The relationship between resting‐state amplitude fluctuations and memory‐related deactivations of the default mode network in young and older adults

Human Brain Mapping, Journal Year: 2023, Volume and Issue: 44(9), P. 3586 - 3609

Published: April 13, 2023

Abstract The default mode network (DMN) typically exhibits deactivations during demanding tasks compared to periods of relative rest. In functional magnetic resonance imaging (fMRI) studies episodic memory encoding, increased activity in DMN regions even predicts later forgetting young healthy adults. This association is attenuated older adults and, some instances, remembering rather than forgetting. It yet unclear whether this phenomenon due a compensatory mechanism, such as self‐referential or schema‐dependent it reflects overall reduced modulation age. We approached question by systematically comparing successful encoding and tonic, task‐independent, at rest sample 106 (18–35 years) 111 (60–80 participants. Using voxel‐wise multimodal analyses, we assessed the age‐dependent relationship between resting‐state amplitude (mean percent fluctuation, mPerAF) fMRI signals related well their age‐related hippocampal volume loss, while controlling for regional grey matter volume. Older showed lower amplitudes task‐related deactivations. However, negative mPerAF subsequent effect within precuneus was observed only young, but not Hippocampal volumes no with mPerAF. Lastly, higher tend show performance, pointing towards importance maintained ability modulate old

Language: Английский

---

Single‐value scores of memory‐related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging

Human Brain Mapping, Journal Year: 2023, Volume and Issue: 44(8), P. 3283 - 3301

Published: March 27, 2023

Memory-related functional magnetic resonance imaging (fMRI) activations show age-related differences across multiple brain regions that can be captured in summary statistics like single-value scores. Recently, we described two scores reflecting deviations from prototypical whole-brain fMRI activity of young adults during novelty processing and successful encoding. Here, investigate the brain-behavior associations these with neurocognitive changes 153 healthy middle-aged older adults. All were associated episodic recall performance. The memory network scores, but not additionally correlated medial temporal gray matter other neuropsychological measures including flexibility. Our results thus suggest novelty-network-based high encoding-network-based capture individual aging-related functions. More generally, our memory-related provide a comprehensive measure dysfunction may contribute to cognitive decline.

Language: Английский

---

Single-value brain activity scores reflect both severity and risk across the Alzheimer’s continuum

Brain, Journal Year: 2024, Volume and Issue: 147(11), P. 3789 - 3803

Published: May 14, 2024

Abstract Single-value scores reflecting the deviation from (FADE score) or similarity with (SAME prototypical novelty-related and memory-related functional MRI activation patterns in young adults have been proposed as imaging biomarkers of healthy neurocognitive ageing. Here, we tested utility these potential diagnostic prognostic markers Alzheimer’s disease (AD) risk states like mild cognitive impairment (MCI) subjective decline (SCD). To this end, analysed subsequent memory data individuals SCD, MCI AD dementia well controls first-degree relatives patients (AD-rel) who participated multi-centre DELCODE study (n = 468). Based on individual participants’ whole-brain novelty responses, calculated FADE SAME assessed their association stage, neuropsychological test scores, CSF amyloid positivity APOE genotype. Memory-based showed a considerably larger reference sample groups compared to controls, SCD AD-rel. In addition, novelty-based significantly differed between groups. Across entire sample, single-value correlated performance. The score further Aβ-positive Aβ-negative AD-rel, ApoE ɛ4 carriers non-carriers Hence, are associated both performance factors for AD. Their warrants exploration, particularly patients.

Language: Английский

---

Inhibitory temporo-parietal effective connectivity is associated with explicit memory performance in older adults

iScience, Journal Year: 2023, Volume and Issue: 26(10), P. 107765 - 107765

Published: Aug. 29, 2023

Successful explicit memory encoding is associated with inferior temporal activations and medial parietal deactivations, which are attenuated in aging. Here we used dynamic causal modeling (DCM) of functional magnetic resonance imaging data to elucidate effective connectivity patterns between hippocampus, parahippocampal place area (PPA), precuneus during novel visual scenes. In 117 young adults, DCM revealed pronounced activating input from the PPA hippocampus inhibitory novelty processing, both being enhanced successful encoding. This pattern could be replicated two cohorts (N = 141 148) older adults. cohorts, adults selectively exhibited PPA-precuneus connectivity, correlated negatively performance. Our results provide insight into network dynamics underlying suggest that age-related differences memory-related activity are, at least partly, attributable altered temporo-parietal neocortical connectivity.

