RESUMEN
Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.
Brain networks identified by functional connectivity (FC) have preserved architectures from rest to task and across task demands. Higher similarity, implying more efficient network reconfiguration, was associated with better cognition and task performance in young adults. To examine how it may be influenced by aging, we compared whole-brain and network-level FC similarities between resting-state and spatial working memory fMRI in young and older adults. At whole-brain level and higher order cognitive networks, older adults evidenced less efficient network reconfiguration from rest to task than young adults. Importantly, more efficient reconfiguration was associated with better accuracy. This relationship relied more on internetwork connections in older adults. Despite reduced neural resources compared to young, maintaining efficient network updating still contributes to better cognition at older age.
RESUMEN
OBJECTIVE: It is unclear how the functional brain hierarchy is organized in preschool-aged children, and whether alterations in the brain organization are linked to mental health in this age group. Here, we assessed whether preschool-aged children exhibit a brain organizational structure similar to that of older children, how this structure might change over time, and whether it might reflect mental health. METHOD: This study derived functional gradients using diffusion embedding from resting state functional magnetic resonance imaging data of 4.5-year-old children (N = 100, 42 male participants) and 6.0-year-old children (N = 133, 62 male participants) from the longitudinal Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. We then conducted partial least-squares correlation analyses to identify the association between the impairment ratings of different mental disorders and network gradient values. RESULTS: The main organizing axis of functional connectivity (ie, principal gradient) separated the visual and somatomotor regions (ie, unimodal) in preschool-aged children, whereas the second axis delineated the unimodal-transmodal gradient. This pattern of organization was stable from 4.5 to 6 years of age. The second gradient separating the high- and low-order networks exhibited a diverging pattern across mental health severity, differentiating dimensions related to attention-deficit/hyperactivity disorder and phobic disorders. CONCLUSION: This study characterized, for the first time, the functional brain hierarchy in preschool-aged children. A divergence in functional gradient pattern across different disease dimensions was found, highlighting how perturbations in functional brain organization can relate to the severity of different mental health disorders.
Asunto(s)
Trastorno por Déficit de Atención con Hiperactividad , Mapeo Encefálico , Humanos , Masculino , Preescolar , Niño , Adolescente , Mapeo Encefálico/métodos , Imagen por Resonancia Magnética/métodos , Encéfalo/diagnóstico por imagen , PsicopatologíaRESUMEN
Optimal levels of intrinsic Blood-Oxygenation-Level-Dependent (BOLD) signal variability (variability hereafter) are important for normative brain functioning. However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.