• Asa Farahani, 
• Zhen-Qi Liu, 
• Filip Morys, 
• Roqaie Moqadam, 
• Yashar Zeighami, 
• Mahsa Dadar, 
• Alain Dagher, 
• Bratislav Misic

x

Abstract

Citation: Farahani A, Liu Z-Q, Morys F, Moqadam R, Zeighami Y, Dadar M, et al. (2026) Aging and metabolism contribute separately to brain–body health. PLoS Biol 24(6): e3003856. https://doi.org/10.1371/journal.pbio.3003856

Academic Editor: Claus C. Hilgetag, University Medical Center Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf, GERMANY

Received: November 20, 2025; Accepted: June 1, 2026; Published: June 15, 2026

Copyright: © 2026 Farahani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Due to the Human Connectome Project-Aging (HCP-A) and UK Biobank data sharing policy, the data used in this study could not be shared though authors of this paper. The HCP-A data are accessible through https://www.humanconnectome.org/study/hcp-lifespan-aging. The UK Biobank data are accessible through https://www.ukbiobank.ac.uk. All code used to perform the analyses and data underlying the main figures are available on GitHub at https://github.com/netneurolab/Farahani_Age_Metabolism and on Zenodo at https://zenodo.org/records/20412555 (DOI: 10.5281/zenodo.20412555). The processed numerical data underlying the graphs presented in the figures are also available on GitHub and Zenodo.

Funding: AF acknowledges support from the Molson Foundation (https://www.mcgill.ca/neuro/training/financial-support/studentships). BM acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2017-04265) (https://nserc-crsng.canada.ca), Canadian Institutes of Health Research (CIHR) (PJT-180439) (https://cihr-irsc.gc.ca), Brain Canada Foundation Future Leaders Fund (https://braincanada.ca), the Canada Research Chairs Program (CRC-2022-00169) (https://www.chairs-chaires.gc.ca), the Michael J. Fox Foundation (MJFF-021133) (https://www.michaeljfox.org), and the Healthy Brains for Healthy Lives initiative (https://www.mcgill.ca/hbhl). The funders have no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: ALK, alkaline phosphatase; ALT, alanine aminotransferase; AMICO, Accelerated Microstructure Imaging via Convex Optimization; AP, anterior-to-posterior; ASL, arterial spin labeling; AST, aspartate aminotransferase; ATT, arterial transit time; BMI, body mass index; DKT, Desikan-Killiany-Tourville; EPI, echo-planar imaging; FA, fractional anisotropy; FC, functional connectivity; FDR, false discovery rate; FFAs, free fatty acids; FLAIR, fluid-attenuated inversion-recovery; fMRI, functional MRI; FMRIB, Functional Magnetic Resonance Imaging of the Brain; FSH, follicle-stimulating hormone; GAMLSS, Generalized additive model for location, scale, and shape; GRE, gradient-recalled echo; HCP–A, Human Connectome Project–Aging; HDL, high-density lipoprotein; ICVF, intracellular volume fraction; IDPs, imaging-derived phenotypes; ISOVF, isotropic volume fraction; JHU, Johns Hopkins University; LDL, low-density lipoprotein; LH, luteinizing hormone; LV–I, first latent variable; LV–II, second latent variable; LVs, latent variables; MAP, mean arterial pressure; MB, multi-band; MD, mean diffusivity; MPRAGE, multi-echo magnetization-prepared rapid gradient echo; MRI, magnetic resonance imaging; NIMH, National Institute of Mental Health; NODDI, Neurite Orientation Dispersion and Density Imaging; OCD, obsessive-compulsive disorder; PA, posterior-to-anterior; pCASL, pseudo-continuous arterial spin labeling; PLS, partial least squares; RBC, red blood cell; SC, structural connectivity; SVD, singular value decomposition; TE, echo times; TI, inversion time; TIA, transient ischemic attack; TR, repetition time; WMH, white matter hyperintensity

Introduction

The human brain and body are highly interconnected throughout the life span [1,2]. Yet, the study of aging has typically treated cerebral and peripheral systems as separate entities. Currently, population-based aging trajectories are established for cortical thickness and gray matter density [3–7], brain volume and morphology [8–10], myelination [11,12], structural connectome [13–15], functional connectome [16–19], and cerebral blood perfusion [20,21]. Similarly, age-dependent trajectories are well-characterized for peripheral biomarkers, including body mass index (BMI), waist circumference, blood pressure, cholesterol, creatinine, albumin, bilirubin, glucose, glycosylated hemoglobin (HbA1c), high- and low-density lipoproteins (HDL and LDL), and endocrine measures such as testosterone and follicle-stimulating hormone [22–29]. An emerging question is how these brain- and body-level measurements relate to each other.

Multi-modal neuroimaging is increasingly used to quantify the effects of typical aging and neurological disease on multiple tissue types and biological systems [30]. For instance, brain blood perfusion reduction and vascular dysregulation are linked with greater dementia risk, and can precede observable brain atrophy and clinical symptom onset of the disease by years [31–36]. Nevertheless, routine clinical evaluation still relies more heavily on demographic information (age and sex) and laboratory-based assessments of blood and urine panels than on neuroimaging workup. Structural magnetic resonance imaging (MRI) (T1- and T2-weighted MRI) is used in occasional relevant scenarios—to rule out the possibility of focal neurological lesions such as primary or metastatic tumors or to indicate the presence of dominant atrophy—while more advanced MR imaging techniques such as functional MRI (fMRI), diffusion MRI, and arterial spin labeling (ASL) feature less prominently in clinical decision-making.

Recognizing the potential for peripheral biomarkers to serve as readily accessible indicators of brain health, there is greater effort to identify translatable brain–body relationships. Initial efforts have focused on isolating specific brain–body associations between individual features of interest. Notable examples include relationships between reduced glucose tolerance and hippocampal atrophy [37], between obesity and frontal cortex thinning and subcortical volume reduction [38–40], and between hypertension and reductions in blood perfusion [41] and cortical thinning [42]. However, most of these studies focus on single brain measurements or a compact set of physiological markers. Without incorporating comprehensive measurements from both neural and peripheral systems, we cannot fully compare the sensitivity of brain features to particular physiological signatures.

Here we ask two questions. First, what can be inferred about brain health from individuals’ demographic and laboratory profiles? Second, among various MRI-derived brain measurements, spanning structural, functional, and vascular domains, which are most closely associated with specific physiological phenotypes? We address these questions using partial least squares (PLS) multivariate pattern learning to assess the covariance between multi-modal MRI brain measurements and a broad set of demographics and physiological characteristics [43–45]. We analyze two datasets with comprehensive neuroimaging and peripheral multi-omics: the Human Connectome Project–Aging [46–48] and UK Biobank [49]. We identify two generalizable axes of brain–body associations in both datasets and in both biological sexes (males and females): an aging axis and a metabolic axis. Furthermore, we identify brain features that contribute most to each axis. Finally, we examine how an individual’s position along these axes relates to their cognitive performance.

Results

The discovery dataset is obtained from the Human Connectome Project–Aging (HCP–A; https://www.humanconnectome.org/study/hcp-lifespan-aging). HCP–A data come from 597 participants (268 males; 329 females) aged 36–100 years [46–48]. Detailed information on participants’ demographics, data acquisition, and preprocessing is provided in Methods.

We use partial least squares (PLS) [43–45] to assess how participants’ age, blood pressure, body mass index (BMI), and plasma measurements (biomarkers; S1 Fig and S1 Table) covary with brain measurements derived from T1- and T2-weighted structural imaging, diffusion and functional magnetic resonance imaging, and arterial spin labeling (ASL) imaging. This multivariate analysis identified two statistically significant (assessed by permutation testing) and cross-validated latent variables (LVs) linking biomarkers to brain measurements in both males and females. In the following sections, we discuss these latent variables in detail. Throughout this report, we stratify the analysis by biological sex. There exist known differences between males and females in both brain and biomarker measures. For example, females typically exhibit higher cerebral blood perfusion than males [20,50–58], along with distinct hormonal trajectories and notable differences in certain plasma markers (e.g., creatinine and lipid markers) [59–63].

Finally, we use the UK Biobank dataset (https://www.ukbiobank.ac.uk) to replicate and validate the findings. UK Biobank data comprise 3,013 participants (1,431 males; 1,582 females) aged 51–83 years [49,64]. The biomarkers from this cohort are shown in S2 Fig and S2 Table.

We show biomarker intercorrelations for the HCP–A and UK Biobank cohorts in S3 and S4 Figs, respectively.

Discussion

In the present report, we analyze data of HCP–Aging and UK Biobank cohorts to characterize the multivariate relationships between a series of brain measurements and bodily physiological phenomics—including age, body mass index (BMI), blood pressure, and plasma/urine protein profiles [46–49,64]. The PLS analysis [43–45] reveals two principal axes of brain–body associations: one driven primarily by age, and the other by metabolic health. The most prominent axis of association is age-related. In both datasets, increased age is associated with cortical thinning and microstructural alteration, along with longer arterial transit time and reduced cerebral blood perfusion. The second axis of brain–body covariance is related to metabolic features. Elevated BMI, blood pressure, HbA1c, glucose, and ALT liver enzyme, along with lower HDL cholesterol are linked to lower brain blood perfusion. We further show that the captured metabolic effect on the brain is accompanied by lower cognitive flexibility, reflecting the potential behavioral consequences of the observed brain–body relationships.

Aging is a time-dependent biological phenomenon that affects every system in the human body [101], from the skeletal and muscular to the cardiovascular and nervous systems. Previous research on brain aging has established typical aging trajectories for various neuroimaging measures that serve as benchmarks to detect pathological deviations from the norm [18]. Age-related brain changes are now documented across multiple brain features, including cortical thickness [3,5,6,102–105], gyrification [104], brain tissue volume [13,18,106], FA [107–111], MD [13,109,110,112,113], SC [14,114,115], FC [17,19,114,116–119], and blood perfusion [20,21,58]. Across these modalities, there is growing consensus regarding age-related structural deterioration and reorganization of brain vascular and functional networks.

PLS brain loadings for the aging axis show consistent directionality in both datasets, reflecting brain structural deterioration alongside cerebrovascular decline. Specifically, we recapitulate age-related structural deterioration, characterized by , as well as cerebrovascular dysfunction, characterized by . However, while cortical thickness shows the greatest loading magnitude (e.g., most affected by aging) in HCP–Aging, blood perfusion, transit time, and white matter hyperintensity loadings dominate in UK Biobank. The differences in brain loading ranking may originate from distinct age ranges in HCP–Aging (36–100 years) and UK Biobank (51–83 years) cohorts, or from their imaging protocol differences.

Our study resonates with a growing appreciation for studying brain aging from a wider, multivariate perspective, and is in line with the studies that suggest superior brain-age prediction accuracy when incorporating multi-modal brain imaging—T1-weighted MRI, T2-FLAIR, T2*, diffusion-MRI, task- and resting-state fMRI—compared to single-modality approaches [120–124]. Our findings emphasize that the spatial patterns of age-related changes are not identical across “all” brain features. For example, while FA and MD have spatially similar age-related profiles, blood perfusion exhibited a distinct pattern of age-related decline; suggesting that different neuroimaging measures capture complementary aspects of brain aging. We further show that age-related brain loadings are relatively consistent across biological sex groups.

One consistent and reproducible finding across this study is the identification of a metabolic axis linking peripheral physiological markers and the brain. Prior studies have shown that metabolic factors are associated with brain health; for example, increased BMI and abdominal fat have been linked to both functional [125] and structural brain alterations [38,39,126–136]; and increased HbA1c and adiposity-related insulin resistance are linked to cortical thickness changes [137,138]. Extending beyond these previously documented associations, we show that cerebral blood perfusion is the primary brain feature that potentially declines as a function of elevated metabolic risk factors. One possibility is that vascular susceptibility to metabolic risks constitutes the principal biological mechanism underlying the brain–body metabolic axis in aging populations. Elevated LDL cholesterol [139–141], glucose, glycosylated hemoglobin [142], blood pressure [143–145], body fat and possibly BMI [146], as well as low HDL cholesterol [147] increase atherosclerotic and cardiovascular disease risk, compromise vascular integrity, and ultimately impair perfusion.

