Brain Connectivity: Neurocognitive Involvement in COVID-19

Hierarchical Microstructure Informed Tractography

Despite this unique and compelling ability and its wide range of possible neurological applications, tractography still lacks anatomical precision. For this reason, in the past few years, tractography post-processing techniques have emerged and the Convex Optimization Modelling for Microstructure Informed Tractography formulation is one of them which allows incorporating the anatomical prior that fibres are naturally organized in fascicles, and has obtained exceptional results in increasing the accuracy of the estimated tractograms.

Mario Ocampo-Pineda and Alessandro Daducci along with their colleagues proposed an extension to this idea and introduced a multilevel grouping of the streamlines to capture the white matter arrangement in fascicles and subfascicles and tested this in synthetic and in vivo data. They showed that using multiple levels allows considering information about the white matter organization more adequately and helps to improve further the accuracy of the resulting tractograms.

General Intelligence Is Associated with Working Memory-Related Functional Connectivity Change: Evidence from a Large-Sample Study

Psychometric intelligence is closely related to working memory and the associated brain activity. Hikaru Takeuchi and Ryuta Kawashima along with their colleagues aimed to clarify the associations between psychometric intelligence and working memory induced functional connectivity changes. They evaluated the associations between psychometric intelligence measured by nonverbal reasoning (using the Raven's Advanced Progressive Matrices) and working memory induced changes in functional connectivity during the N-back paradigm, in a large cohort.

They observed that the measures of general intelligence showed a significant positive correlation with working memory induced changes in the functional connectivity with the key nodes of the frontoparietal network, such as the bilateral premotor cortices and the pre-supplementary motor area. Significant correlations were observed for (1) areas showing a working memory induced increase of the functional connectivity with the abovementioned key nodes, such as the lateral parietal cortex; (2) areas showing a working memory induced decrease of the functional connectivity with the abovementioned key nodes (2-a) such as left perisylvian areas and cuneus, the fusiform gyrus, and the lingual gyrus, which play key roles in language processing, (2-b) hippocampus and para hippocampal gyrus, which play key roles in memory processing, and (2-c) the key node of the default mode network such as the medial prefrontal cortex; as well as (3) the border areas between (1) and (2). They conclude that psychometric intelligence is associated with working memory induced changes in functional connectivity, influencing the way in which working memory key nodes dynamically modulate the interaction with other brain nodes.

Functional Connectivity Within and Between n-Back Modulated Regions: An Adult Lifespan Psychophysiological Interaction Investigation

Functional connectivity (FC) generally increases with working memory load; however, how aging impacts connectivity and whether this is load-dependent, region-dependent, or associated with cognitive performance is unclear. In this study, Ekarin E. Pongpipat, Karen M. Rodrigue and colleagues examined 170 healthy adults who completed functional magnetic resonance imaging scanning during an n-back task. The FC was estimated by utilizing a modified generalized psychophysiological interaction approach with seeds from frontoparietal (FP) and default mode (DM) regions that modulated to n-back difficulty. The FC analyses focused on both connectivity during working memory engagement (task vs. control) and connectivity in response to increased working memory load (linear slope across conditions). The authors found that engaging in working memory either generally (task vs. control) or as a function of difficulty strengthened integration within- and between-FP and DM regions. Notably, these task-sensitive functional connections were robust to the effects of age. Stronger negative FC between FP and DM regions was also associated with better working memory performance in an age-dependent manner, occurring selectively in middle-aged and older adults.

Temporal Lobe Epilepsy Shows Distinct Functional Connectivity Patterns in Different Thalamic Nuclei

The thalamus has been implicated in focal limbic seizures propagation, awareness maintenance, and seizure- related cognitive deficits. However, the functional alterations between different thalamic nuclei and subcortical-cortical systems in temporal lobe epilepsy (TLE) remain largely unknown. Rong Li and Li Feng along with their colleagues examined thalamic functional connectivity (FC) in TLE patients and healthy controls. The anterior (ANT), ventral posterior medial, and central lateral nuclei of thalamus were employed to establish whole-brain seed-to-voxel thalamic FC maps. Abnormal thalamic FC and the memory performance in TLE was also investigated. The TLE showed significantly decreased FC between different thalamic nuclei and subcortical-cortical networks, including the limbic structures, midbrain, sensorimotor network, medial prefrontal cortex, temporal-occipital fusiform gyrus, and cerebellum. Verification analyses yielded similar patterns of thalamic FC changes in TLE. The decreased FC between the ANT and hippocampal pathway was correlated with the poorer memory performance of TLE. The authors conclude that the specific pathology of the ANT–hippocampal pathway in TLE may be a potential factor that contributes to memory deficits.

A Classification-Based Approach to Estimate the Number of Resting Functional Magnetic Resonance Imaging Dynamic Functional Connectivity States

Dynamic FNC is most commonly estimated using a sliding window-based approach to capture short periods of FNC change. These data are then clustered to estimate transient connectivity patterns or states. The elbow criterion is one of the widely used approaches to determine the connectivity states. Debbrata K. Saha and Vince D. Calhoun along with their colleagues present an alternative approach that evaluates classification as a measure to select the optimal number of states (clusters). They apply different classification strategies to perform classification between HCs and patients with schizophrenia for different numbers of states (i.e., varying the model order in the clustering algorithm). The authors demonstrate that that overall accuracy improves when dynamic connectivity measures are used separately or in combination with static connectivity measures. Results also show that the optimal model order for classification is different from that using the standard k-means model selection method, and that such optimization improves cross-validated accuracy. The optimal model order obtained from the proposed approach also gives significantly improved classification performance over the traditional model selection method.

Reconfiguration of Electroencephalography Microstate Networks After Breath-Focused, Digital Meditation Training

Sustained attention and working memory were improved in young adults after they engaged in a recently developed, closed-loop, digital meditation practice. Lucie Bréchet and Christoph M. Michel along with their colleagues investigated whether this type of meditation has a sustained effect on dominant resting-state networks. The authors examined the resting brain states before and after a period of breath-focused, digital meditation training versus placebo using an electroencephalography (EEG) microstate approach. They found topographical changes in post-meditation rest, compared with baseline rest, selectively for participants who were actively involved in the meditation training and not in participants who engaged with an active, expectancy-match, placebo control paradigm suggesting a reorganization of brain network connectivity after 6 weeks of intensive meditation training in brain areas, mainly including the right insula, the superior temporal gyrus, the superior parietal lobule, and the superior frontal gyrus bilaterally.

Finally, I would like to thank all the researchers and all the staff at Mary Ann Liebert, Inc., publishers, editors and reviewers of Brain Connectivity working during this difficult to advance research in every corner of the world to improve our lives.

I sincerely hope that all our contributors and readers of the journal are remaining in good health.