset and further compared structural and functional connectome characteristics that support cognitive control.

RESULTS

Our results showed that both structural (r values 0.263-0.375) and functional (r values 0.336-0.503) connectomes can significantly predict individuals' cognitive control subcomponents. There is overlap between the functional and structural networks of all three cognitive control subcomponents, particularly in the frontoparietal (FP) and motor (Mot) networks, while each subcomponent also has its own unique weight prediction network. Overall, the functional and structural connectivity that supports different cognitive control subcomponents manifests overlapping and distinct spatial patterns.

CONCLUSIONS

The structural and functional connectomes provide complementary information for predicting cognitive control ability. Integrating information from both connectomes offers a more comprehensive understanding of the neural underpinnings of cognitive control.

Sleep Disorders and Fatigue among Patients with MS: The Role of Depression, Stress, and Anxiety

Sleep disorders and fatigue represent prominent symptoms frequently experienced by individuals with multiple sclerosis (MS). Some psychological factors such as depression, stress, and anxiety seem to have a relationship with such problems. This study aimed to examine the role of depression, stress, and anxiety in predicting sleep disorders and fatigue among patients with MS. Employing a cross-sectional descriptive-correlational design, the study involved a sample size of 252 participants selected through purposive sampling based on inclusion and exclusion criteria. We utilized a demographic information questionnaire along with the Mini-Sleep Questionnaire (MSQ), Fatigue Severity Scale (FSS), and Depression, Anxiety, and Stress Scale (DASS-21) to collect data and analyzed them applying SPSS22, incorporating statistical measures including Pearson correlation and regression. The results of the Pearson correlation coefficient showed that sleep disorders had a positive and significant relationship with depression (r = 0.56; P < 0.001), stress (r = 0.40; P < 0.001), and anxiety (r = 0.52; P < 0.001). There was no significant relationship between age and the development of sleep disorders in total score (r = -0.001; P < 0.985), but age had a relationship with insomnia (r = -0.146; P < 0.021) and oversleeping (r = 0.153; P < 0.015). Age and fatigue did not have a significant relationship as well (r = -0.044; P < 0.941). In addition, fatigue had a positive and significant relationship with depression (r = 0.52; P < 0.001), stress (r = 0.48; P < 0.001), and anxiety (r = 0.54; P < 0.001). The results of the regression analysis also showed that depression, stress, and anxiety predict 0.37% of the total variance of sleep disorders (F = 48.34; P < 0.001) and 0.35% of the total variance of fatigue (F = 44.64; P < 0.001). Our findings suggest that depression, stress, and anxiety play a significant role in predicting sleep disorders and fatigue among patients with MS. This study has been reported in accordance with the TREND checklist for nonrandomized trials.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

Waist Circumference and Its Association With Premenstrual Food Craving: The PHASE Longitudinal Study

Visceral adiposity is a significant marker of all-cause mortality. Reproductive age women are at a considerable risk for developing visceral adiposity; however, the associated factors are poorly understood. The proposed study evaluated whether food craving experienced during the premenstrual period is associated with waist circumference. Forty-six women (mean BMI = 24.36) prospectively provided daily ratings of food craving across two-three menstrual cycles (122 cycles total). Their premenstrual rating of food craving was contrasted against food craving in the follicular phase to derive a corrected summary score of the premenstrual food craving increase. Study groups were divided into normal (n = 26) and obese (n = 20) based on the 80 cm waist circumference cutoff signifying an increase in risk. Waist circumference category was significantly associated with premenstrual food cravings [F _(1,44) = 5.12, p = 0.028]. Post hoc comparisons using the Tukey HSD test (95% family-wise confidence level) showed that the mean score for the food craving effect size was 0.35 higher for the abdominally obese vs. normal study groups (95% CI: 0.039 to 0.67). The result was statistically significant even following inclusion of BMI in the model, pointing to a particularly dangerous process of central fat accumulation. The present study establishes an association between temporal vulnerability to an increased food-related behavior and a marker of metabolic abnormality risk (i.e., waist circumference), thereby forming a basis for integrating the premenstruum as a viable intervention target for this at-risk sex and age group.