Various biological pathways have been suggested to explain how metabolic abnormalities may disrupt endothelial function. For instance, HDL is known to have anti-inflammatory and antioxidant properties [148], by mediating cholesterol trafficking and reducing oxidized lipids from LDL, inhibiting endothelial apoptosis and stimulating their migration, and preventing monocyte adhesion to the endothelial surface—a critical step in the development of atherosclerotic plaques [149–151]. When HDL levels are low, vascular resilience is reduced, leaving the brain particularly vulnerable to vascular injury. Indeed, low serum HDL is linked to compromised blood-brain barrier integrity, increased cerebral amyloid burden [152] and enhanced -amyloid accumulation within cerebral vessels [153]; there are also studies that report association of lower HDL (and ApoAI, the major protein component of HDL) with Alzheimer’s disease and individual cognitive performance [153–158]. Taken together, a healthier metabolism may foster vascular resilience and vascular reactivity and sustain healthy cerebral blood perfusion, which further protects against neurodegeneration.

The perfusion-centric metabolic effect can partly explain the disparate structural effects of metabolic risk factors reported to date [159]. While most studies report reductions in frontal cortex volume and thickness in obesity [39,127,128,130,132–134,136], some report overall gray matter loss or even white and gray matter expansion [40,135,160]. In our PLS model, the metabolic axis is characterized by lower frontal cortex thickness and greater occipital cortex thickness in the HCP–A dataset. This pattern is consistent with independent findings [128,135,161,162] and is in line with the findings of Petersen and colleagues [163], who reported a similar cortical thickness pattern associated with metabolic syndrome risk factors when analyzing data of over 40,000 UK Biobank participants. Several mechanisms are proposed for obesity- and metabolic syndrome-related cortical thickening in occipital cortex, ranging from neuroinflammation [162] to enhancement of visual processing and attention networks in obesity [135]. Given that metabolic markers covary most strongly with cerebral blood perfusion in both datasets, future neuroimaging studies of obesity and metabolic syndrome may achieve greater consistency by including blood perfusion measures when assessing brain health.

Our results also suggest a pattern of blood plasma markers and body vitals that are closely associated with brain physiology. Defining “metabolic dysfunction” remains an important challenge in the field, with ongoing debate over which features should be included in clinical definitions [164–167]. Current discussions often focus on including or excluding features from the definition, often without a specific weighting of the relative importance of these plasma and urine factors in the neurologic context. For instance, one definition of metabolic dysfunction is presence of three or more of the listed elements including elevated glucose, blood pressure, triglycerides, waist circumference, and reduced HDL [168]. Our cross-dataset and cross-sex consensus suggests that higher HDL cholesterol may be neuroprotective, while elevated BMI, HbA1c, blood pressure, and ALT consistently emerge as factors associated with lower brain blood perfusion.

This study provides a framework for studying the mechanisms of neuroprotection and vulnerability in metabolic dysfunction. Not all individuals exposed to metabolic dysfunction experience equivalent brain outcomes. Some individuals demonstrate resilience to expected metabolic brain correlates despite peripheral risk factors, while some show heightened vulnerability (discrepancy between brain and peripheral biomarker profiles; see S27 Fig). While part of this discrepancy may originate from measurement errors, it is also conceivable that genetic predisposition and environmental factors modulate the heterogeneity of interactions between brain and peripheral features [169]. Our framework quantifies this discrepancy and provides an opportunity for targeted genetic investigations to identify genetic variants that may confer resilience to brain alterations caused by metabolic risks [170] and to test the contribution of behavioral and lifestyle factors—such as smoking, physical activity, socioeconomic status, and pollution exposure—on shaping this discrepancy. One plausible example of how genetics can alter the association between brain and bodily metabolic signature is detailed here: vascular dysfunction and arterial stiffness partially mediate the link between systemic metabolic conditions and brain blood flow, function and structure [171], meanwhile, specific genes are known to contribute to individuals’ propensity to develop arterial stiffening [172,173], which may, in turn, amplify the downstream effects of metabolic abnormalities on the brains of these vulnerable individuals. It is important to note that in the present report resilience and vulnerability are defined with respect to normative brain–body associations estimated across the full cohort. These cross-sectional findings should therefore be replicated in longitudinal studies to assess individual vulnerability and resilience over time.

The brain–body metabolic axis may in part reflect background genetic predisposition rather than a direct causal effect of peripheral metabolic dysfunction on the brain. Most obesity-associated genes are expressed in the central nervous system, with obesity-associated loci overlapping with genes and pathways implicated in neurodevelopment [174,175]. These genetic influences extend beyond energy homeostasis and reward-related brain areas, and affect broadly distributed neural circuits across the brain [176]. In other words, rather than the presence of a pure “causal” relationship between metabolic dysfunction and brain features, it is possible that individuals with higher genetic risk for obesity also have genetic propensity for the brain patterns identified in this study [135]. In line with this notion, Morys and colleagues have shown that polygenic risk scores for obesity are negatively correlated with frontal cortex thickness and volume in adolescents (ABCD cohort; mean age = 10 years) [177]. This brain pattern is related to increased impulsivity and subsequent BMI increase at one-year follow-up [178]. Hence, genetic predisposition may initiate chronic obesity and cardiovascular consequences that further impact the brain in older individuals. In either case, reversible brain alterations (both functional and structural) following weight loss [179–184] highlight the contribution of modifiable lifestyle factors in maintaining brain health. Indeed, weight loss, following either Roux-en-Y gastric bypass surgery [185] or a simple 12-month behavioral change (diet and exercise) has been shown to increase cerebral blood flow [186].

Lifestyle changes that ameliorate (or avoid) metabolic dysfunction can have a significant clinical relevance due to their influence on brain blood perfusion health. Hypoperfusion and vascular dysfunction impair the brain’s clearance of solutes and metabolites through compromised interstitial fluid drainage leading to accumulation of toxic proteins [187,188]. Simultaneously, hypoperfusion induces oxidative stress, promoting the formation of misfolded proteins and amyloid- () precursors, and further reducing the expression and activity of neprilysin, a major -degrading enzyme [189]. These pathological processes create a vicious cycle, as peptides themselves can further impair vasoregulation [190] and reduce blood flow [191], perpetuating the hypoperfusion that initially contributed to their formation. Meanwhile, animal studies have shown that induced hypoperfusion can accelerate cerebral amyloid angiopathy [192]. In short, a potential mechanistic explanation is that obesity leads to cerebrovascular alterations, which in turn facilitates amyloid- deposition in brain tissue (leading to Alzheimer’s disease) and amyloid formation in brain vasculature system (leading to cerebral amyloid angiopathy). Furthermore, reduced blood flow due to arterial narrowing promotes thrombosis and increases risk of emboli formation [193]. Maintaining metabolic health may thus prevent or slow down neurodegenerative cascades by preserving brain perfusion.

The present study is part of a wider effort to understand links between the brain and the body. These bidirectional interactions are increasingly explored in the context of the gut-brain axis [194] and the proteomics-brain axis [195,196]. The gut-brain axis consists of two communication mechanisms: one mediated by chemicals including neurotransmitters, neuropeptides, hormones, and gut microbiome [197], and the other mediated by the physical connections via the vagus nerve. There is evidence that this axis of interaction plays a role in obesity and metabolic health via serotonergic signaling [198,199]. Concurrently, proteomics can also help to monitor brain health by defining protein signatures associated with obesity and type 2 diabetes. The identified protein profiles enable the identification of potential target molecules for effective intervention plans [195,196,200–203].

Importantly, both aging and metabolic brain signatures have behavioral relevance across participants. We replicate well-known associations between aging and cognitive domains; in particular, we find the relationship between aging and diminished cognitive flexibility [204–207]. In addition, participants with greater scores on the metabolic dysfunction axis show greater cognitive impairment compared to their metabolically healthier counterparts. This finding aligns with reports documenting the interplay between diabetes mellitus type 2, low cerebral blood flow and worse memory, executive function and processing speed [208], as well as studies linking cortical thickness or brain structural volume signatures of BMI and metabolic status to participants’ working memory performance [209], fluid intelligence and prospective memory [210]. Note, however, that there are several studies that find no significant associations between specific metabolic syndrome features and cognition [211,212]. The present report shows that a composite metabolic brain–body profile can identify links between physiological measurements and cognitive outcomes.

Our results point to possible sex differences in metabolic-cognitive associations. In the HCP–A dataset, greater metabolic deviation from health is associated with lower fluid cognition scores in females, whereas no significant negative associations survive multiple comparison correction in males. Consistent with prior reports [131,213–216], we also find that metabolic burden affects brain structure and function differently in males and females (e.g., as reflected by partial divergence in brain loadings; see S11 and S13 Figs). Greater female cognitive susceptibility to metabolic dysregulation is also reported in multiple independent studies [91–96]. The potentially greater cognitive effects observed in females may account for the higher Alzheimer’s disease prevalence in females compared to males [217]. Differential cognitive risk and resilience may be driven by sex-related factors ranging from physiological differences to acquired socioeconomic disparities [218,219]. For example, sex hormones may influence blood-brain barrier integrity, endothelial function, and vascular regulation [220–223]—systems that are also sensitive to metabolic dysfunction—thereby shaping sex-specific vulnerability to metabolic brain injury. Metabolic health and sex differences should be integrated in future aging and neurodegenerative disease studies to understand cognitive resilience disparities across individuals in greater detail.

The present findings should be interpreted with respect to several methodological considerations. First, cross-sectional associations between brain measurements and peripheral biomarkers are not causal—longitudinal studies, and interventional studies—e.g., brain imaging before and after obesity-reduction trials—can help understand the nature of associations (causal or co-existence). Second, while our findings suggest that ASL imaging may offer greater sensitivity for obesity and metabolism research, the inherently low signal-to-noise ratio of this imaging modality and its sensitivity to obesity-related confounds (such as motion artifacts, different body composition, and head placement [224–226]) is an ongoing topic of research. Encouragingly, changes in FC strength and in perfusion in the presence of metabolic dysfunction are similar. Third, in the UK Biobank dataset, laboratory-based biomarkers were acquired at the baseline visit, whereas the neuroimaging data were acquired during a follow-up session (time interval between sessions can span several years, see S2 Fig). We addressed this temporal discrepancy by using GAMLSS models to regress out age effects from all features entered into the PLS analysis. Fourth, the present statistical model is sensitive only to linear relationships between brain and body features [43], and does not consider the possibility that multiple such relationships are non-linear. In other words, the latent axes identified in this study do not characterize the full picture of the underlying brain–body associations which may have a complex non-linear nature. As comprehensively phenotyped datasets become larger, it may be possible to fit more complete and complex models that additionally encode non-linear relationships between brain features and systemic biomarkers. Last, many of the biomarkers included in this study are directly linked to the metabolic status of the body and are highly interconnected (e.g., HDL, LDL, BMI, total cholesterol). Indeed, the choice of biomarkers shapes the reported axes of brain–body covariance in this report and contributes to the prominence of the metabolic latent variable. Future work incorporating more diverse and comprehensive plasma and urine measures may reveal latent brain–body dimensions that extend beyond the metabolic axis.

Materials and methods

All code used to perform the analyses is available on GitHub at https://github.com/netneurolab/Farahani_Age_Metabolism and has been archived and on Zenodo at https://zenodo.org/records/20412555 (https://doi.org/10.5281/zenodo.20412555).

Datasets and code

The code to obtain the results within this manuscript relies on open-source Python packages including NumPy (version 1.21.6) [248,249], SciPy (version 1.7.3) [250], pandas (version 1.3.5) [251], seaborn (version 0.12.2) [252], Matplotlib (version 3.5.3) [253], statsmodels (version 0.13.5) [254], bctpy (version 0.6.1) [255], Nilearn (version 0.10.1, see [256]), NiBabel (version 4.0.2) [257], netneurotools (version 0.2.3) [258], neuromaps (version 0.0.4) [245], and rpy2 (version 3.5.17) [259].

