Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective

The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

“…Decreases in between-network connectivity of associative networks was also relatively uncommon, reported in two of the 36 (6%) studies reviewed (Lou et al, 2020;Wen et al 2020a). Sixteen studies reported on the default mode network separately, with 14 (88%) reporting increased connectivity to other large-scale networks.…”

“…All seven of the studies (100%) that assessed dynamic within-network connectivity found altered dynamics in older adults compared to younger adults in all or some of the networks investigated (see Table S5 for details). Older adults showed reduced connectivity strength and reduced signal variability compared to younger adults (Chen et al, 2018a;Madhyastha & Grabowski, 2014;Park et al, 2017;Qin et al, 2015;Tian et al, 2018;Wen et al, 2020a). All eight of the studies (100%) that assessed dynamic between-network connectivity also found different dynamics with age (Chen et al, 2018a;Davison et al, 2016;Qin et al, 2015;Tian et al, 2018;Viviano et al, 2017;Wen et al, 2020a).…”

“…There is more variability in results for the primary processing and control networks where they were reported separately. Eight of 23 (35%) studies reported no age differences or higher within-network connectivity among older adults in the sensorimotor network (Cao et al, 2014;Chan, 2014;Onada et al, 2013;Simon-Tov et al, 2017;Song et al, 2014;Tomasi & Volklow, 2012;Viviano et al, 2017;Wen et al, 2020a), three of 14 (21%) in the visual system (Farras-Permanyer et al, 2019;Petrican et al, 2017;Zonneveld et al, 2019) and two of two (100%) in the emotional network (Nashiro et al, 2017;Wang et al, 2012). Age-related reductions in integration within the sensorimotor network have also been reported (He et al, 2020;Roski et al, 2013).…”

The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework

“…As for the topics of research interest, nearly 70% of the inclusions (24/35) concentrate on gerontology, namely, biomarkers (3) [33][34][35], frailty index (1) [36], epigenetics of aging (1) [37], aging of brain functional connectivity networks (1) [38], ARDs (16) that contain dementia (6) [39][40][41][42][43][44], stroke (2) [45,46], Parkinson's Disease (1 ) [47], fracture (1) [48], hypertension (1) [49], mild cognitive impairment (1) [50], depression (1) [51], pressure ulcers (1) [52], polypharmacy side-effects (1) [53], and mortality related to sarcopenia and frailty (1) [54]. The rest are devoted to technical support and decision support like cloud-based healthcare platforms focusing on dementia (1) [55], memory recall training (1) [56]), fraud detection in medicare (2) [57,58], prediction of readmission risk (2) [59,60], well-being (2) [61,62], population portraits (1) [63], built environment and health outcomes (1) [64], patterns of living activities (1) [65], trajectories (1) [66], and geospatial patterns of points of interest (1) [67].…”

“…The analysis results showed that low-glucose diets and metformin are positive as anti-aging interventions [33]. Besides, researchers carried out a whole-body MRI-based analysis of biopsychosocial parameters, cardiovascular indexes, metabolomics, lipidomics, and microbiome variables in association with anthropometrics, demographics, and socioeconomic data to quantify the subclinical disease burden occuring with aging in all organ systems [35], or experiments using fMRI to mine the functional connectivity changes during brain aging [38].…”