Project description:The brain operates via networked activity in separable groups of regions called modules. The quantification of modularity compares the number of connections within and between modules, with high modularity indicating greater segregation, or dense connections within sub-networks and sparse connections between sub-networks. Previous work has demonstrated that baseline brain network modularity predicts executive function outcomes in older adults and patients with traumatic brain injury after cognitive and exercise interventions. In healthy young adults, however, the functional significance of brain modularity in predicting training-related cognitive improvements is not fully understood. Here, we quantified brain network modularity in young adults who underwent cognitive training with casual video games that engaged working memory and reasoning processes. Network modularity assessed at baseline was positively correlated with training-related improvements on untrained tasks. The relationship between baseline modularity and training gain was especially evident in initially lower performing individuals and was not present in a group of control participants that did not show training-related gains. These results suggest that a more modular brain network organization may allow for greater training responsiveness. On a broader scale, these findings suggest that, particularly in low-performing individuals, global network properties can capture aspects of brain function that are important in understanding individual differences in learning.
Project description:We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury.Patients with acquired brain injury (n = 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre- to posttraining changes in neuropsychological measures of attention and executive control.The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training.Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts.
Project description:BackgroundCognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients.ObjectiveTo investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology.MethodsResting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage.ResultsA more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (Radj2=15%), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band.ConclusionsThe level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.
Project description:Cognitive training has been shown effective in improving the cognitive function of older adults. While training related plasticity of the brain has been observed at different levels, it is still open to exploration whether local functional connectivity (FC) may be affected by training. Here, we examined the neuroimaging data from a previous randomized-controlled double-blinded behavioural study, in which healthy older adults participated in a 3-month cognitive training program. Resting-state fMRI was acquired at baseline and one year after training. The local FC in the brain was estimated using the regional homogeneity (ReHo), and the high ReHo clusters (HRCs) were extracted to quantify the level of local FC integration. Results showed that: (i) HRCs exhibited a power-law size distribution; (ii) local FC were less integrated in older participants than in younger participants; (iii) local FC in older participants of the training group became more integrated after training than the control group; (iv) the baseline local FC integration was positively correlated with educational level. These results indicated a training-related alteration in local FC.
Project description:Managing age-related decrease of cognitive function is an important public health challenge, especially in the context of the global aging of the population. Over the last years several Cognitive Mobile Games (CMG) have been developed to train and challenge the brain. However, currently the level of evidence supporting the benefits of using CMG in real-life use is limited in older adults, especially at a late age. In this study we analyzed game scores and the processing speed obtained over the course of 100 sessions in 12,000 subjects aged 60 to over 80 years. Users who trained with the games improved regardless of age in terms of scores and processing speed throughout the 100 sessions, suggesting that old and very old adults can improve their cognitive performance using CMG in real-life use.
Project description:Recent work suggests that the brain can be conceptualized as a network comprised of groups of sub-networks or modules. The extent of segregation between modules can be quantified with a modularity metric, where networks with high modularity have dense connections within modules and sparser connections between modules. Previous work has shown that higher modularity predicts greater improvements after cognitive training in patients with traumatic brain injury and in healthy older and young adults. It is not known, however, whether modularity can also predict cognitive gains after a physical exercise intervention. Here, we quantified modularity in older adults (N = 128, mean age = 64.74) who underwent one of the following interventions for 6 months (NCT01472744 on ClinicalTrials.gov): (1) aerobic exercise in the form of brisk walking (Walk), (2) aerobic exercise in the form of brisk walking plus nutritional supplement (Walk+), (3) stretching, strengthening and stability (SSS), or (4) dance instruction. After the intervention, the Walk, Walk+ and SSS groups showed gains in cardiorespiratory fitness (CRF), with larger effects in both walking groups compared to the SSS and Dance groups. The Walk, Walk+ and SSS groups also improved in executive function (EF) as measured by reasoning, working memory, and task-switching tests. In the Walk, Walk+, and SSS groups that improved in EF, higher baseline modularity was positively related to EF gains, even after controlling for age, in-scanner motion and baseline EF. No relationship between modularity and EF gains was observed in the Dance group, which did not show training-related gains in CRF or EF control. These results are consistent with previous studies demonstrating that individuals with a more modular brain network organization are more responsive to cognitive training. These findings suggest that the predictive power of modularity may be generalizable across interventions aimed to enhance aspects of cognition and that, especially in low-performing individuals, global network properties can capture individual differences in neuroplasticity.