Parcellation atlases, including the Schaefer-400 functional atlas [65], and Johns Hopkins University (JHU) white matter tractography atlas [66,67], can be obtained from https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations, and https://web.mit.edu/fsl_v5.0.10/fsl/doc/wiki/Atlases.html, respectively. The GAMLSS models are implemented using GAMLSS R package (version ) available at https://github.com/gamlss-dev [90]. The PLS analysis is performed using the pyls toolbox (version 0.0.1) available at https://github.com/netneurolab/pypyls. All brain plots in the manuscript are visualized using Connectome Workbench (version 1.5.0), available at https://www.humanconnectome.org/software/get-connectome-workbench [260].

Supporting information

Acknowledgments

We thank all participants and staff of the HCP–A and UK Biobank datasets. We thank Justine Hansen, Vincent Bazinet, Filip Milisav, Yigu Zhou, Tahmineh Taheri, and Moohebat Pourmajidian for their comments and suggestions on the manuscript.

References

1. 1. Tian YE, Cropley V, Maier AB, Lautenschlager NT, Breakspear M, Zalesky A. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat Med. 2023;29(5):1221–31. pmid:37024597
   • View Article
   • PubMed/NCBI
   • Google Scholar
2. 2. Amirmoezzi Y, Cropley V, Mansour L S, Seguin C, Zalesky A, Tian YE. Characterizing brain-cardiovascular aging using multiorgan imaging and machine learning. J Neurosci. 2025;45(8):e1440242024. pmid:39971581
   • View Article
   • PubMed/NCBI
   • Google Scholar
3. 3. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. Mapping cortical change across the human life span. Nat Neurosci. 2003;6(3):309–15. pmid:12548289
   • View Article
   • PubMed/NCBI
   • Google Scholar
4. 4. Rutherford S, Fraza C, Dinga R, Kia SM, Wolfers T, Zabihi M, et al. Charting brain growth and aging at high spatial precision. Elife. 2022;11:e72904. pmid:35101172
   • View Article
   • PubMed/NCBI
   • Google Scholar
5. 5. Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, Agartz I, et al. Cortical thickness across the lifespan: data from 17,075 healthy individuals aged 3-90 years. Hum Brain Mapp. 2022;43(1):431–51. pmid:33595143
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. 6. Di Biase MA, Tian YE, Bethlehem RAI, Seidlitz J, Alexander-Bloch AF, Yeo BTT, et al. Mapping human brain charts cross-sectionally and longitudinally. Proc Natl Acad Sci U S A. 2023;120(20):e2216798120. pmid:37155868
   • View Article
   • PubMed/NCBI
   • Google Scholar
7. 7. Rehak Buckova B, Fraza C, Rehák R, Kolenič M, Beckmann CF, Španiel F, et al. Using normative models pre-trained on cross-sectional data to evaluate intra-individual longitudinal changes in neuroimaging data. Elife. 2025;13:RP95823. pmid:40072912
   • View Article
   • PubMed/NCBI
   • Google Scholar
8. 8. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14(1 Pt 1):21–36. pmid:11525331
   • View Article
   • PubMed/NCBI
   • Google Scholar
9. 9. Huizinga W, Poot DHJ, Vernooij MW, Roshchupkin GV, Bron EE, Ikram MA, et al. A spatio-temporal reference model of the aging brain. Neuroimage. 2018;169:11–22. pmid:29203452
   • View Article
   • PubMed/NCBI
   • Google Scholar
10. 10. Liang X, Sun L, Xia M, Zhao T, Gong G, Li Q, et al. Dissecting human cortical similarity networks across the lifespan. Neuron. 2025;113(19):3275-3295.e11. pmid:40706587
    • View Article
    • PubMed/NCBI
    • Google Scholar
11. 11. Bartzokis G, Lu PH, Heydari P, Couvrette A, Lee GJ, Kalashyan G, et al. Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals. Biol Psychiatry. 2012;72(12):1026–34. pmid:23017471
    • View Article
    • PubMed/NCBI
    • Google Scholar
12. 12. Grydeland H, Vértes PE, Váša F, Romero-Garcia R, Whitaker K, Alexander-Bloch AF, et al. Waves of maturation and senescence in micro-structural mri markers of human cortical myelination over the lifespan. Cereb Cortex. 2019;29(3):1369–81. pmid:30590439
    • View Article
    • PubMed/NCBI
    • Google Scholar
13. 13. Westlye LT, Walhovd KB, Dale AM, Bjørnerud A, Due-Tønnessen P, Engvig A, et al. Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cereb Cortex. 2010;20(9):2055–68. pmid:20032062
    • View Article
    • PubMed/NCBI
    • Google Scholar
14. 14. Zhao T, Cao M, Niu H, Zuo X-N, Evans A, He Y, et al. Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Hum Brain Mapp. 2015;36(10):3777–92. pmid:26173024
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. 15. Dorfschmidt L, White SR, Gardner M, Bedford S, Ball G, Edwards AD. Charting structural brain asymmetry across the human lifespan. bioRxiv. 2025;2025–07.
16. 16. Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci U S A. 2014;111(46):E4997-5006. pmid:25368199
    • View Article
    • PubMed/NCBI
    • Google Scholar
17. 17. Bethlehem RAI, Paquola C, Seidlitz J, Ronan L, Bernhardt B, Consortium C-C, et al. Dispersion of functional gradients across the adult lifespan. Neuroimage. 2020;222:117299. pmid:32828920
    • View Article
    • PubMed/NCBI
    • Google Scholar
18. 18. Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, et al. Brain charts for the human lifespan. Nature. 2022;604(7906):525–33. pmid:35388223
    • View Article
    • PubMed/NCBI
    • Google Scholar
19. 19. Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, et al. Human lifespan changes in the brain’s functional connectome. Nat Neurosci. 2025;28(4):891–901. pmid:40181189
    • View Article
    • PubMed/NCBI
    • Google Scholar
20. 20. Farahani A, Liu Z-Q, Ceballos EG, Hansen JY, Wennberg K, Zeighami Y, et al. Mapping cerebral blood perfusion and its links to multi-scale brain organization across the human lifespan. PLoS Biol. 2025;23(7):e3003277. pmid:40729400
    • View Article
    • PubMed/NCBI
    • Google Scholar
21. 21. Zeng X, Li Y, Hua L, Lu R, Franco LL, Kochunov P. Normative Cerebral Perfusion Across the Lifespan. arXiv. 2025.
    • View Article
    • Google Scholar
22. 22. Chan-Yeung M, Ferreira P, Frohlich J, Schulzer M, Tan F. The effects of age, smoking, and alcohol on routine laboratory tests. Am J Clin Pathol. 1981;75(3):320–6. pmid:7211753
    • View Article
    • PubMed/NCBI
    • Google Scholar
23. 23. Arbeev KG, Ukraintseva SV, Akushevich I, Kulminski AM, Arbeeva LS, Akushevich L, et al. Age trajectories of physiological indices in relation to healthy life course. Mech Ageing Dev. 2011;132(3):93–102. pmid:21262255
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24. Wills AK, Lawlor DA, Matthews FE, Sayer AA, Bakra E, Ben-Shlomo Y, et al. Life course trajectories of systolic blood pressure using longitudinal data from eight UK cohorts. PLoS Med. 2011;8(6):e1000440. pmid:21695075
    • View Article
    • PubMed/NCBI
    • Google Scholar
25. 25. Randolph Jr JF, Zheng H, Sowers MR, Crandall C, Crawford S, Gold EB, et al. Change in follicle-stimulating hormone and estradiol across the menopausal transition: effect of age at the final menstrual period. J Clin Endocrinol Metab. 2011;96(3):746–54. pmid:21159842
    • View Article
    • PubMed/NCBI
    • Google Scholar
26. 26. AlGhatrif M, Strait JB, Morrell CH, Canepa M, Wright J, Elango P, et al. Longitudinal trajectories of arterial stiffness and the role of blood pressure: the Baltimore Longitudinal Study of Aging. Hypertension. 2013;62(5):934–41. pmid:24001897
    • View Article
    • PubMed/NCBI
    • Google Scholar
27. 27. Murakata Y, Fujimaki T, Yamada Y. Age-related changes in clinical parameters and their associations with common complex diseases. Biomed Rep. 2015;3(6):767–77. pmid:26623014
    • View Article
    • PubMed/NCBI
    • Google Scholar
28. 28. Sebastiani P, Thyagarajan B, Sun F, Honig LS, Schupf N, Cosentino S, et al. Age and sex distributions of age-related biomarker values in healthy older adults from the long life family study. J Am Geriatr Soc. 2016;64(11):e189–94. pmid:27783390
    • View Article
    • PubMed/NCBI
    • Google Scholar
29. 29. Yang YC, Walsh CE, Johnson MP, Belsky DW, Reason M, Curran P, et al. Life-course trajectories of body mass index from adolescence to old age: Racial and educational disparities. Proc Natl Acad Sci U S A. 2021;118(17):e2020167118. pmid:33875595
    • View Article
    • PubMed/NCBI
    • Google Scholar
30. 30. Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature. 2018;561(7721):45–56. pmid:30185958
    • View Article
    • PubMed/NCBI
    • Google Scholar
31. 31. Johnson KA, Jones K, Holman BL, Becker JA, Spiers PA, Satlin A, et al. Preclinical prediction of Alzheimer’s disease using SPECT. Neurology. 1998;50(6):1563–71. pmid:9633695
    • View Article
    • PubMed/NCBI
    • Google Scholar
32. 32. Johnson KA, Lopera F, Jones K, Becker A, Sperling R, Hilson J, et al. Presenilin-1-associated abnormalities in regional cerebral perfusion. Neurology. 2001;56(11):1545–51. pmid:11402113
    • View Article
    • PubMed/NCBI
    • Google Scholar
33. 33. Wierenga CE, Hays CC, Zlatar ZZ. Cerebral blood flow measured by arterial spin labeling MRI as a preclinical marker of Alzheimer’s disease. J Alzheimers Dis. 2014;42 Suppl 4(Suppl 4):S411-9. pmid:25159672
    • View Article
    • PubMed/NCBI
    • Google Scholar
34. 34. Grade M, Hernandez Tamames JA, Pizzini FB, Achten E, Golay X, Smits M. A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice. Neuroradiology. 2015;57(12):1181–202. pmid:26351201
    • View Article
    • PubMed/NCBI
    • Google Scholar
35. 35. Iturria-Medina Y, Sotero RC, Toussaint PJ, Mateos-Pérez JM, Evans AC, Alzheimer’s Disease Neuroimaging Initiative. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat Commun. 2016;7:11934. pmid:27327500
    • View Article
    • PubMed/NCBI
    • Google Scholar
36. 36. Wolters FJ, Zonneveld HI, Hofman A, van der Lugt A, Koudstaal PJ, Vernooij MW, et al. Cerebral perfusion and the risk of dementia: a population-based study. Circulation. 2017;136(8):719–28. pmid:28588075
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37. Convit A, Wolf OT, Tarshish C, de Leon MJ. Reduced glucose tolerance is associated with poor memory performance and hippocampal atrophy among normal elderly. Proc Natl Acad Sci U S A. 2003;100(4):2019–22. pmid:12571363
    • View Article
    • PubMed/NCBI
    • Google Scholar
38. 38. Veit R, Kullmann S, Heni M, Machann J, Häring H-U, Fritsche A, et al. Reduced cortical thickness associated with visceral fat and BMI. Neuroimage Clin. 2014;6:307–11. pmid:25379443
    • View Article
    • PubMed/NCBI
    • Google Scholar
39. 39. Marqués-Iturria I, Pueyo R, Garolera M, Segura B, Junqué C, García-García I, et al. Frontal cortical thinning and subcortical volume reductions in early adulthood obesity. Psychiatry Res. 2013;214(2):109–15. pmid:24041490
    • View Article
    • PubMed/NCBI
