Plasticity of brain networks in a randomized intervention trial of exercise training in older adults

Abstract

Research has shown the human brain is organized into separable functional networks during rest and varied states of cognition, and that aging is associated with specific network dysfunctions. The present study used functional magnetic resonance imaging (fMRI) to examine low-frequency (0.008 < f < 0.08 Hz) coherence of cognitively relevant and sensory brain networks in older adults who participated in a 1-year intervention trial, comparing the effects of aerobic and non-aerobic fitness training on brain function and cognition. Results showed that aerobic training improved the aging brain's resting functional efficiency in higher-level cognitive networks. One year of walking increased functional connectivity between aspects of the frontal, posterior, and temporal cortices within the Default Mode Network and a Frontal Executive Network, two brain networks central to brain dysfunction in aging. Length of training was also an important factor. Effects in favor of the walking group were observed only after 12 months of training, compared to non-significant trends after 6 months. A non-aerobic stretching and toning group also showed increased functional connectivity in the DMN after 6 months and in a Frontal Parietal Network after 12 months, possibly reflecting experience-dependent plasticity. Finally, we found that changes in functional connectivity were behaviorally relevant. Increased functional connectivity was associated with greater improvement in executive function. Therefore the study provides the first evidence for exercise-induced functional plasticity in large-scale brain systems in the aging brain, using functional connectivity techniques, and offers new insight into the role of aerobic fitness in attenuating age-related brain dysfunction.

Strategy‐based reasoning training modulates cortical thickness and resting‐state functional connectivity in adults with chronic traumatic brain injury

“…Quantitative MRI‐based morphometry further revealed experience‐dependent brain plasticity in clinical populations such as balance training for Parkinson's disease (Sehm et al., 2014) and physical activity for heart failure, Schizophrenia, and mild cognitive impairment (Alosco et al., 2015; McEwen et al., 2015; Reiter et al., 2015). Training‐induced changes in resting‐state networks in the healthy adult brain have also been reported following motor training (Lewis, Baldassarre, Committeri, Romani, & Corbetta, 2009; Taubert, Lohmann, Margulies, Villringer, & Ragert, 2011), cognitive training (Jolles, van Buchem, Crone, & Rombouts, 2013; Mackey, Miller Singley, & Bunge, 2013; Takeuchi et al., 2013), and physical activity in the elderly (Voss, 2010). In clinical populations, rsFC has also been used to identify changes in resting‐state networks induced by rehabilitation for multiple sclerosis (de Giglio et al., 2016) and stroke (Fan et al., 2015; Varkuti et al., 2013).…”

Multiple effects of physical activity on molecular and cognitive signs of brain aging: can exercise slow neurodegeneration and delay Alzheimer’s disease?

“…53,54 Using functional magnetic resonance imaging (fMRI), functional connectivity analyses can characterise the nature of interactions among brain regions, and the relationship between exercise on these networks has been investigated. Voss et al 55 reported that a one year walking program enhanced the functional connectivity between the frontal, temporal and posterior cortices within the DMN and FEN. Interestingly, a control group (who engaged in non-aerobic stretching and toning exercises) also showed increased functional connectivity in the DMN, which could possibly be attributed to experience-dependent brain plasticity.…”

An Integrated Assessment of Changes in Brain Structure and Function of the Insula Resulting from an Intensive Mindfulness-Based Intervention

“…Whole-brain analyses employing analysis of variance (ANOVA) on data from randomized controlled trials carry a risk of producing findings that are not driven by the experimental manipulation but rather by a combination of chance baseline differences and chance divergence between conditions over time (Voss et al 2010). This risk is reduced by using stringent corrections for multiple comparisons, but can be further minimized using a statistical procedure that first detects regions showing significant changes over time in the intervention condition and then subsequently examines these regions at pre-test and post-test across both conditions (Hölzel et al 2011;Mrazek et al 2016).…”