Behaviorally meaningful functional networks mediate the effect of Alzheimer’s pathology on cognition DOI
Jacob Ziontz, Theresa M. Harrison, Xi Chen

et al.

Cerebral Cortex, Journal Year: 2024, Volume and Issue: 34(4)

Published: April 1, 2024

Abstract Tau pathology is associated with cognitive impairment in both aging and Alzheimer’s disease, but the functional structural bases of this relationship remain unclear. We hypothesized that integrity behaviorally meaningful networks would help explain between tau performance. Using resting state fMRI, we identified unique related to episodic memory executive function domains. The network was particularly measured positron emission tomography entorhinal temporal cortices. Further, strength mediated performance above beyond neurodegeneration. replicated association these a separate cohort older adults, including cognitively unimpaired mildly impaired individuals. Together, results suggest brain represent mechanism linking cognition.

Language: Английский

Consensus Paper: Cerebellum and Ageing DOI
Angelo Arleo, Martin Bareš, Jessica A. Bernard

et al.

The Cerebellum, Journal Year: 2023, Volume and Issue: 23(2), P. 802 - 832

Published: July 10, 2023

Language: Английский

Citations

45
The future of human behaviour research DOI Open Access
Janet M. Box‐Steffensmeier, Jean Burgess, Maurizio Corbetta

et al.

Nature Human Behaviour, Journal Year: 2022, Volume and Issue: 6(1), P. 15 - 24

Published: Jan. 27, 2022

Language: Английский

Citations

62
A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity DOI
Rongtao Jiang, Dustin Scheinost, Nianming Zuo

et al.

Advanced Science, Journal Year: 2022, Volume and Issue: 9(24)

Published: July 10, 2022

Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in brain terms neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it shown both cognitive function (domains spanning executive function, emotion processing, motor memory) human can be reliably predicted from unique patterns functional connectivity, models generalizable external datasets (n 533 453). Results show aging manifest decrease within-network connections (especially default mode ventral attention networks) increase between-network (somatomotor network). Whereas dorsal network an exception, which highly predictive ability but weakly correlated Further, positively weighted predicting fluid intelligence significantly mediate its association age. Together, findings offer insights into why often associated organization, indicating process dedifferentiation compensational theory.

Language: Английский

Citations

43
Qigong exercise enhances cognitive functions in the elderly via an interleukin-6-hippocampus pathway: A randomized active-controlled trial DOI Creative Commons
Di Qi, Nichol M. L. Wong, Robin Shao

et al.

Brain Behavior and Immunity, Journal Year: 2021, Volume and Issue: 95, P. 381 - 390

Published: April 18, 2021

Evidence has suggested that exercise protects against cognitive decline in aging, but the recent lockdown measures associated with COVID-19 pandemic have limited opportunity for outdoor exercise. Herein we tested effects of an indoor exercise, Qigong, on neurocognitive functioning as well its potential neuro-immune pathway.We conducted a 12-week randomized active-controlled trial two study arms cognitively healthy older people. We applied Wu Xing Ping Heng Gong (Qigong), which was designed by experienced Daoist Qigong master, to experimental group, whereas physical stretching control group. The consisted range movements involving and legs, turning torso, relaxing, would follow fundamental principles Daoism traditional Chinese medicine (e.g., Qi). measured aging-sensitive abilities, serum interleukin-6 (IL-6) levels, brain structural volumes (Qigong, n = 22) groups (stretching, 26) before after training.We observed caused significant improvement processing speed (t (46) 2.03, p 0.048) sustained attention -2.34, 0.023), increased hippocampal volume (41) 3.94, < 0.001), reduced peripheral IL-6 levels -3.17, 0.003). Moreover, following training, greater reduction increase performance (bootstrapping CI: [0.16, 3.30]) more training-induced effect [-0.35, -0.004]).Overall, these findings offer insight into mechanistic role IL-6-and intricate interplay neural processes-in beneficial Qigong. profound implications early identification intervention individuals vulnerable decline, focusing pathway. registered at clinicaltrials.gov (identifier: NCT04641429).

Language: Английский

Citations

46
Neurophysiological and behavioral effects of multisession prefrontal tDCS and concurrent cognitive remediation training in patients with autism spectrum disorder (ASD): A double-blind, randomized controlled fNIRS study DOI Creative Commons
Yvonne M. Y. Han, Melody M.Y. Chan,

Caroline KS Shea

et al.

Brain stimulation, Journal Year: 2022, Volume and Issue: 15(2), P. 414 - 425

Published: Feb. 15, 2022

Language: Английский

Citations

34
Multimodal brain connectome-based prediction of suicide risk in people with late-life depression DOI Creative Commons
Mengxia Gao, Nichol M. L. Wong, Chemin Lin

et al.