    • Google Scholar
40. 40. Kim H, Kim C, Seo SW, Na DL, Kim HJ, Kang M, et al. Association between body mass index and cortical thickness: among elderly cognitively normal men and women. Int Psychogeriatr. 2015;27(1):121–30. pmid:25263181
    • View Article
    • PubMed/NCBI
    • Google Scholar
41. 41. Alosco ML, Gunstad J, Xu X, Clark US, Labbe DR, Riskin-Jones HH, et al. The impact of hypertension on cerebral perfusion and cortical thickness in older adults. J Am Soc Hypertens. 2014;8(8):561–70. pmid:25151318
    • View Article
    • PubMed/NCBI
    • Google Scholar
42. 42. Gonzalez CE, Pacheco J, Beason-Held LL, Resnick SM. Longitudinal changes in cortical thinning associated with hypertension. J Hypertens. 2015;33(6):1242–8. pmid:25693060
    • View Article
    • PubMed/NCBI
    • Google Scholar
43. 43. McIntosh AR, Lobaugh NJ. Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage. 2004;23 Suppl 1:S250-63. pmid:15501095
    • View Article
    • PubMed/NCBI
    • Google Scholar
44. 44. Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage. 2011;56(2):455–75. pmid:20656037
    • View Article
    • PubMed/NCBI
    • Google Scholar
45. 45. McIntosh AR, Mišić B. Multivariate statistical analyses for neuroimaging data. Annu Rev Psychol. 2013;64:499–525. pmid:22804773
    • View Article
    • PubMed/NCBI
    • Google Scholar
46. 46. Harms MP, Somerville LH, Ances BM, Andersson J, Barch DM, Bastiani M, et al. Extending the human connectome project across ages: imaging protocols for the lifespan development and aging projects. Neuroimage. 2018;183:972–84. pmid:30261308
    • View Article
    • PubMed/NCBI
    • Google Scholar
47. 47. Somerville LH, Bookheimer SY, Buckner RL, Burgess GC, Curtiss SW, Dapretto M, et al. The lifespan human connectome project in development: a large-scale study of brain connectivity development in 5-21 year olds. Neuroimage. 2018;183:456–68. pmid:30142446
    • View Article
    • PubMed/NCBI
    • Google Scholar
48. 48. Bookheimer SY, Salat DH, Terpstra M, Ances BM, Barch DM, Buckner RL, et al. The lifespan human connectome project in aging: an overview. Neuroimage. 2019;185:335–48. pmid:30332613
    • View Article
    • PubMed/NCBI
    • Google Scholar
49. 49. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19(11):1523–36. pmid:27643430
    • View Article
    • PubMed/NCBI
    • Google Scholar
50. 50. Gur RC, Gur RE, Obrist WD, Hungerbuhler JP, Younkin D, Rosen AD, et al. Sex and handedness differences in cerebral blood flow during rest and cognitive activity. Science. 1982;217(4560):659–61. pmid:7089587
    • View Article
    • PubMed/NCBI
    • Google Scholar
51. 51. Shaw TG, Mortel KF, Meyer JS, Rogers RL, Hardenberg J, Cutaia MM. Cerebral blood flow changes in benign aging and cerebrovascular disease. Neurology. 1984;34(7):855–62. pmid:6539861
    • View Article
    • PubMed/NCBI
    • Google Scholar
52. 52. Rodriguez G, Warkentin S, Risberg J, Rosadini G. Sex differences in regional cerebral blood flow. J Cereb Blood Flow Metab. 1988;8(6):783–9. pmid:3192645
    • View Article
    • PubMed/NCBI
    • Google Scholar
53. 53. Daniel DG, Mathew RJ, Wilson WH. Sex roles and regional cerebral blood flow. Psychiatry Res. 1989;27(1):55–64. pmid:2922445
    • View Article
    • PubMed/NCBI
    • Google Scholar
54. 54. Gur RE, Gur RC. Gender differences in regional cerebral blood flow. Schizophr Bull. 1990;16(2):247–54. pmid:2374883
    • View Article
    • PubMed/NCBI
    • Google Scholar
55. 55. Esposito G, Van Horn JD, Weinberger DR, Berman KF. Gender differences in cerebral blood flow as a function of cognitive state with PET. J Nucl Med. 1996;37(4):559–64. pmid:8691239
    • View Article
    • PubMed/NCBI
    • Google Scholar
56. 56. Aanerud J, Borghammer P, Rodell A, Jónsdottir KY, Gjedde A. Sex differences of human cortical blood flow and energy metabolism. J Cereb Blood Flow Metab. 2017;37(7):2433–40. pmid:27629099
    • View Article
    • PubMed/NCBI
    • Google Scholar
57. 57. Alisch JSR, Khattar N, Kim RW, Cortina LE, Rejimon AC, Qian W, et al. Sex and age-related differences in cerebral blood flow investigated using pseudo-continuous arterial spin labeling magnetic resonance imaging. Aging (Albany NY). 2021;13(4):4911–25. pmid:33596183
    • View Article
    • PubMed/NCBI
    • Google Scholar
58. 58. Juttukonda MR, Li B, Almaktoum R, Stephens KA, Yochim KM, Yacoub E, et al. Characterizing cerebral hemodynamics across the adult lifespan with arterial spin labeling MRI data from the Human Connectome Project-Aging. Neuroimage. 2021;230:117807. pmid:33524575
    • View Article
    • PubMed/NCBI
    • Google Scholar
59. 59. James GD, Sealey JE, Alderman M, Ljungman S, Mueller FB, Pecker MS, et al. A longitudinal study of urinary creatinine and creatinine clearance in normal subjects. Race, sex, and age differences. Am J Hypertens. 1988;1(2):124–31. pmid:3401350
    • View Article
    • PubMed/NCBI
    • Google Scholar
60. 60. Bale TL, Epperson CN. Sex as a biological variable: who, what, when, why, and how. Neuropsychopharmacology. 2017;42(2):386–96. pmid:27658485
    • View Article
    • PubMed/NCBI
    • Google Scholar
61. 61. Lau ES, Paniagua SM, Guseh JS, Bhambhani V, Zanni MV, Courchesne P, et al. Sex differences in circulating biomarkers of cardiovascular disease. J Am Coll Cardiol. 2019;74(12):1543–53. pmid:31537263
    • View Article
    • PubMed/NCBI
    • Google Scholar
62. 62. Costanzo M, Caterino M, Sotgiu G, Ruoppolo M, Franconi F, Campesi I. Sex differences in the human metabolome. Biol Sex Differ. 2022;13(1):30. pmid:35706042
    • View Article
    • PubMed/NCBI
    • Google Scholar
63. 63. Holven KB, Roeters van Lennep J. Sex differences in lipids: a life course approach. Atherosclerosis. 2023;384:117270. pmid:37730457
    • View Article
    • PubMed/NCBI
    • Google Scholar
64. 64. Okell TW, Xu X, Craig M, Alfaro-Almagro F, Thomas DL, De Vita E. Brain perfusion imaging of a large population: Arterial spin labelling MRI in UK Biobank. medRxiv. 2025;2025–07.
65. 65. Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X-N, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex. 2018;28(9):3095–114. pmid:28981612
    • View Article
    • PubMed/NCBI
    • Google Scholar
66. 66. Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 2007;36(3):630–44. pmid:17481925
    • View Article
    • PubMed/NCBI
    • Google Scholar
67. 67. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 2008;39(1):336–47. pmid:17931890
    • View Article
    • PubMed/NCBI
    • Google Scholar
68. 68. Musch W, Verfaillie L, Decaux G. Age-related increase in plasma urea level and decrease in fractional urea excretion: clinical application in the syndrome of inappropriate secretion of antidiuretic hormone. Clin J Am Soc Nephrol. 2006;1(5):909–14. pmid:17699306
    • View Article
    • PubMed/NCBI
    • Google Scholar
69. 69. Mackay EM, Mackay LL. The concentration of urea in the blood of normal individuals. J Clin Invest. 1927;4(2):295–306. pmid:16693759
    • View Article
    • PubMed/NCBI
    • Google Scholar
70. 70. Verhave JC, Fesler P, Ribstein J, du Cailar G, Mimran A. Estimation of renal function in subjects with normal serum creatinine levels: influence of age and body mass index. Am J Kidney Dis. 2005;46(2):233–41. pmid:16112041
    • View Article
    • PubMed/NCBI
    • Google Scholar
71. 71. Araujo AB, Wittert GA. Endocrinology of the aging male. Best Pract Res Clin Endocrinol Metab. 2011;25(2):303–19. pmid:21397200
    • View Article
    • PubMed/NCBI
    • Google Scholar
72. 72. Lenton EA, Sexton L, Lee S, Cooke ID. Progressive changes in LH and FSH and LH: FSH ratio in women throughout reproductive life. Maturitas. 1988;10(1):35–43. pmid:3135465
    • View Article
    • PubMed/NCBI
    • Google Scholar
73. 73. Dubowitz N, Xue W, Long Q, Ownby JG, Olson DE, Barb D, et al. Aging is associated with increased HbA1c levels, independently of glucose levels and insulin resistance, and also with decreased HbA1c diagnostic specificity. Diabet Med. 2014;31(8):927–35. pmid:24698119
    • View Article
    • PubMed/NCBI
    • Google Scholar
74. 74. Wu L, Lin H, Gao J, Li X, Xia M, Wang D, et al. Effect of age on the diagnostic efficiency of HbA1c for diabetes in a Chinese middle-aged and elderly population: The Shanghai Changfeng Study. PLoS One. 2017;12(9):e0184607. pmid:28886160
    • View Article
    • PubMed/NCBI
    • Google Scholar
75. 75. Franklin SS, Gustin W 4th, Wong ND, Larson MG, Weber MA, Kannel WB, et al. Hemodynamic patterns of age-related changes in blood pressure. The Framingham Heart Study. Circulation. 1997;96(1):308–15. pmid:9236450
    • View Article
    • PubMed/NCBI
    • Google Scholar
76. 76. Tweedale M, Morys F, Pastor-Bernier A, Azizi H, Tremblay C, Dagher A. Obesity and diffusion-weighted imaging of subcortical grey matter in young and older adults. Appetite. 2024;200:107527. pmid:38797235
    • View Article
    • PubMed/NCBI
    • Google Scholar
77. 77. Thethi T, Kamiyama M, Kobori H. The link between the renin-angiotensin-aldosterone system and renal injury in obesity and the metabolic syndrome. Curr Hypertens Rep. 2012;14(2):160–9. pmid:22302531
    • View Article
    • PubMed/NCBI
    • Google Scholar
78. 78. Sowers JR. Diabetes mellitus and vascular disease. Hypertension. 2013;61(5):943–7. pmid:23595139
    • View Article
    • PubMed/NCBI
    • Google Scholar
79. 79. Parvanova A, Reseghetti E, Abbate M, Ruggenenti P. Mechanisms and treatment of obesity-related hypertension-Part 1: Mechanisms. Clin Kidney J. 2023;17(1):sfad282. pmid:38186879
    • View Article
    • PubMed/NCBI
    • Google Scholar
80. 80. Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest. 2000;106(4):473–81. pmid:10953022
    • View Article
    • PubMed/NCBI
    • Google Scholar
81. 81. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9. pmid:3899825
    • View Article
    • PubMed/NCBI
    • Google Scholar
82. 82. Mota M, Banini BA, Cazanave SC, Sanyal AJ. Molecular mechanisms of lipotoxicity and glucotoxicity in nonalcoholic fatty liver disease. Metabolism. 2016;65(8):1049–61. pmid:26997538
    • View Article
    • PubMed/NCBI
    • Google Scholar
83. 83. Marchesini G, Moscatiello S, Di Domizio S, Forlani G. Obesity-associated liver disease. J Clin Endocrinol Metab. 2008;93(11 Suppl 1):S74-80. pmid:18987273
    • View Article
    • PubMed/NCBI
    • Google Scholar
84. 84. Prentki M, Nolan CJ. Islet beta cell failure in type 2 diabetes. J Clin Invest. 2006;116(7):1802–12. pmid:16823478
    • View Article
    • PubMed/NCBI
    • Google Scholar
85. 85. Steinberg HO, Tarshoby M, Monestel R, Hook G, Cronin J, Johnson A, et al. Elevated circulating free fatty acid levels impair endothelium-dependent vasodilation. J Clin Invest. 1997;100(5):1230–9. pmid:9276741
