Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks

The relation of structural integrity and task-related functional connectivity in the aging brain.

The relations among structural integrity, functional connectivity (FC), and cognitive performance in the aging brain are still understudied. Here, we used multimodal and multivariate approaches to specifically examine age-related changes in task-related FC, gray-matter volumetrics, white-matter integrity, and performance. Our results are two-fold, showing (i) age-related differences in FC of the working memory network and (ii) age-related recruitment of a compensatory network associated with better accuracy on the task. Increased connectivity in the compensatory network correlates positively with preserved white-matter integrity in bilateral frontoparietal tracks and with larger gray-matter volume of right inferior parietal lobule. These findings demonstrate the importance of structural integrity and FC in working memory performance associated with healthy aging

Connectivity analysis with structural equation modelling: an example of the effects of voxel selection

Structural equation modelling (SEM) of neuroimaging data is commonly applied to a network of distributed brain regions. We applied SEM to an fMRI dataset to identify condition-specific effects in a simple experiment composed of visual stimulation and baseline conditions. The visual network was composed of three well-defined anatomical regions (V1, V2, and V5) and three path connections (V1 → V2, V1 → V5, and V2 → V5). This network was used to test four hypotheses: (1) whether the condition-specific effects for all three connections vary according to the data selected for modelling; (2) whether the “summary” measures that are often used are indeed appropriate; (3) whether measures taken from the voxel timecourse can reliably predict the condition-specific effects for each one of the three path connections, and (4) whether all voxels within an anatomical region yield equivalent SEM outcomes. There was some variability in the significance of the condition-specific effects across randomly selected voxels within regions. However, the SEM outcome from the “summary” measures was comparable to the most frequent pattern of condition-specific effects. Magnitude, delay, spread, and goodness-of-fit measures taken from a gamma fit to the voxel time courses predicted reliably the significance of the SEM condition-specific effects for each connection. This result enabled us to identify spatially coherent regions at the boundaries of V2 that displayed different condition-specific effects from those seen in the majority of the voxels. Although the generality of these results awaits further investigation, this example highlights a number of important issues for SEM

Neural Correlate of Filtering of Irrelevant Information from Visual Working Memory

In a dynamic environment stimulus task relevancy could be altered through time and it is not always possible to dissociate relevant and irrelevant objects from the very first moment they come to our sight. In such conditions, subjects need to retain maximum possible information in their WM until it is clear which items should be eliminated from WM to free attention and memory resources. Here, we examined the neural basis of irrelevant information filtering from WM by recording human ERP during a visual change detection task in which the stimulus irrelevancy was revealed in a later stage of the task forcing the subjects to keep all of the information in WM until test object set was presented. Assessing subjects' behaviour we found that subjects' RT was highly correlated with the number of irrelevant objects and not the relevant one, pointing to the notion that filtering, and not selection, process was used to handle the distracting effect of irrelevant objects. In addition we found that frontal N150 and parietal N200 peak latencies increased systematically as the amount of irrelevancy load increased. Interestingly, the peak latency of parietal N200, and not frontal N150, better correlated with subjects' RT. The difference between frontal N150 and parietal N200 peak latencies varied with the amount of irrelevancy load suggesting that functional connectivity between modules underlying fronto-parietal potentials vary concomitant with the irrelevancy load. These findings suggest the existence of two neural modules, responsible for irrelevant objects elimination, whose activity latency and functional connectivity depend on the number of irrelevant object

Attention and Functional Connectivity in Survivors of Childhood Brain Tumors

To study potential hyperactivity and hyperconnectivity based on the latent resource hypothesis, this study assessed functional connectivity in survivors of childhood brain tumors compared to their healthy peers during an attention task using psychophysiological interaction (PPI) analyses and evaluated for a relationship with performance. Twenty-three survivors and 23 healthy controls completed a letter n-back task in the scanner. An empirically-based seed was placed in the parietal lobe, a theoretical seed was placed in the hippocampus, and a control seed was placed in the occipital lobe. Differences in both performance and functional connectivity networks from each seed emerged between groups, with some findings supporting the latent resource hypothesis and other networks showing compensatory function in survivors. Attention networks, phonological networks, and executive function networks were all found to differ between controls and survivors

Effective connectivity among the working memory regions during preparation for and during performance of the n-back task

Recent neuroimaging studies have shown that working memory (WM) task difficulty can be decoded from patterns of brain activation in the WM network during preparation to perform those tasks. The inter-regional connectivity among the WM regions during task preparation has not yet been investigated. We examined this question using the graph modeling methods IMaGES and LOFS, applied to the previously published fMRI data of Manelis and Reder (2013). In that study, subjects performed 1-, 2-, and 3-back tasks. Each block of n-back was preceded by a preparation period and followed by a rest period. The analyses of task-related brain activity identified a network of 18 regions that increased in activation from 1to 3-back (Increase network) and a network of 17 regions that decreased in activation from 1to 3-back (Decrease network). The graph analyses revealed two types of connectivity sub-networks within the Increase and Decrease networks: "default" and "preparation-related." The "default" connectivity was present not only during task performance, but also during task preparation and during rest. We propose that this sub-network may serve as a core system that allows one to quickly activate cognitive, perceptual and motor systems in response to the relevant stimuli. The "preparation-related" connectivity was present during task preparation and task performance, but not at rest, and depended on the n-back condition. The role of this sub-network may be to pre-activate a connectivity "road map" in order to establish a top-down and bottom-up regulation of attention prior to performance on WM tasks. © 2014 Manelis and Reder

Altered Cortico-Cortical Brain Connectivity During Muscle Fatigue

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of int

Altered Cortico-Cortical Brain Connectivity During Muscle Fatigue

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of int

The Effect of Binaural Beats on Visuospatial Working Memory and Cortical Connectivity

Binaural beats utilize a phenomenon that occurs within the cortex when two different frequencies are presented separately to each ear. This procedure produces a third phantom binaural beat, whose frequency is equal to the difference of the two presented tones and which can be manipulated for non-invasive brain stimulation. The effects of binaural beats on working memory, the system in control of temporary retention and online organization of thoughts for successful goal directed behavior, have not been well studied. Furthermore, no studies have evaluated the effects of binaural beats on brain connectivity during working memory tasks. In this study, we determined the effects of different acoustic stimulation conditions on participant response accuracy and cortical network topology, as measured by EEG recordings, during a visuospatial working memory task. Three acoustic stimulation control conditions and three binaural beat stimulation conditions were used: None, Pure Tone, Classical Music, 5Hz binaural beats, 10Hz binaural beats, and 15Hz binaural beats. We found that listening to 15Hz binaural beats during a visuospatial working memory task not only increased the response accuracy, but also modified the strengths of the cortical networks during the task. The three auditory control conditions and the 5Hz and 10Hz binaural beats all decreased accuracy. Based on graphical network analyses, the cortical activity during 15Hz binaural beats produced networks characteristic of high information transfer with consistent connection strengths throughout the visuospatial working memory task