Nature Mental Health, Journal Year: 2023, Volume and Issue: 1(2), P. 100 - 113

Published: Feb. 17, 2023

Abstract Suicidal ideation, plans and behavior are particularly serious health issues among the older population, resulting in a higher likelihood of deaths than any other age group. The increasing prevalence depression late life reflects urgent need for efficient screening suicide risk people with late-life depression. Employing cross-sectional design, we performed connectome-based predictive modelling using whole-brain resting-state functional connectivity white matter structural data to predict patients ( N = 37 non-suicidal patients, 24 suicidal ideation/plan, 30 who attempted suicide). Suicide was measured three standardized questionnaires. Brain profiles were used classify groups our dataset two independent datasets machine learning. We found that brain patterns could explained variance up 30.34%. improved classification-prediction accuracy compared questionnaire scores alone be applied identify depressed had datasets. Our findings suggest multimodal capture individual differences patients. models might further tested help clinicians detailed assessments interventions. trial registration number this study is ChiCTR2200066356.

Language: Английский

Citations

18
Fronto-cerebellar connectivity mediating cognitive processing speed DOI Creative Commons
Clive H. Y. Wong, Jiao Liu, Tatia M.C. Lee

et al.

NeuroImage, Journal Year: 2020, Volume and Issue: 226, P. 117556 - 117556

Published: Nov. 13, 2020

Processing speed is an important construct in understanding cognition. This study was aimed to control task specificity for the neural mechanisms underlying cognitive processing speed. Forty young adult subjects performed attention tasks of two modalities (auditory and visual) levels rules (compatible incompatible). Block-design fMRI captured BOLD signals during tasks. Thirteen regions interest were defined with reference publicly available activation maps Cognitive derived from reaction times, which yielded six sets connectivity measures. Mixed-effect LASSO regression revealed significant paths suggestive a cerebello-frontal network predicting Among them, three are long range (two fronto-cerebellar, one cerebello-frontal), short (fronto-frontal, cerebello-cerebellar, cerebello-thalamic). The long-range connections likely relate control, short-range rule-based stimulus-response processes. suggests that automaticity, acting on interplaying effortful top–down attentional accounts

Language: Английский

Citations

35
Predicting attention across time and contexts with functional brain connectivity DOI
Hayoung Song, Monica D. Rosenberg

Current Opinion in Behavioral Sciences, Journal Year: 2021, Volume and Issue: 40, P. 33 - 44

Published: Jan. 29, 2021

Language: Английский

Citations

30
Predicting response time variability from task and resting-state functional connectivity in the aging brain DOI Creative Commons
Oyetunde Gbadeyan,

James T. C. Teng,

Ruchika Shaurya Prakash

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 250, P. 118890 - 118890

Published: Jan. 8, 2022

Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline functioning to deficits goal maintenance inhibition. Despite these well-documented control resources, older adults endorse fewer episodes mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on neural basis has mostly focused young studies predominantly focusing activity connectivity select few canonical networks. However, whole-brain functional networks aging have not yet been characterized. In this study, response time variability—the trial-to-trial fluctuations behavioral responses—as an indirect marker or "out-of-the-zone" state representing suboptimal performance, we show that brain-based predictive variability can be derived from task connectivity. contrast, resting-state alone did predict individual variability. Finally, despite successful within-sample prediction variability, our generalize independent cohorts Overall, findings provide evidence for utility task-based predicting aging. Future research needed derive more robust generalizable models.

Language: Английский

Citations

22
Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome DOI
Guozheng Feng, Yiwen Wang, Weijie Huang

et al.

Human Brain Mapping, Journal Year: 2022, Volume and Issue: 43(12), P. 3775 - 3791

Published: April 27, 2022

An emerging trend is to use regression-based machine learning approaches predict cognitive functions at the individual level from neuroimaging data. However, prediction models are inherently influenced by vast options for network construction and model selection in pipelines. In particular, brain white matter (WM) structural connectome lacks a systematic evaluation of effects different pipeline on predictive performance. Here, we focused methodological connectome-based predictions. For construction, considered two parcellation schemes defining nodes seven strategies edges. regression algorithms, used eight models. Four domains age were targeted as tasks based independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects [HCP-A]: 560 adults). Based results, WM provided satisfying ability functions, especially executive function attention. Second, induce significant difference Third, results data sets showed that dMRI with distinct acquisition parameters may plausibly result preference proper fiber reconstruction algorithms weighting options. Finally, deep Elastic-Net more accurate robust Together, performances identified this study, which provide important references guidelines select suitable future studies field.

Language: Английский

Citations

21