    • View Article
    • PubMed/NCBI
    • Google Scholar
86. 86. Muniyappa R, Iantorno M, Quon MJ. An integrated view of insulin resistance and endothelial dysfunction. Endocrinol Metab Clin North Am. 2008;37(3):685–711, ix–x. pmid:18775359
    • View Article
    • PubMed/NCBI
    • Google Scholar
87. 87. Shariq OA, McKenzie TJ. Obesity-related hypertension: a review of pathophysiology, management, and the role of metabolic surgery. Gland Surg. 2020;9(1):80–93. pmid:32206601
    • View Article
    • PubMed/NCBI
    • Google Scholar
88. 88. Karhapää P, Malkki M, Laakso M. Isolated low HDL cholesterol. An insulin-resistant state. Diabetes. 1994;43(3):411–7. pmid:8314013
    • View Article
    • PubMed/NCBI
    • Google Scholar
89. 89. Rashid S, Genest J. Effect of obesity on high-density lipoprotein metabolism. Obesity (Silver Spring). 2007;15(12):2875–88. pmid:18198293
    • View Article
    • PubMed/NCBI
    • Google Scholar
90. 90. Stasinopoulos DM, Rigby RA. Generalized Additive Models for Location Scale and Shape (GAMLSS) inR. J Stat Soft. 2007;23(7).
    • View Article
    • Google Scholar
91. 91. Laudisio A, Marzetti E, Pagano F, Cocchi A, Franceschi C, Bernabei R, et al. Association of metabolic syndrome with cognitive function: the role of sex and age. Clin Nutr. 2008;27(5):747–54. pmid:18715681
    • View Article
    • PubMed/NCBI
    • Google Scholar
92. 92. Schuur M, Henneman P, van Swieten JC, Zillikens MC, de Koning I, Janssens ACJW, et al. Insulin-resistance and metabolic syndrome are related to executive function in women in a large family-based study. Eur J Epidemiol. 2010;25(8):561–8. pmid:20585974
    • View Article
    • PubMed/NCBI
    • Google Scholar
93. 93. McEvoy LK, Laughlin GA, Barrett-Connor E, Bergstrom J, Kritz-Silverstein D, Der-Martirosian C, et al. Metabolic syndrome and 16-year cognitive decline in community-dwelling older adults. Ann Epidemiol. 2012;22(5):310–7. pmid:22285865
    • View Article
    • PubMed/NCBI
    • Google Scholar
94. 94. Crane BM, Nichols E, Carlson MC, Deal JA, Gross AL. Body mass index and cognition: associations across mid- to late life and gender differences. J Gerontol A Biol Sci Med Sci. 2023;78(6):988–96. pmid:36638277
    • View Article
    • PubMed/NCBI
    • Google Scholar
95. 95. Chanti-Ketterl M, Stebbins RC, Obhi HK, Belsky DW, Plassman BL, Yang YC. Sex differences in the association between metabolic dysregulation and cognitive aging: the health and retirement study. J Gerontol A Biol Sci Med Sci. 2022;77(9):1827–35. pmid:34606593
    • View Article
    • PubMed/NCBI
    • Google Scholar
96. 96. Chang Y-M, Lee C-L, Wang J-S. Sex disparity in the association of metabolic syndrome with cognitive impairment. J Clin Med. 2024;13(9):2571. pmid:38731099
    • View Article
    • PubMed/NCBI
    • Google Scholar
97. 97. Elliott P, Peakman TC, UK Biobank. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol. 2008;37(2):234–44. pmid:18381398
    • View Article
    • PubMed/NCBI
    • Google Scholar
98. 98. Cox SR, Ritchie SJ, Tucker-Drob EM, Liewald DC, Hagenaars SP, Davies G, et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 2016;7:13629. pmid:27976682
    • View Article
    • PubMed/NCBI
    • Google Scholar
99. 99. Wallach JB. Interpretation of diagnostic tests. Lippincott Williams & Wilkins; 2007.
100. 100. Helmer M, Warrington S, Mohammadi-Nejad A-R, Ji JL, Howell A, Rosand B, et al. On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations. Commun Biol. 2024;7(1):217. pmid:38383808
     • View Article
     • PubMed/NCBI
     • Google Scholar
101. 101. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. pmid:23746838
     • View Article
     • PubMed/NCBI
     • Google Scholar
102. 102. Thambisetty M, Wan J, Carass A, An Y, Prince JL, Resnick SM. Longitudinal changes in cortical thickness associated with normal aging. Neuroimage. 2010;52(4):1215–23. pmid:20441796
     • View Article
     • PubMed/NCBI
     • Google Scholar
103. 103. McGinnis SM, Brickhouse M, Pascual B, Dickerson BC. Age-related changes in the thickness of cortical zones in humans. Brain Topogr. 2011;24(3–4):279–91. pmid:21842406
     • View Article
     • PubMed/NCBI
     • Google Scholar
104. 104. Hogstrom LJ, Westlye LT, Walhovd KB, Fjell AM. The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cereb Cortex. 2013;23(11):2521–30. pmid:22892423
     • View Article
     • PubMed/NCBI
     • Google Scholar
105. 105. Fjell AM, Grydeland H, Krogsrud SK, Amlien I, Rohani DA, Ferschmann L, et al. Development and aging of cortical thickness correspond to genetic organization patterns. Proc Natl Acad Sci U S A. 2015;112(50):15462–7. pmid:26575625
     • View Article
     • PubMed/NCBI
     • Google Scholar
106. 106. Smith CD, Chebrolu H, Wekstein DR, Schmitt FA, Markesbery WR. Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly. Neurobiol Aging. 2007;28(7):1075–87. pmid:16774798
     • View Article
     • PubMed/NCBI
     • Google Scholar
107. 107. Sullivan EV, Pfefferbaum A. Diffusion tensor imaging and aging. Neurosci Biobehav Rev. 2006;30(6):749–61. pmid:16887187
     • View Article
     • PubMed/NCBI
     • Google Scholar
108. 108. Grieve SM, Williams LM, Paul RH, Clark CR, Gordon E. Cognitive aging, executive function, and fractional anisotropy: a diffusion tensor MR imaging study. AJNR Am J Neuroradiol. 2007;28(2):226–35. pmid:17296985
     • View Article
     • PubMed/NCBI
     • Google Scholar
109. 109. Hsu J-L, Leemans A, Bai C-H, Lee C-H, Tsai Y-F, Chiu H-C, et al. Gender differences and age-related white matter changes of the human brain: a diffusion tensor imaging study. Neuroimage. 2008;39(2):566–77. pmid:17951075
     • View Article
     • PubMed/NCBI
     • Google Scholar
110. 110. Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH. Age-related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. Human Brain Mapping. 2010;31(3):378–90.
     • View Article
     • Google Scholar
111. 111. Kochunov P, Williamson DE, Lancaster J, Fox P, Cornell J, Blangero J, et al. Fractional anisotropy of water diffusion in cerebral white matter across the lifespan. Neurobiol Aging. 2012;33(1):9–20. pmid:20122755
     • View Article
     • PubMed/NCBI
     • Google Scholar
112. 112. Jeon T, Mishra V, Uh J, Weiner M, Hatanpaa KJ, White 3rd CL, et al. Regional changes of cortical mean diffusivities with aging after correction of partial volume effects. Neuroimage. 2012;62(3):1705–16. pmid:22683383
     • View Article
     • PubMed/NCBI
     • Google Scholar
113. 113. Burzynska AZ, Preuschhof C, Bäckman L, Nyberg L, Li S-C, Lindenberger U, et al. Age-related differences in white matter microstructure: region-specific patterns of diffusivity. Neuroimage. 2010;49(3):2104–12. pmid:19782758
     • View Article
     • PubMed/NCBI
     • Google Scholar
114. 114. Damoiseaux JS. Effects of aging on functional and structural brain connectivity. Neuroimage. 2017;160:32–40. pmid:28159687
     • View Article
     • PubMed/NCBI
     • Google Scholar
115. 115. Madole JW, Ritchie SJ, Cox SR, Buchanan CR, Hernández MV, Maniega SM, et al. Aging-sensitive networks within the human structural connectome are implicated in late-life cognitive declines. Biol Psychiatry. 2021;89(8):795–806. pmid:32828527
     • View Article
     • PubMed/NCBI
     • Google Scholar
116. 116. Tomasi D, Volkow ND. Aging and functional brain networks. Mol Psychiatry. 2012;17(5):471, 549–58. pmid:21727896
     • View Article
     • PubMed/NCBI
     • Google Scholar
117. 117. Cao M, Wang J-H, Dai Z-J, Cao X-Y, Jiang L-L, Fan F-M, et al. Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci. 2014;7:76–93. pmid:24333927
     • View Article
     • PubMed/NCBI
     • Google Scholar
118. 118. Spreng RN, Stevens WD, Viviano JD, Schacter DL. Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest. Neurobiol Aging. 2016;45:149–60. pmid:27459935
     • View Article
     • PubMed/NCBI
     • Google Scholar
119. 119. Setton R, Mwilambwe-Tshilobo L, Girn M, Lockrow AW, Baracchini G, Hughes C, et al. Age differences in the functional architecture of the human brain. Cereb Cortex. 2022;33(1):114–34. pmid:35231927
     • View Article
     • PubMed/NCBI
     • Google Scholar
120. 120. Liem F, Varoquaux G, Kynast J, Beyer F, Kharabian Masouleh S, Huntenburg JM, et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage. 2017;148:179–88. pmid:27890805
     • View Article
     • PubMed/NCBI
     • Google Scholar
121. 121. Cole JH. Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging. 2020;92:34–42. pmid:32380363
     • View Article
     • PubMed/NCBI
     • Google Scholar
122. 122. Niu X, Zhang F, Kounios J, Liang H. Improved prediction of brain age using multimodal neuroimaging data. Hum Brain Mapp. 2020;41(6):1626–43. pmid:31837193
     • View Article
     • PubMed/NCBI
     • Google Scholar
123. 123. Rokicki J, Wolfers T, Nordhøy W, Tesli N, Quintana DS, Alnaes D, et al. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp. 2021;42(6):1714–26. pmid:33340180
     • View Article
     • PubMed/NCBI
     • Google Scholar
124. 124. Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, Barch DM, et al. A systematic review of multimodal brain age studies: uncovering a divergence between model accuracy and utility. Patterns (N Y). 2023;4(4):100712. pmid:37123443
     • View Article
     • PubMed/NCBI
     • Google Scholar
125. 125. Park B-Y, Park H, Morys F, Kim M, Byeon K, Lee H, et al. Inter-individual body mass variations relate to fractionated functional brain hierarchies. Commun Biol. 2021;4(1):735. pmid:34127795
     • View Article
     • PubMed/NCBI
     • Google Scholar
126. 126. Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, et al. Brain structure and obesity. Hum Brain Mapp. 2010;31(3):353–64. pmid:19662657
     • View Article
     • PubMed/NCBI
     • Google Scholar
127. 127. Kurth F, Levitt JG, Phillips OR, Luders E, Woods RP, Mazziotta JC, et al. Relationships between gray matter, body mass index, and waist circumference in healthy adults. Hum Brain Mapp. 2013;34(7):1737–46. pmid:22419507
     • View Article
     • PubMed/NCBI
     • Google Scholar
128. 128. Pannacciulli N, Del Parigi A, Chen K, Le DSNT, Reiman EM, Tataranni PA. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage. 2006;31(4):1419–25. pmid:16545583
     • View Article
     • PubMed/NCBI
     • Google Scholar
129. 129. Karlsson HK, Tuulari JJ, Hirvonen J, Lepomäki V, Parkkola R, Hiltunen J, et al. Obesity is associated with white matter atrophy: a combined diffusion tensor imaging and voxel-based morphometric study. Obesity (Silver Spring). 2013;21(12):2530–7. pmid:23512884
     • View Article