Language: Английский

---

Prediction of cognitive performance differences in older age from multimodal neuroimaging data

GeroScience, Journal Year: 2023, Volume and Issue: 46(1), P. 283 - 308

Published: June 13, 2023

Abstract Differences in brain structure and functional structural network architecture have been found to partly explain cognitive performance differences older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, reported mixed prediction results of selective variables based on features using machine learning (ML). the aim current study was investigate general validity from imaging data healthy adults. In particular, focus with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state connectivity (RSFC), (SC) estimates, improve predictability targets, (2) arise global cognition distinct profiles, (3) generalize across different ML approaches 594 adults (age range: 55–85 years) 1000BRAINS study. Prediction examined each modality all combinations, without confound (i.e., age, education, sex) regression analytic options, variations algorithms, feature sets, concatenation vs. stacking). Results showed that differed considerably between deconfounding strategies. absence demographic confounder control, successful could be observed choices. Combination modalities tended marginally compared single modalities. Importantly, previously described effects vanished strict control condition. Despite a small trend benefit, developing biomarker aging remains challenging.

Language: Английский

---

Distinct MRI-based functional and structural connectivity for antidepressant response prediction in major depressive disorder

Clinical Neurophysiology, Journal Year: 2024, Volume and Issue: 160, P. 19 - 27

Published: Feb. 9, 2024

Language: Английский

---

Openness to Experience is associated with neural and performance measures of memory in older adults

Social Cognitive and Affective Neuroscience, Journal Year: 2023, Volume and Issue: 18(1)

Published: Jan. 1, 2023

Age-related decline in episodic memory performance is a well-replicated finding across numerous studies. Recent studies focusing on aging and individual differences found that the Big Five personality trait Openness to Experience (hereafter: Openness) associated with better older adults, but neural mechanisms are largely unclear. Here, we investigated relationship between network function sample of 352 participants (143 50-80 years; 209 young 18-35 years). Participants underwent functional magnetic resonance imaging (fMRI) during visual encoding task. Functional brain-network integrity was assessed using similarity activations (SAME) scores, which reflect participant's activity compared prototypical fMRI patterns adults. NEO Five-Factor Inventory. Older vs adults showed lower higher deviation (i.e. SAME scores). Specifically high performance, mediation analysis this partially mediated by scores. Our results suggest may constitute protective factor cognitive preservation brain's network.

Language: Английский

---

Longitudinal evidence for a mutually reinforcing relationship between white matter hyperintensities and cortical thickness in cognitively unimpaired older adults

Alzheimer s Research & Therapy, Journal Year: 2024, Volume and Issue: 16(1)

Published: Oct. 28, 2024

Language: Английский

---

Diffusion Deep Learning for Brain Age Prediction and Longitudinal Tracking in Children Through Adulthood

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 20, 2023

Abstract Deep learning (DL)-based prediction of biological age in the developing human from a brain magnetic resonance image (MRI) (“ ”) may have important diagnostic and therapeutic applications as non-invasive biomarker health, aging, neurocognition. While previous deep tools for predicting shown promising capabilities using single-institution, cross-sectional datasets, our work aims to advance field by leveraging multi-site, longitudinal data with externally validated independently implementable code facilitate clinical translation utility. This builds on prior foundational efforts modeling enable broader generalization individual’s development. Here, we leveraged 32,851 T1-weighted MRI scans healthy children adolescents aged 3 30 16 multisite datasets develop evaluate several DL frameworks, including novel regression diffusion network (AgeDiffuse). In external validation (5 datasets), found that AgeDiffuse outperformed conventional mean absolute error (MAE) 2.78 years (IQR:[1.2-3.9]). second, separate (3 yielded an MAE 1.97 (IQR: [0.8-2.8]). We predictions reflected age- related structure volume changes better than (R2=0.48 vs R2=0.37). Finally, predicted tracked closely chronological at individual level. To independent application, made publicly available usable research community. Highlights Diffusion models trained large dataset (AgeDiffuse) accurate pediatric prediction. demonstrates relatively stable performance multiple sets across people – 30. Our pipeline is accessible, encouraging collaboration progress research.

Language: Английский

---

Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood

Imaging Neuroscience, Journal Year: 2024, Volume and Issue: 2, P. 1 - 14

Published: March 1, 2024

Abstract Deep learning (DL)-based prediction of biological age in the developing human from a brain magnetic resonance imaging (MRI) (“brain age”) may have important diagnostic and therapeutic applications as non-invasive biomarker health, aging, neurocognition. While previous deep tools for predicting shown promising capabilities using single-institution, cross-sectional datasets, our work aims to advance field by leveraging multi-site, longitudinal data with externally validated independently implementable code facilitate clinical translation utility. This builds on prior foundational efforts modeling enable broader generalization individual’s development. Here, we leveraged 32,851 T1-weighted MRI scans healthy children adolescents aged 3 30 16 multisite datasets develop evaluate several DL frameworks, including novel regression diffusion network (AgeDiffuse). In external validation (5 datasets), found that AgeDiffuse outperformed conventional mean absolute error (MAE) 2.78 years (interquartile range [IQR]: [1.2-3.9]). second, separate (3 yielded an MAE 1.97 (IQR: [0.8-2.8]). We predictions reflected age-related structure volume changes better than (R2 = 0.48 vs. R2 0.37). Finally, predicted tracked closely chronological at individual level. To independent application, made publicly available usable research community.

Language: Английский

---