Project description:IntroductionGiven rapid global population aging, developing interventions against age-associated cognitive decline is an important medical and societal goal. We evaluated a cognitive training protocol combined with transcranial direct current stimulation (tDCS) on trained and non-trained functions in non-demented older adults.MethodsFifty-six older adults (65-80 years) were randomly assigned to one of two interventional groups, using age and baseline performance as strata. Both groups performed a nine-session cognitive training over 3 weeks with either concurrent anodal tDCS (atDCS, 1 mA, 20 minutes) over the left dorsolateral prefrontal cortex (target intervention) or sham stimulation (control intervention). Primary outcome was performance on the trained letter updating task immediately after training. Secondary outcomes included performance on other executive and memory (near and far transfer) tasks. All tasks were administered at baseline, post-intervention, and at 1- and 7-month follow-up assessments. Prespecified analyses to investigate treatment effects were conducted using mixed-model analyses.ResultsNo between-group differences emerged in the trained letter updating and Markov decision-making tasks at post-intervention and at follow-up timepoints. Secondary analyses revealed group differences in one near-transfer task: Superior n-back task performance was observed in the tDCS group at post-intervention and at follow-up. No such effects were observed for the other transfer tasks. Improvements in working memory were associated with individually induced electric field strengths.DiscussionCognitive training with atDCS did not lead to superior improvement in trained task performance compared to cognitive training with sham stimulation. Thus, our results do not support the immediate benefit of tDCS-assisted multi-session cognitive training on the trained function. As the intervention enhanced performance in a near-transfer working memory task, we provide exploratory evidence for effects on non-trained working memory functions in non-demented older adults that persist over a period of 1 month.
Project description:The presence of olfactory dysfunction in individuals at higher risk of Alzheimer's disease has significant diagnostic and screening implications for preventive and ameliorative drug trials. Olfactory threshold, discrimination and identification can be reliably recorded in the early stages of neurodegenerative diseases. The current study has examined the ability of various olfactory functions in predicting cognitive decline in a community-dwelling sample. A group of 308 participants, aged 46-86 years old, were recruited for this study. After 3 years of follow-up, participants were divided into cognitively declined and non-declined groups based on their performance on a neuropsychological battery. Assessment of olfactory functions using the Sniffin' Sticks battery indicated that, contrary to previous findings, olfactory discrimination, but not olfactory identification, significantly predicted subsequent cognitive decline (odds ratio = 0.869; P<0.05; 95% confidence interval = 0.764-0.988). The current study findings confirm previously reported associations between olfactory and cognitive functions, and indicate that impairment in olfactory discrimination can predict future cognitive decline. These findings further our current understanding of the association between cognition and olfaction, and support olfactory assessment in screening those at higher risk of dementia.
Project description:Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN.SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control network and default mode network strengthen their interaction with one another during episodic retrieval. Such across-network communication likely facilitates effective access to internally generated representations of past event knowledge.
Project description:The expression of microRNA (miRNA) is influenced by ongoing biological processes, including aging, and has begun to play a role in the measurement of neurodegenerative processes in central nervous system. The purpose of this study is to utilize machine learning approaches to determine whether miRNA can be utilized as a blood-based biomarker of cognitive aging. A random forest regression combining miRNA with biological (brain volume), clinical (comorbid conditions), and demographic variables in 115 typically aging older adults explained the greatest level of variance in cognitive performance compared to the other machine learning models explored. Three miRNA (miR-140-5p, miR-197-3p, and miR-501-3p) were top-ranked predictors of multiple cognitive outcomes (Fluid, Crystallized, and Overall Cognition) and past studies of these miRNA link them to cellular senescence, inflammatory signals for atherosclerotic formation, and potential development of neurodegenerative disorders (e.g., Alzheimer's disease). Several novel miRNAs were also linked to age and multiple cognitive functions, findings which together warrant further exploration linking these miRNAs to brain-derived metrics of neurodegeneration in typically aging older adults.