Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training

Working memory related functional connectivity in adult ADHD and its amenability to training: A randomized controlled trial

BACKGROUND

Working memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group.

METHODS

Forty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic.

RESULTS

At the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls.

CONCLUSION

Our results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited.

Comparison of local activation, functional connectivity, and structural connectivity in the N-back task

The N-back task is widely used to investigate working memory. Previous functional magnetic resonance imaging (fMRI) studies have shown that local brain activation depends on the difficulty of the N-back task. Recently, changes in functional connectivity and local activation during a task, such as a single-hand movement task, have been reported to give the distinct information. However, previous studies have not investigated functional connectivity changes in the entire brain during N-back tasks. In this study, we compared alterations in functional connectivity and local activation related to the difficulty of the N-back task. Because structural connectivity has been reported to be associated with local activation, we also investigated the relationship between structural connectivity and accuracy in a N-back task using diffusion tensor imaging (DTI). Changes in functional connectivity depend on the difficulty of the N-back task in a manner different from local activation, and the 2-back task is the best method for investigating working memory. This indicates that local activation and functional connectivity reflect different neuronal events during the N-back task. The top 10 structural connectivities associated with accuracy in the 2-back task were locally activated during the 2-back task. Therefore, structural connectivity as well as fMRI will be useful for predicting the accuracy of the 2-back task.

Molecular Mechanisms for Changing Brain Connectivity in Mice and Humans

The goal of this study was to examine commonalities in the molecular basis of learning in mice and humans. In previous work we have demonstrated that the anterior cingulate cortex (ACC) and hippocampus (HC) are involved in learning a two-choice visuospatial discrimination task. Here, we began by looking for candidate genes upregulated in mouse ACC and HC with learning. We then determined which of these were also upregulated in mouse blood. Finally, we used RT-PCR to compare candidate gene expression in mouse blood with that from humans following one of two forms of learning: a working memory task (network training) or meditation (a generalized training shown to change many networks). Two genes were upregulated in mice following learning: caspase recruitment domain-containing protein 6 (Card6) and inosine monophosphate dehydrogenase 2 (Impdh2). The Impdh2 gene product catalyzes the first committed step of guanine nucleotide synthesis and is tightly linked to cell proliferation. The Card6 gene product positively modulates signal transduction. In humans, Card6 was significantly upregulated, and Impdh2 trended toward upregulation with training. These genes have been shown to regulate pathways that influence nuclear factor kappa B (NF-κB), a factor previously found to be related to enhanced synaptic function and learning.

Cognitive control training enhances the integration of intrinsic functional networks in adolescents

Introduction

We have demonstrated that intensive cognitive training can produce sustained improvements in cognitive performance in adolescents. Few studies, however, have investigated the neural basis of these training effects, leaving the underlying mechanism of cognitive plasticity during this period unexplained.

Methods

In this study, we trained 51 typically developing adolescents on cognitive control tasks and examined how their intrinsic brain networks changed by applying graph theoretical analysis. We hypothesized that the training would accelerate the process of network integration, which is a key feature of network development throughout adolescence.

Results

We found that the cognitive control training enhanced the integration of functional networks, particularly the cross-network integration of the cingulo-opercular network. Moreover, the analysis of additional data from older adolescents revealed that the cingulo-opercular network was more integrated with other networks in older adolescents than in young adolescents.

Discussion

These findings are consistent with the hypothesis that cognitive control training may speed up network development, such that brain networks exhibit more mature patterns after training.

Eight-week multi-domain cognitive training does not impact large-scale resting-state brain networks in Parkinson's disease

There is meta-analytic evidence for the efficacy of cognitive training (CT) in Parkinson's disease (PD). We performed a randomized controlled trial where we found small positive effects of CT on executive function and processing speed in individuals with PD (ntotal = 140). In this study, we assessed the effects of CT on brain network connectivity and topology in a subsample of the full study population (nmri = 86). Participants were randomized into an online multi-domain CT and an active control condition and performed 24 sessions of either intervention in eight weeks. Resting-state functional MRI scans were acquired in addition to extensive clinical and neuropsychological assessments pre- and post-intervention. In line with our preregistered analysis plan (osf.io/3st82), we computed connectivity between 'cognitive' resting-state networks and computed topological outcomes at the whole-brain and sub-network level. We assessed group differences after the intervention with mixed-model analyses adjusting for baseline performance and analyzed the association between network and cognitive performance changes with repeated measures correlation analyses. The final analysis sample consisted of 71 participants (n CT = 37). After intervention there were no group differences on between-network connectivity and network topological outcomes. No associations between neural network and neuropsychological performance change were found. CT increased segregated network topology in a small sub-sample of cognitively intact participants. Post-hoc nodal analyses showed post-intervention enhanced connectivity of both the dorsal anterior cingulate cortex and dorsolateral prefrontal cortex in the CT group. The results suggest no large-scale brain network effects of eight-week computerized CT, but rather localized connectivity changes of key regions in cognitive function, that potentially reflect the specific effects of the intervention.

MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

Cognitive Training in Parkinson's Disease Induces Local, Not Global, Changes in White Matter Microstructure

Previous studies showed that cognitive training can improve cognitive performance in various neurodegenerative diseases but little is known about the effects of cognitive training on the brain. Here, we investigated the effects of our cognitive training paradigm, COGTIPS, on regional white matter microstructure and structural network topology. We previously showed that COGTIPS has small, positive effects on processing speed. A subsample of 79 PD patients (N = 40 cognitive training group, N = 39 active control group) underwent multi-shell diffusion-weighted imaging pre- and post-intervention. Our pre-registered analysis plan (osf.io/cht6g) entailed investigating white matter microstructural integrity (e.g., fractional anisotropy) in five tracts of interest, including the anterior thalamic radiation (ATR), whole-brain tract-based spatial statistics (TBSS), and the topology of the structural network. Relative to the active control condition, cognitive training had no effect on topology of the structural network or whole-brain TBSS. Cognitive training did lead to a reduction in fractional anisotropy in the ATR (B [SE]: - 0.32 [0.12], P = 0.01). This reduction was associated with faster responses on the Tower of London task (r = 0.42, P = 0.007), but this just fell short of our statistical threshold (P < 0.006). Post hoc "fixel-based" analyses showed that this was not due to changes in fiber density and cross section. This suggests that the observed effect in the ATR is due to training-induced alterations in neighboring fibers running through the same voxels, such as intra-striatal and thalamo-striatal fibers. These results indicate that 8 weeks of cognitive training does not alter network topology, but has subtle local effects on structural connectivity.

Navigating the link between processing speed and network communication in the human brain

Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.

MRI Predictors of Cognitive Training Outcomes

The prospect of improving or maintaining cognitive functioning has provoked a steadily increasing number of cognitive training interventions over the last years, especially for clinical and elderly populations. However, there are discrepancies between the findings of the studies. One of the reasons behind these heterogeneous findings is that there are vast inter-individual differences in how people benefit from the training and in the extent that training-related gains are transferred to other untrained tasks and domains. In this paper, we address the value of incorporating neural measures to cognitive training studies in order to fully understand the mechanisms leading to inter-individual differences in training gains and their generalizability to other tasks. Our perspective is that it is necessary to collect multimodal neural measures in the pre- and post-training phase, which can enable us to understand the factors contributing to successful training outcomes. More importantly, this understanding can enable us to predict who will benefit from different types of interventions, thereby allowing the development of individually tailored intervention programs.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Computerized neurocognitive interventions in the context of the brain training controversy

This article presents, in the form of an analytic narrative review, a complete picture of the state-of-the-art, challenges, and perspectives in the field of information and communication technology (ICT)-based neurocognitive interventions for older adults. The narrative particularly focuses on applications aimed at mild cognitive impairment and similar age-related cognitive deficits, which are analyzed in the context of the brain training controversy. Clarifying considerations are provided about the nature and present extent of the brain training debate, regarding the possible influence it has on the support received by research and development initiatives dealing with innovative computerized neurocognitive interventions. It is recommended that, because of the preliminary nature of most data currently available in this area, further research initiatives must be supported in the quest for better effectiveness of computer-based interventions intended for age-related cognitive impairment. The conclusion suggests that advanced ICT-based tools, such as virtual and augmented reality technologies, are the most fitting platforms for applying nonpharmacological computerized neurocognitive interventions.

White-Matter Pathways for Statistical Learning of Temporal Structures

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

Enhancing Intelligence: From the Group to the Individual

Research aimed at testing whether short-term training programs can enhance intelligence is mainly concentrated on behavior. Expected positive effects are found sometimes, but the evidence is far from conclusive. It is assumed that training must evoke changes in the brain for observing genuine improvements in behavior. However, behavioral and brain data are seldom combined in the same study. Here we present one example of this latter type of research summarizing, discussing, and integrating already published results. The training program was based on the adaptive dual n-back task, and participants completed a comprehensive battery measuring fluid and crystallized ability, along with working memory and attention control, before and after training. They were also submitted to MRI scanning at baseline and post-training. Behavioral results revealed positive effects for visuospatial processing across cognitive domains. Brain imaging data were analyzed by longitudinal voxel-based morphometry, tensor-based morphometry, surface-based morphometry, and structural connectivity. The integration of these multimodal brain results provides clues about those observed in behavior. Our findings, along with previous research and current technological advances, are considered from the perspective that we now live in ideal times for (a) moving from the group to the individual and (b) developing personalized training programs.

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on "resting-state" networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.

Developmental song learning as a model to understand neural mechanisms that limit and promote the ability to learn

Songbirds famously learn their vocalizations. Some species can learn continuously, others seasonally, and still others just once. The zebra finch (Taeniopygia guttata) learns to sing during a single developmental "Critical Period," a restricted phase during which a specific experience has profound and permanent effects on brain function and behavioral patterns. The zebra finch can therefore provide fundamental insight into features that promote and limit the ability to acquire complex learned behaviors. For example, what properties permit the brain to come "on-line" for learning? How does experience become encoded to prevent future learning? What features define the brain in receptive compared to closed learning states? This piece will focus on epigenomic, genomic, and molecular levels of analysis that operate on the timescales of development and complex behavioral learning. Existing data will be discussed as they relate to Critical Period learning, and strategies for future studies to more directly address these questions will be considered. Birdsong learning is a powerful model for advancing knowledge of the biological intersections of maturation and experience. Lessons from its study not only have implications for understanding developmental song learning, but also broader questions of learning potential and the enduring effects of early life experience on neural systems and behavior.