     • PubMed/NCBI
     • Google Scholar
130. 130. Brooks SJ, Benedict C, Burgos J, Kempton MJ, Kullberg J, Nordenskjöld R, et al. Late-life obesity is associated with smaller global and regional gray matter volumes: a voxel-based morphometric study. Int J Obes (Lond). 2013;37(2):230–6. pmid:22290540
     • View Article
     • PubMed/NCBI
     • Google Scholar
131. 131. Dekkers IA, Jansen PR, Lamb HJ. Obesity, brain volume, and white matter microstructure at MRI: a cross-sectional UK Biobank Study. Radiology. 2019;291(3):763–71. pmid:31012815
     • View Article
     • PubMed/NCBI
     • Google Scholar
132. 132. Cox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR, et al. Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J. 2019;40(28):2290–300. pmid:30854560
     • View Article
     • PubMed/NCBI
     • Google Scholar
133. 133. Franz CE, Xian H, Lew D, Hatton SN, Puckett O, Whitsel N, et al. Body mass trajectories and cortical thickness in middle-aged men: a 42-year longitudinal study starting in young adulthood. Neurobiol Aging. 2019;79:11–21. pmid:31026618
     • View Article
     • PubMed/NCBI
     • Google Scholar
134. 134. Willette AA, Kapogiannis D. Does the brain shrink as the waist expands?. Ageing Res Rev. 2015;20:86–97. pmid:24768742
     • View Article
     • PubMed/NCBI
     • Google Scholar
135. 135. Vainik U, Baker TE, Dadar M, Zeighami Y, Michaud A, Zhang Y, et al. Neurobehavioral correlates of obesity are largely heritable. Proc Natl Acad Sci U S A. 2018;115(37):9312–7. pmid:30154161
     • View Article
     • PubMed/NCBI
     • Google Scholar
136. 136. García-García I, Michaud A, Dadar M, Zeighami Y, Neseliler S, Collins DL, et al. Neuroanatomical differences in obesity: meta-analytic findings and their validation in an independent dataset. Int J Obes (Lond). 2019;43(5):943–51. pmid:30022057
     • View Article
     • PubMed/NCBI
     • Google Scholar
137. 137. Shin J, Pelletier S, Richer L, Pike GB, Gaudet D, Paus T, et al. Adiposity-related insulin resistance and thickness of the cerebral cortex in middle-aged adults. J Neuroendocrinol. 2020;32(12):e12921. pmid:33340164
     • View Article
     • PubMed/NCBI
     • Google Scholar
138. 138. Shin J, Patel Y, Parker N, Paus T, Pausova Z. Prediabetic HbA1c and cortical atrophy: underlying neurobiology. Diabetes Care. 2023;46(12):2267–72. pmid:37824790
     • View Article
     • PubMed/NCBI
     • Google Scholar
139. 139. Brown MS, Goldstein JL. How LDL receptors influence cholesterol and atherosclerosis. Sci Am. 1984;251(5):58–66. pmid:6390676
     • View Article
     • PubMed/NCBI
     • Google Scholar
140. 140. Goldstein JL, Brown MS. A century of cholesterol and coronaries: from plaques to genes to statins. Cell. 2015;161(1):161–72. pmid:25815993
     • View Article
     • PubMed/NCBI
     • Google Scholar
141. 141. Fernández-Friera L, Fuster V, López-Melgar B, Oliva B, García-Ruiz JM, Mendiguren J, et al. Normal LDL-cholesterol levels are associated with subclinical atherosclerosis in the absence of risk factors. Journal of the American College of Cardiology. 2017;70(24):2979–91.
     • View Article
     • Google Scholar
142. 142. Bornfeldt KE. Uncomplicating the macrovascular complications of diabetes: the 2014 Edwin Bierman Award Lecture. Diabetes. 2015;64(8):2689–97. pmid:26207031
     • View Article
     • PubMed/NCBI
     • Google Scholar
143. 143. Newman 3rd WP, Freedman DS, Voors AW, Gard PD, Srinivasan SR, Cresanta JL, et al. Relation of serum lipoprotein levels and systolic blood pressure to early atherosclerosis. The Bogalusa Heart Study. N Engl J Med. 1986;314(3):138–44. pmid:3455748
     • View Article
     • PubMed/NCBI
     • Google Scholar
144. 144. Schiffrin EL, Canadian Institutes of Health Research Multidisciplinary Research Group on Hypertension. Beyond blood pressure: the endothelium and atherosclerosis progression. Am J Hypertens. 2002;15(10 Pt 2):115S-122S. pmid:12383592
     • View Article
     • PubMed/NCBI
     • Google Scholar
145. 145. Sipahi I, Tuzcu EM, Schoenhagen P, Wolski KE, Nicholls SJ, Balog C, et al. Effects of normal, pre-hypertensive, and hypertensive blood pressure levels on progression of coronary atherosclerosis. J Am Coll Cardiol. 2006;48(4):833–8. pmid:16904557
     • View Article
     • PubMed/NCBI
     • Google Scholar
146. 146. McGill Jr HC, McMahan CA, Herderick EE, Zieske AW, Malcom GT, Tracy RE, et al. Obesity accelerates the progression of coronary atherosclerosis in young men. Circulation. 2002;105(23):2712–8. pmid:12057983
     • View Article
     • PubMed/NCBI
     • Google Scholar
147. 147. Assmann G, Gotto AM. HDL cholesterol and protective factors in atherosclerosis. Circulation. 2004;109(23_suppl_1):III–8.
     • View Article
     • Google Scholar
148. 148. Vitali C, Wellington CL, Calabresi L. HDL and cholesterol handling in the brain. Cardiovasc Res. 2014;103(3):405–13. pmid:24907980
     • View Article
     • PubMed/NCBI
     • Google Scholar
149. 149. Barter PJ, Nicholls S, Rye K-A, Anantharamaiah GM, Navab M, Fogelman AM. Antiinflammatory properties of HDL. Circ Res. 2004;95(8):764–72. pmid:15486323
     • View Article
     • PubMed/NCBI
     • Google Scholar
150. 150. Mineo C, Deguchi H, Griffin JH, Shaul PW. Endothelial and antithrombotic actions of HDL. Circ Res. 2006;98(11):1352–64. pmid:16763172
     • View Article
     • PubMed/NCBI
     • Google Scholar
151. 151. Tran-Dinh A, Diallo D, Delbosc S, Varela-Perez LM, Dang QB, Lapergue B, et al. HDL and endothelial protection. Br J Pharmacol. 2013;169(3):493–511. pmid:23488589
     • View Article
     • PubMed/NCBI
     • Google Scholar
152. 152. Reed B, Villeneuve S, Mack W, DeCarli C, Chui HC, Jagust W. Associations between serum cholesterol levels and cerebral amyloidosis. JAMA Neurol. 2014;71(2):195–200. pmid:24378418
     • View Article
     • PubMed/NCBI
     • Google Scholar
153. 153. Button EB, Robert J, Caffrey TM, Fan J, Zhao W, Wellington CL. HDL from an Alzheimer’s disease perspective. Curr Opin Lipidol. 2019;30(3):224–34. pmid:30946049
     • View Article
     • PubMed/NCBI
     • Google Scholar
154. 154. Bates KA, Sohrabi HR, Rainey-Smith SR, Weinborn M, Bucks RS, Rodrigues M, et al. Serum high-density lipoprotein is associated with better cognitive function in a cross-sectional study of aging women. Int J Neurosci. 2017;127(3):243–52. pmid:27113638
     • View Article
     • PubMed/NCBI
     • Google Scholar
155. 155. Merched A, Xia Y, Visvikis S, Serot JM, Siest G. Decreased high-density lipoprotein cholesterol and serum apolipoprotein AI concentrations are highly correlated with the severity of Alzheimer’s disease. Neurobiol Aging. 2000;21(1):27–30. pmid:10794845
     • View Article
     • PubMed/NCBI
     • Google Scholar
156. 156. Zuliani G, Cavalieri M, Galvani M, Volpato S, Cherubini A, Bandinelli S, et al. Relationship between low levels of high-density lipoprotein cholesterol and dementia in the elderly. The InChianti study. J Gerontol A Biol Sci Med Sci. 2010;65(5):559–64. pmid:20299544
     • View Article
     • PubMed/NCBI
     • Google Scholar
157. 157. Reitz C, Tang M-X, Schupf N, Manly JJ, Mayeux R, Luchsinger JA. Association of higher levels of high-density lipoprotein cholesterol in elderly individuals and lower risk of late-onset Alzheimer disease. Arch Neurol. 2010;67(12):1491–7. pmid:21149810
     • View Article
     • PubMed/NCBI
     • Google Scholar
158. 158. Liu S, Suzuki H, Ito H, Korenaga T, Akatsu H, Meno K, et al. Serum levels of proteins involved in amyloid-β clearance are related to cognitive decline and neuroimaging changes in mild cognitive impairment. Alzheimers Dement (Amst). 2019;11:85–97. pmid:30671532
     • View Article
     • PubMed/NCBI
     • Google Scholar
159. 159. Medic N, Kochunov P, Ziauddeen H, Ersche KD, Nathan PJ, Ronan L, et al. BMI-related cortical morphometry changes are associated with altered white matter structure. Int J Obes (Lond). 2019;43(3):523–32. pmid:30568264
     • View Article
     • PubMed/NCBI
     • Google Scholar
160. 160. Haltia LT, Viljanen A, Parkkola R, Kemppainen N, Rinne JO, Nuutila P, et al. Brain white matter expansion in human obesity and the recovering effect of dieting. J Clin Endocrinol Metab. 2007;92(8):3278–84. pmid:17536002
     • View Article
     • PubMed/NCBI
     • Google Scholar
161. 161. Herrmann MJ, Tesar A-K, Beier J, Berg M, Warrings B. Grey matter alterations in obesity: a meta-analysis of whole-brain studies. Obes Rev. 2019;20(3):464–71. pmid:30537231
     • View Article
     • PubMed/NCBI
     • Google Scholar
162. 162. Morys F, Dadar M, Dagher A. Association between midlife obesity and its metabolic consequences, cerebrovascular disease, and cognitive decline. J Clin Endocrinol Metab. 2021;106(10):e4260–74. pmid:33677592
     • View Article
     • PubMed/NCBI
     • Google Scholar
163. 163. Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, et al. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. Elife. 2024;12:RP93246. pmid:38512127
     • View Article
     • PubMed/NCBI
     • Google Scholar
164. 164. Reaven GM. Role of Insulin Resistance in Human Disease. Diabetes. 1988;37(12):1595–607.
     • View Article
     • Google Scholar
165. 165. Kahn R, Buse J, Ferrannini E, Stern M, American Diabetes Association, European Association for the Study of Diabetes. The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2005;28(9):2289–304. pmid:16123508
     • View Article
     • PubMed/NCBI
     • Google Scholar
166. 166. WHO. Definition, diagnosis and classification of diabetes mellitus and its complications. 1999. http://whqlibdoc.who.int/hq/1999/WHO_NCD_NCS_99_2.pdf
167. 167. Grundy SM, Brewer Jr HB, Cleeman JI, Smith Jr SC, Lenfant C. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation. 2004;109(3):433–8.
     • View Article
     • Google Scholar
168. 168. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018;20(2):12. pmid:29480368
     • View Article
     • PubMed/NCBI
     • Google Scholar
169. 169. Assis SG, Tavares PH, Oliveira N, Serpeloni F, Avanci JQ. Epigenetics, resilience, protective factors and factors promoting positive outcomes: a scoping review. Int J Dev Neurosci. 2025;85(6):e70042. pmid:41114472
     • View Article
     • PubMed/NCBI
     • Google Scholar
170. 170. Udeh-Momoh CT, Migeot J, Blackmon K, Mielke MM, Melloni M, Cox L, et al. Resilience and brain health in global populations. Nat Med. 2025;31(8):2518–31. pmid:40731089
     • View Article
     • PubMed/NCBI
     • Google Scholar
171. 171. Pasha EP, Birdsill AC, Oleson S, Haley AP, Tanaka H. Impacts of metabolic syndrome scores on cerebrovascular conductance are mediated by arterial stiffening. Am J Hypertens. 2017;31(1):72–9. pmid:28992237
     • View Article