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

Target Amplification and Distractor Inhibition: Theta Oscillatory Dynamics of Selective Attention in a Flanker Task

Selective attention is a key mechanism to monitor conflict-related processing and behaviour, by amplifying task-relevant processing and inhibiting task-irrelevant information. Conflict monitoring and resolution is typically associated with brain oscillatory power increase in the theta frequency range (3-8 Hz), as indexed by increased midfrontal theta power. We expand previous findings of theta power increase related to conflict processing and distractor inhibition by considering attentional target amplification to be represented in theta frequency as well. The present study (N = 41) examined EEG oscillatory activities associated with stimulus and response conflict in a lateralized flanker task. Depending on the perceptual (in)congruency and response (in)compatibility of distractor-target associations, resulting stimulus and response conflicts were examined in behavioural and electrophysiological data analyses. Both response and stimulus conflict emerged in RT analysis. Regarding EEG data, response-locked cluster analysis showed an increase of midfrontal theta power related to response conflict. In addition, stimulus-locked cluster analysis revealed early clusters with increased parietal theta power for nonconflicting compared to conflicting trials, followed by increased midfrontal theta power for both stimulus and response conflict. Our results suggest that conflict resolution in the flanker task relies on a combination of target amplification, depicted by parietal theta power increase, and distractor inhibition, indexed by midfrontal theta power increase, for both stimulus and response conflicts. Attentional amplification of sensory target features is discussed with regard to a domain-general conflict monitoring account.

Joint analysis of frontal theta synchrony and white matter following mild traumatic brain injury

Some of the most disabling aspects of mild traumatic brain injury (mTBI) include lingering deficits in executive functioning. It is known that mTBI can damage white matter tracts, but it remains unknown how this structural brain damage translates into cognitive deficits. This experiment utilized theta band phase synchrony to identify the dysfunctional neural operations that contribute to cognitive problems following mTBI. Sub-acute stage (< 2 weeks) mTBI patients (N = 52) and healthy matched controls (N = 32) completed a control-demanding task with concurrent EEG. Structural MRI was also collected. While there were no performance-specific behavioral differences between groups in the dot probe expectancy task, the degree of theta band phase synchrony immediately following injury predicted the degree of symptom recovery two months later. Although there were no differences in fractional anisotropy (FA) between groups, joint independent components analysis revealed that a smaller network of lower FA-valued voxels contributed to a diminished frontal theta phase synchrony network in the mTBI group. This finding suggests that frontal theta band markers of cognitive control are sensitive to sub-threshold structural aberrations following mTBI.

Effect of α7nAChR on learning and memory dysfunction in a rat model of diffuse axonal injury

Diffuse axonal injury (DAI) is the predominant effect of severe traumatic brain injury and significantly contributes to cognitive deficits. The mechanisms that underlie these cognitive deficits are often associated with complex molecular alterations. α7nAChR, one of the abundant and widespread nicotinic acetylcholine receptors (nAChRs) in the brain, plays important physiological functions in the central nervous system. However, the relationship between temporospatial alterations in the α7nAChR and DAI-related learning and memory dysfunction are not completely understood. Our study detected temporospatial alterations of α7nAChR in vulnerable areas (hippocampus, internal capsule, corpus callosum and brain stem) of DAI rats and evaluated the development and progression of learning and memory dysfunction via the Morris water maze (MWM). We determined that α7nAChR expression in vulnerable areas was mainly reduced at the recovery of DAI in rats. Moreover, the escape latency of the injured group increased significantly and the percentages of the distance travelled and time spent in the target quadrant were significantly decreased after DAI. Furthermore, α7nAChR expression in the vulnerable area was significantly positively correlated with MWM performance after DAI according to regression analysis. In addition, we determined that a selective α7nAChR agonist significantly improved learning and memory dysfunction. Rats in the α7nAChR agonist group showed better learning and memory performance than those in the antagonist group. These results demonstrate that microstructural injury-induced alterations of α7nAChR in the vulnerable area are significantly correlated with learning and memory dysfunctions after DAI and that augmentation of the α7nAChR level by its agonist contributes to the improvement of learning and memory function.

"Practice makes perfect?" white matter microstructural characteristic predicts the degree of improvement in within-trial conflict processing across two weeks

Several studies have investigated the trait-like characteristics of conflict processing at different levels. Our study extends these findings by reporting a practice-based improvement in within-trial conflict processing across two sessions. Eighty-three participants performed the same flanker task on two occasions 2 weeks apart. A subset of 37 subjects also underwent diffusion tensor imaging (DTI) scanning the day before the first behavioral task. Despite the trait-like characteristics of conflict processing, within-trial conflict processing in the second behavioral session was significantly shorter than that in the first session, indicating a practice-based improvement in conflict processing. Furthermore, changes in within-trial conflict processing across the two sessions exhibited significant individual differences. Tract-based spatial statistics revealed that the improvement across two sessions was related to the axial diffusivity values in white matter regions, including the body and splenium of the corpus callosum, right superior and posterior corona radiate, and right superior longitudinal fasciculus. Subsequently, lasso regression with leave-one-out cross validation was used to assess the predictive ability of white matter microstructural characteristics in significant regions. The results showed that 61% of individual variability in the improvement in the within-trial conflict processing could be explained by variations in the axial diffusivity values in the four significant regions and the within-trial conflict processing in the first session. These results suggest that axonal morphology in the white tracts connecting conflict-related regions predicts the degree of within-trial conflict processing improvement across two sessions.