Characterization of the angular gyrus in an older adult population: a multimodal multilevel approach

Jockwitz, Christiane; Krämer, Camilla; Stumme, Johanna; Dellani, Paulo R.; Moebus, Susanne; Bittner, Nóra; Caspers, Svenja

doi:10.1007/s00429-022-02529-3

Cited by 5 publications

(4 citation statements)

References 102 publications

(125 reference statements)

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“…This is also supported by the feature importance analyses in the current study. Without the addition of extra features, relevant regions for prediction included parts of the lingual/fusiform/parahippocampal gyrus (visual network) and the inferior parietal lobule/angular gyrus (DMN), which not only seem to be involved in different language-related functions, but also to be predictive of language abilities in older age [17,[99][100][101][102]. However, once added to the ML models, educational level appeared to be the most important feature for the prediction of verbal memory & language and with it to explain a large portion of variance in the target, which corresponds to prior research reporting strong associations between language measures and educational level [103,104].…”

Section: Discussionmentioning

confidence: 99%

“…However, despite robust findings at the group level, cognitive performance has been found to vary greatly at the individual level [1,14], particularly in the older ages. In light of the increasing aging population and high relevance of cognitive health for the quality of life of healthy older adults, research has turned to searching for a neuroimaging marker for individual cognitive ability in aging [11,[15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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

Krämer¹,

Stumme²,

Campos³

et al. 2023

GeroScience

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

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Krämer¹,

Stumme²,

Campos³

et al. 2023

GeroScience

Self Cite

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“…The lingual gyrus is involved in visual processing, particularly in recognizing words and faces [56]. The observed connectivity in these regions suggests that testosterone may support and enhance these specific cognitive processes [57][58][59].…”

Section: Discussionmentioning

confidence: 99%

Sex-steroid hormones relate to cerebellar structure and functional connectivity across adulthood

Magalhães,

Hicks,

Jackson

et al. 2024

Preprint

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Aging involves complex biological changes that affect disease susceptibility and aging trajectories. Although females typically live longer than males, they have a higher susceptibility to diseases like Alzheimer’s, speculated to be influenced by menopause, and reduced ovarian hormone production. Understanding sex-specific differences is crucial for personalized medical interventions and gender equality in health. Our study aims to elucidate sex differences in regional cerebellar structure and connectivity during normal aging by investigating both structural and functional connectivity variations, with a focus on investigating these differences in the context of sex-steroid hormones. The study included 138 participants (mean age = 57(13.3) years, age range = 35-86 years, 54% women). The cohort was divided into three groups: 38 early middle-aged individuals (EMA) (mean age = 41(4.7) years), 48 late middle-aged individuals (LMA) (mean age = 58(4) years), and 42 older adults (OA) (mean age = 72(6.3) years). All participants underwent MRI scans, and saliva samples were collected for sex-steroid hormone quantification (17β-estradiol (E), progesterone (P), and testosterone (T)). We found less connectivity in females between Lobule I-IV and the cuneus, and greater connectivity in females between Crus I, Crus II, and the precuneus with increased age. Higher 17β-estradiol levels were linked to greater connectivity in Crus I and Crus II cerebellar subregions. Analyzing all participants together, testosterone was associated with both higher and lower connectivity in Lobule I-IV and Crus I, respectively, while higher progesterone levels were linked to lower connectivity in females. Structural differences were observed, with EMA males having larger volumes compared to LMA and OA groups, particularly in the right I-IV, right Crus I, right V, and right VI. EMA females showed higher volumes in the right lobules V and VI. These results highlight the significant role of sex hormones in modulating cerebellar connectivity and structure across adulthood, emphasizing the need to consider sex and hormonal status in neuroimaging studies to better understand age-related cognitive decline and neurological disorders.

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“…Another crucial region in cognition is R106 (right angular gyrus), which is associated with language, spatial, and memory functions [Seghier, 2013, Humphreys et al, 2021]. The aging process leads to structural atrophy in the angular gyrus, which is linked to subjective and mild cognitive impairments, as well as dementia [Karas et al, 2008, Jockwitz et al, 2023]. Additionally, another significant region, R116 (right entorhinal area), often exhibits the earliest histological alterations in AD.…”

Section: An Application To the Proteomics-radiomics Study Of Admentioning

confidence: 99%

HILAMA: High-dimensional multi-omic mediation analysis with latent confounding

Wang,

Liu,

et al. 2023

Preprint

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Motivation: The increasingly available multi-omic datasets have posed both new opportunities and challenges to the development of quantitative methods for discovering novel mechanisms in biomedical research. One natural approach to analyzing such datasets is mediation analysis originated from the causal inference literature. Mediation analysis can help unravel the mechanisms through which exposure(s) exert the effect on outcome(s). However, existing methods fail to consider the case where (1) both exposures and mediators are potentially high-dimensional and (2) it is very likely that some important confounding variables are unmeasured or latent; both issues are quite common in practice. To the best of our knowledge, however, no methods have been developed to address these challenges with statistical guarantees. Results: In this article, we propose a new method for HIgh-dimensional LAtent-confounding Mediation Analysis, abbreviated as "HILAMA", that considers both high-dimensional exposures and mediators, and more importantly, the possible existence of latent confounding variables. HILAMA achieves false discovery rate (FDR) control under finite sample size for multiple mediation effect testing. The proposed method is evaluated through extensive simulation experiments, demonstrating its improved stability in FDR control and superior power in finite sample size compared to existing competitive methods. Furthermore, our method is applied to the proteomics-radiomics data from ADNI, identifying some key proteins and brain regions relating to Alzheimer's disease. The results show that HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables. Availability: The R package HILAMA is publicly available at https://github.com/Cinbo Wang/HILAMA. Contact: cinbo_w@sjtu.edu.cn

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Characterization of the angular gyrus in an older adult population: a multimodal multilevel approach

Cited by 5 publications

References 102 publications

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

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

Sex-steroid hormones relate to cerebellar structure and functional connectivity across adulthood

HILAMA: High-dimensional multi-omic mediation analysis with latent confounding

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