     • PubMed/NCBI
     • Google Scholar
172. 172. Lacolley P, Challande P, Osborne-Pellegrin M, Regnault V. Genetics and pathophysiology of arterial stiffness. Cardiovasc Res. 2009;81(4):637–48. pmid:19098299
     • View Article
     • PubMed/NCBI
     • Google Scholar
173. 173. Cecelja M, Chowienczyk P. Role of arterial stiffness in cardiovascular disease. JRSM Cardiovasc Dis. 2012;1(4):cvd.2012.012016. pmid:24175067
     • View Article
     • PubMed/NCBI
     • Google Scholar
174. 174. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206. pmid:25673413
     • View Article
     • PubMed/NCBI
     • Google Scholar
175. 175. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27(20):3641–9. pmid:30124842
     • View Article
     • PubMed/NCBI
     • Google Scholar
176. 176. Timshel PN, Thompson JJ, Pers TH. Genetic mapping of etiologic brain cell types for obesity. Elife. 2020;9:e55851. pmid:32955435
     • View Article
     • PubMed/NCBI
     • Google Scholar
177. 177. Morys F, Tremblay C, Rahayel S, Hansen JY, Dai A, Misic B, et al. Neural correlates of obesity across the lifespan. Commun Biol. 2024;7(1):656. pmid:38806652
     • View Article
     • PubMed/NCBI
     • Google Scholar
178. 178. Morys F, Yu E, Shishikura M, Paquola C, Vainik U, Nave G, et al. Neuroanatomical correlates of genetic risk for obesity in children. Transl Psychiatry. 2023;13(1):1. pmid:36596778
     • View Article
     • PubMed/NCBI
     • Google Scholar
179. 179. van de Sande-Lee S, Pereira FRS, Cintra DE, Fernandes PT, Cardoso AR, Garlipp CR, et al. Partial reversibility of hypothalamic dysfunction and changes in brain activity after body mass reduction in obese subjects. Diabetes. 2011;60(6):1699–704. pmid:21515852
     • View Article
     • PubMed/NCBI
     • Google Scholar
180. 180. Tuulari JJ, Karlsson HK, Antikainen O, Hirvonen J, Pham T, Salminen P, et al. Bariatric surgery induces white and grey matter density recovery in the morbidly obese: a voxel-based morphometric study. Hum Brain Mapp. 2016;37(11):3745–56. pmid:27400738
     • View Article
     • PubMed/NCBI
     • Google Scholar
181. 181. Zhang Y, Ji G, Xu M, Cai W, Zhu Q, Qian L, et al. Recovery of brain structural abnormalities in morbidly obese patients after bariatric surgery. Int J Obes (Lond). 2016;40(10):1558–65. pmid:27200505
     • View Article
     • PubMed/NCBI
     • Google Scholar
182. 182. Michaud A, Dadar M, Pelletier M, Zeighami Y, Garcia-Garcia I, Iceta S, et al. Neuroanatomical changes in white and grey matter after sleeve gastrectomy. Neuroimage. 2020;213:116696. pmid:32145436
     • View Article
     • PubMed/NCBI
     • Google Scholar
183. 183. Wang Y, Ji G, Hu Y, Li G, Ding Y, Hu C, et al. Laparoscopic sleeve gastrectomy induces sustained changes in gray and white matter brain volumes and resting functional connectivity in obese patients. Surg Obes Relat Dis. 2020;16(1):1–9. pmid:31679986
     • View Article
     • PubMed/NCBI
     • Google Scholar
184. 184. Zeighami Y, Iceta S, Dadar M, Pelletier M, Nadeau M, Biertho L, et al. Spontaneous neural activity changes after bariatric surgery: a resting-state fMRI study. Neuroimage. 2021;241:118419. pmid:34302967
     • View Article
     • PubMed/NCBI
     • Google Scholar
185. 185. Almby KE, Lundqvist MH, Abrahamsson N, Kvernby S, Fahlström M, Pereira MJ, et al. Effects of gastric bypass surgery on the brain: simultaneous assessment of glucose uptake, blood flow, neural activity, and cognitive function during normo- and hypoglycemia. Diabetes. 2021;70(6):1265–77. pmid:33674408
     • View Article
     • PubMed/NCBI
     • Google Scholar
186. 186. Stillman CM, Jakicic J, Rogers R, Alfini AJ, Smith JC, Watt J, et al. Changes in cerebral perfusion following a 12-month exercise and diet intervention. Psychophysiology. 2021;58(7):e13589. pmid:32343445
     • View Article
     • PubMed/NCBI
     • Google Scholar
187. 187. Arbel-Ornath M, Hudry E, Eikermann-Haerter K, Hou S, Gregory JL, Zhao L, et al. Interstitial fluid drainage is impaired in ischemic stroke and Alzheimer’s disease mouse models. Acta Neuropathol. 2013;126(3):353–64. pmid:23818064
     • View Article
     • PubMed/NCBI
     • Google Scholar
188. 188. ElAli A, Thériault P, Préfontaine P, Rivest S. Mild chronic cerebral hypoperfusion induces neurovascular dysfunction, triggering peripheral beta-amyloid brain entry and aggregation. Acta Neuropathol Commun. 2013;1:75. pmid:24252187
     • View Article
     • PubMed/NCBI
     • Google Scholar
189. 189. Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci. 2011;12(12):723–38. pmid:22048062
     • View Article
     • PubMed/NCBI
     • Google Scholar
190. 190. Niwa K, Kazama K, Younkin L, Younkin SG, Carlson GA, Iadecola C. Cerebrovascular autoregulation is profoundly impaired in mice overexpressing amyloid precursor protein. Am J Physiol Heart Circ Physiol. 2002;283(1):H315-23. pmid:12063304
     • View Article
     • PubMed/NCBI
     • Google Scholar
191. 191. Niwa K, Kazama K, Younkin SG, Carlson GA, Iadecola C. Alterations in cerebral blood flow and glucose utilization in mice overexpressing the amyloid precursor protein. Neurobiol Dis. 2002;9(1):61–8. pmid:11848685
     • View Article
     • PubMed/NCBI
     • Google Scholar
192. 192. Okamoto Y, Yamamoto T, Kalaria RN, Senzaki H, Maki T, Hase Y, et al. Cerebral hypoperfusion accelerates cerebral amyloid angiopathy and promotes cortical microinfarcts. Acta Neuropathol. 2012;123(3):381–94. pmid:22170742
     • View Article
     • PubMed/NCBI
     • Google Scholar
193. 193. Caplan LR, Hennerici M. Impaired clearance of emboli (washout) is an important link between hypoperfusion, embolism, and ischemic stroke. Arch Neurol. 1998;55(11):1475–82. pmid:9823834
     • View Article
     • PubMed/NCBI
     • Google Scholar
194. 194. Morais LH, Schreiber 4th HL, Mazmanian SK. The gut microbiota-brain axis in behaviour and brain disorders. Nat Rev Microbiol. 2021;19(4):241–55. pmid:33093662
     • View Article
     • PubMed/NCBI
     • Google Scholar
195. 195. Oh HS-H, Le Guen Y, Rappoport N, Urey DY, Farinas A, Rutledge J, et al. Plasma proteomics links brain and immune system aging with healthspan and longevity. Nat Med. 2025;31(8):2703–11. pmid:40634782
     • View Article
     • PubMed/NCBI
     • Google Scholar
196. 196. Imam F, Saloner R, Vogel JW, Krish V, Abdel-Azim G, Ali M, et al. The global neurodegeneration proteomics consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging. Nat Med. 2025;31(8):2556–66. pmid:40665048
     • View Article
     • PubMed/NCBI
     • Google Scholar
197. 197. O’Mahony SM, Clarke G, Borre YE, Dinan TG, Cryan JF. Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behav Brain Res. 2015;277:32–48. pmid:25078296
     • View Article
     • PubMed/NCBI
     • Google Scholar
198. 198. Jiang C, Zhan Q, Zeng C. The 5-HT-related gut-brain axis in obesity. Life Sci. 2024;358:123171. pmid:39447731
     • View Article
     • PubMed/NCBI
     • Google Scholar
199. 199. Hwang YK, Oh JS. Interaction of the Vagus Nerve and Serotonin in the Gut-Brain Axis. Int J Mol Sci. 2025;26(3):1160. pmid:39940928
     • View Article
     • PubMed/NCBI
     • Google Scholar
200. 200. Sun BB, Chiou J, Traylor M, Benner C, Hsu Y-H, Richardson TG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622(7982):329–38. pmid:37794186
     • View Article
     • PubMed/NCBI
     • Google Scholar
201. 201. Winchester L, Barber I, Lawton M, Ash J, Liu B, Evetts S, et al. Identification of a possible proteomic biomarker in Parkinson’s disease: discovery and replication in blood, brain and cerebrospinal fluid. Brain Commun. 2022;5(1):fcac343. pmid:36694577
     • View Article
     • PubMed/NCBI
     • Google Scholar
202. 202. Rodriguez-Muñoz A, Motahari-Rad H, Martin-Chaves L, Benitez-Porres J, Rodriguez-Capitan J, Gonzalez-Jimenez A, et al. A systematic review of proteomics in obesity: unpacking the molecular puzzle. Curr Obes Rep. 2024;13(3):403–38. pmid:38703299
     • View Article
     • PubMed/NCBI
     • Google Scholar
203. 203. Loesch DP, Garg M, Matelska D, Vitsios D, Jiang X, Ritchie SC, et al. Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities. Nat Commun. 2025;16(1):2124. pmid:40032831
     • View Article
     • PubMed/NCBI
     • Google Scholar
204. 204. Craik FIM, Bialystok E. Cognition through the lifespan: mechanisms of change. Trends Cogn Sci. 2006;10(3):131–8. pmid:16460992
     • View Article
     • PubMed/NCBI
     • Google Scholar
205. 205. Ghisletta P, Rabbitt P, Lunn M, Lindenberger U. Two thirds of the age-based changes in fluid and crystallized intelligence, perceptual speed, and memory in adulthood are shared. Intelligence. 2012;40(3):260–8.
     • View Article
     • Google Scholar
206. 206. Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychol Sci. 2015;26(4):433–43. pmid:25770099
     • View Article
     • PubMed/NCBI
     • Google Scholar
207. 207. Ferguson HJ, Brunsdon VEA, Bradford EEF. The developmental trajectories of executive function from adolescence to old age. Sci Rep. 2021;11(1):1382. pmid:33446798
     • View Article
     • PubMed/NCBI
     • Google Scholar
208. 208. Bangen KJ, Werhane ML, Weigand AJ, Edmonds EC, Delano-Wood L, Thomas KR, et al. Reduced regional cerebral blood flow relates to poorer cognition in older adults with type 2 diabetes. Front Aging Neurosci. 2018;10:270. pmid:30250430
     • View Article
     • PubMed/NCBI
     • Google Scholar
209. 209. Qureshi D, Topiwala A, Al Abid SU, Allen NE, Kuźma E, Littlejohns TJ. Association of metabolic syndrome with neuroimaging and cognitive outcomes in the UK Biobank. Diabetes Care. 2024;47(8):1415–23. pmid:38894691
     • View Article
     • PubMed/NCBI
     • Google Scholar
210. 210. Zuo Q, Song L, Gao X, Cen M, Fu X, Qin S, et al. Associations of metabolic syndrome with cognitive function and dementia risk: Evidence from the UK Biobank cohort. Diabetes Obes Metab. 2024;26(12):6023–33. pmid:39360436
     • View Article
     • PubMed/NCBI
     • Google Scholar
211. 211. Haley AP, Gonzales MM, Tarumi T, Miles SC, Goudarzi K, Tanaka H. Elevated cerebral glutamate and myo-inositol levels in cognitively normal middle-aged adults with metabolic syndrome. Metab Brain Dis. 2010;25(4):397–405. pmid:21063759
     • View Article
     • PubMed/NCBI
     • Google Scholar
212. 212. Tournoy J, Lee DM, Pendleton N, O’Neill TW, O’Connor DB, Bartfai G, et al. Association of cognitive performance with the metabolic syndrome and with glycaemia in middle-aged and older European men: the European Male Ageing Study. Diabetes Metab Res Rev. 2010;26(8):668–76. pmid:21043047
     • View Article
     • PubMed/NCBI
     • Google Scholar
213. 213. Horstmann A, Busse FP, Mathar D, Müller K, Lepsien J, Schlögl H, et al. Obesity-related differences between women and men in brain structure and goal-directed behavior. Front Hum Neurosci. 2011;5:58. pmid:21713067
     • View Article
     • PubMed/NCBI
     • Google Scholar
214. 214. Mueller K, Anwander A, Möller HE, Horstmann A, Lepsien J, Busse F, et al. Sex-dependent influences of obesity on cerebral white matter investigated by diffusion-tensor imaging. PLoS One. 2011;6(4):e18544. pmid:21494606
     • View Article
     • PubMed/NCBI
     • Google Scholar
215. 215. Malpetti M, Sala A, Vanoli EG, Gianolli L, Luzi L, Perani D. Unfavourable gender effect of high body mass index on brain metabolism and connectivity. Sci Rep. 2018;8(1):12584. pmid:30135519
     • View Article
     • PubMed/NCBI
     • Google Scholar
216. 216. Hohl R, de Morais FGF, Taporoski TP, Negrão AB, Evans SL, de Oliveira CM, et al. Sex-specific impact of metabolic syndrome on brain structures vulnerable to alzheimer’s disease: a cross-sectional study in a Brazilian cohort. Brain Sci. 2025;15(12):1341. pmid:41440137
     • View Article
     • PubMed/NCBI
     • Google Scholar
217. 217. Rajan KB, Weuve J, Barnes LL, McAninch EA, Wilson RS, Evans DA. Population estimate of people with clinical Alzheimer’s disease and mild cognitive impairment in the United States (2020-2060). Alzheimers Dement. 2021;17(12):1966–75. pmid:34043283
     • View Article
     • PubMed/NCBI
     • Google Scholar
218. 218. Lopez-Lee C, Torres ERS, Carling G, Gan L. Mechanisms of sex differences in Alzheimer’s disease. Neuron. 2024;112(8):1208–21. pmid:38402606
     • View Article
     • PubMed/NCBI
     • Google Scholar
219. 219. Arenaza-Urquijo EM, Boyle R, Casaletto K, Anstey KJ, Vila-Castelar C, Colverson A, et al. Sex and gender differences in cognitive resilience to aging and Alzheimer’s disease. Alzheimers Dement. 2024;20(8):5695–719. pmid:38967222
     • View Article
     • PubMed/NCBI
     • Google Scholar
220. 220. Krause DN, Duckles SP, Pelligrino DA. Influence of sex steroid hormones on cerebrovascular function. J Appl Physiol (1985). 2006;101(4):1252–61. pmid:16794020
     • View Article
     • PubMed/NCBI
     • Google Scholar
221. 221. Miller VM, Mulvagh SL. Sex steroids and endothelial function: translating basic science to clinical practice. Trends Pharmacol Sci. 2007;28(6):263–70. pmid:17466385
     • View Article
     • PubMed/NCBI
     • Google Scholar
222. 222. Cipolla MJ, Godfrey JA, Wiegman MJ. The effect of ovariectomy and estrogen on penetrating brain arterioles and blood-brain barrier permeability. Microcirculation. 2009;16(8):685–93. pmid:19905968
     • View Article
     • PubMed/NCBI
     • Google Scholar
223. 223. Collignon A, Dion-Albert L, Ménard C, Coelho-Santos V. Sex, hormones and cerebrovascular function: from development to disorder. Fluids and Barriers of the CNS. 2024;21(1):2.
     • View Article
     • Google Scholar
224. 224. Teo TWJ, Saffari SE, Chan LL, Welton T. Comparison of MRI head motion indicators in 40,969 subjects informs neuroimaging study design. Sci Rep. 2024;14(1):29430. pmid:39604510
     • View Article
     • PubMed/NCBI
     • Google Scholar
225. 225. Beyer F, Prehn K, Wüsten KA, Villringer A, Ordemann J, Flöel A, et al. Weight loss reduces head motion: revisiting a major confound in neuroimaging. Hum Brain Mapp. 2020;41(9):2490–4. pmid:32239733
     • View Article
     • PubMed/NCBI
     • Google Scholar
226. 226. Mon A, Abé C, Durazzo TC, Meyerhoff DJ. Fat may affect magnetic resonance signal intensity and brain tissue volumes. Obes Res Clin Pract. 2016;10(2):211–5. pmid:26259685
     • View Article
     • PubMed/NCBI
     • Google Scholar
227. 227. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80:105–24. pmid:23668970
     • View Article
     • PubMed/NCBI
     • Google Scholar
228. 228. Kirk TF, McConnell FAK, Toner J, Craig MS, Carone D, Li X, et al. Arterial spin labelling perfusion MRI analysis for the human connectome project lifespan ageing and development studies. Imaging Neurosci (Camb). 2025;3:imag_a_00444. pmid:40084116
     • View Article
     • PubMed/NCBI
     • Google Scholar
229. 229. Ji JL, Demšar J, Fonteneau C, Tamayo Z, Pan L, Kraljič A, et al. QuNex-An integrative platform for reproducible neuroimaging analytics. Front Neuroinform. 2023;17:1104508. pmid:37090033
     • View Article
     • PubMed/NCBI
     • Google Scholar
230. 230. Weintraub S, Dikmen SS, Heaton RK, Tulsky DS, Zelazo PD, Bauer PJ, et al. Cognition assessment using the NIH Toolbox. Neurology. 2013;80(11 Suppl 3):S54-64. pmid:23479546
     • View Article
     • PubMed/NCBI
     • Google Scholar
231. 231. Fawns-Ritchie C, Deary IJ. Reliability and validity of the UK Biobank cognitive tests. PLoS One. 2020;15(4):e0231627. pmid:32310977
     • View Article
     • PubMed/NCBI
     • Google Scholar
232. 232. Glasser MF, Van Essen DC. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci. 2011;31(32):11597–616. pmid:21832190
     • View Article
     • PubMed/NCBI
     • Google Scholar
233. 233. Sui YV, Masurkar AV, Rusinek H, Reisberg B, Lazar M. Cortical myelin profile variations in healthy aging brain: A T1w/T2w ratio study. Neuroimage. 2022;264:119743. pmid:36368498
     • View Article
     • PubMed/NCBI
     • Google Scholar
234. 234. Glasser MF, Coalson TS, Harms MP, Xu J, Baum GL, Autio JA, et al. Empirical transmit field bias correction of T1w/T2w myelin maps. Neuroimage. 2022;258:119360. pmid:35697132
     • View Article
     • PubMed/NCBI
     • Google Scholar
235. 235. Figley CR, Uddin MN, Wong K, Kornelsen J, Puig J, Figley TD. Potential pitfalls of using fractional anisotropy, axial diffusivity, and radial diffusivity as biomarkers of cerebral white matter microstructure. Front Neurosci. 2022;15:799576. pmid:35095400
     • View Article
     • PubMed/NCBI
     • Google Scholar
236. 236. Prins ND, Scheltens P. White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol. 2015;11(3):157–65. pmid:25686760
     • View Article
     • PubMed/NCBI
     • Google Scholar
237. 237. Dadar M, Collins DL. BISON: Brain tissue segmentation pipeline using T1 -weighted magnetic resonance images and a random forest classifier. Magn Reson Med. 2021;85(4):1881–94. pmid:33040404
     • View Article
     • PubMed/NCBI
     • Google Scholar
238. 238. Griffanti L, Zamboni G, Khan A, Li L, Bonifacio G, Sundaresan V, et al. BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage. 2016;141:191–205. pmid:27402600
     • View Article
     • PubMed/NCBI
     • Google Scholar
239. 239. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61(4):1000–16. pmid:22484410
     • View Article
     • PubMed/NCBI
     • Google Scholar
240. 240. Daducci A, Canales-Rodríguez EJ, Zhang H, Dyrby TB, Alexander DC, Thiran J-P. Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage. 2015;105:32–44. pmid:25462697
     • View Article
     • PubMed/NCBI
     • Google Scholar
241. 241. Mirchi N, Betzel RF, Bernhardt BC, Dagher A, Mišic B. Tracking mood fluctuations with functional network patterns. Soc Cogn Affect Neurosci. 2019;14(1):47–57. pmid:30481361
     • View Article
     • PubMed/NCBI
     • Google Scholar
242. 242. Hansen JY, Markello RD, Vogel JW, Seidlitz J, Bzdok D, Misic B. Mapping gene transcription and neurocognition across human neocortex. Nat Hum Behav. 2021;5(9):1240–50. pmid:33767429
     • View Article
     • PubMed/NCBI
     • Google Scholar
243. 243. Váša F, Mišić B. Null models in network neuroscience. Nat Rev Neurosci. 2022;23(8):493–504. pmid:35641793
     • View Article
     • PubMed/NCBI
     • Google Scholar
244. 244. Burt JB, Helmer M, Shinn M, Anticevic A, Murray JD. Generative modeling of brain maps with spatial autocorrelation. Neuroimage. 2020;220:117038. pmid:32585343
     • View Article
     • PubMed/NCBI
     • Google Scholar
245. 245. Markello RD, Hansen JY, Liu Z-Q, Bazinet V, Shafiei G, Suárez LE, et al. neuromaps: structural and functional interpretation of brain maps. Nat Methods. 2022;19(11):1472–9. pmid:36203018
     • View Article
     • PubMed/NCBI
     • Google Scholar
246. 246. Van Essen DC, Glasser MF, Dierker DL, Harwell J, Coalson T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb Cortex. 2012;22(10):2241–62. pmid:22047963
     • View Article
     • PubMed/NCBI
     • Google Scholar
247. 247. Nieuwenhuys R. The myeloarchitectonic studies on the human cerebral cortex of the Vogt-Vogt school, and their significance for the interpretation of functional neuroimaging data. Brain Struct Funct. 2013;218(2):303–52. pmid:23076375
     • View Article
     • PubMed/NCBI
     • Google Scholar
248. 248. van der Walt S, Colbert SC, Varoquaux G. The NumPy array: a structure for efficient numerical computation. Comput Sci Eng. 2011;13(2):22–30.
     • View Article
     • Google Scholar
249. 249. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585(7825):357–62. pmid:32939066
     • View Article
     • PubMed/NCBI
     • Google Scholar
250. 250. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72. pmid:32015543
     • View Article
     • PubMed/NCBI
     • Google Scholar
251. 251. McKinney W. Data structures for statistical computing in Python. SciPy. 2010;445(1):51–6.
     • View Article
     • Google Scholar
252. 252. Waskom M. seaborn: statistical data visualization. JOSS. 2021;6(60):3021.
     • View Article
     • Google Scholar
253. 253. Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9(3):90–5.
     • View Article
     • Google Scholar
254. 254. Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with python. In: Proceedings of the Python in Science Conference, 2010. p. 92–6. http://dx.doi.org/10.25080/majora-92bf1922-011
255. 255. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69. pmid:19819337
     • View Article
     • PubMed/NCBI
     • Google Scholar
256. 256. Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, et al. Machine learning for neuroimaging with scikit-learn. Front Neuroinform. 2014;8:14. pmid:24600388
     • View Article
     • PubMed/NCBI
     • Google Scholar
257. 257. Brett M, Markiewicz CJ, Hanke M, Côté MA, Cipollini B, McCarthy P, et al. Nipy/nibabel. Zenodo; 2022. https://doi.org/10.5281/zenodo.6658382
258. 258. Liu ZQ, Bazinet V, Hansen JY, Milisav F, Luppi AI, Ceballos EG. Netneurotools: a trainee-oriented approach to network neuroscience. bioRxiv. 2025;:2025–09.
259. 259. Gautier L. rpy2. https://github.com/rpy2/rpy2/releases/tag/RELEASE_3_5_17
260. 260. Marcus DS, Harms MP, Snyder AZ, Jenkinson M, Wilson JA, Glasser MF, et al. Human Connectome Project informatics: quality control, database services, and data visualization. Neuroimage. 2013;80:202–19. pmid:23707591
     • View Article
     • PubMed/NCBI
     • Google Scholar