Understanding how brain connectivity reorganizes with age is essential for characterizing healthy aging. While static functional connectivity (sFC) has revealed broad age-related shifts in network segregation and integration, recent work underscores the need to examine how these patterns dynamically fluctuate over time to better understand cognitive decline and resilience in aging. The present resting-state fMRI study was conducted to enhance comprehension of the temporal variability and dynamics of the brain’s functional architecture in the context of the aging process. To this end, dynamic functional connectivity (dFC) was extracted in 817 older adults between 55 and 85 years from the 1000BRAINS study. Using a sliding window and clustering approach, we identified four recurring dFC states and quantified temporal metrics, e.g., mean dwell time, or number of transitions. Overall, aging was associated with slower and less flexible network dynamics: integrative states became less frequent, whereas segregated states dominated, reflecting reduced inter-network communication, although the effect size was relatively small. Age-stratified analyses yielded two novel insights, thereby suggesting a refinement of the so far established theories of aging and giving rise to a new model of age-related differences in functional connectivity: First, trajectories of dedifferentiation and segregation shown from younger to older adults seem to stabilize during the transition from mid-to-old age and turn into a process of re-segregation and overcompensation during older-old ages, challenging the assumption of a monotonic increase in integration across the lifespan. Secondly, opposing links between dFC and cognitive performance have been identified, with greater network integration supporting better cognitive performance in mid-to-old adults but poorer cognitive performance in older-old adults. This suggests a shift from dedifferentiated to re-segregated connectivity with advancing age. Sex-stratified modeling further demonstrated stronger age-related reductions in flexibility among females, indicating that pooled analyses might obscure systematic sex-specific dynamics and highlighting divergent adaptive and vulnerability profiles across aging. The findings of this study emphasize the necessity of accounting for temporal variability in age-related changes of functional brain connectivity when studying aging, specifically during advanced age, with the aim of enhancing comprehension of cognitive variability.

ObjectiveSubthreshold depressive symptoms (SDS) in older adults as a prodromal state of late-life depression (LLD) increase with age and are associated with elevated risk for cardio- and cerebrovascular diseases. LLD has been linked to functional brain network disruptions, including the ventral attention (VAN) and default mode network (DMN). Thus, identifying alterations in functional connectivity (FC) linked to SDS may be critical for the early detection and treatment of individuals at high-risk for LLD.MethodsA total of 243 healthy older subjects (55–84 years; MAge = 67.0 ± 6.5) without a history of depression and current antidepressant intake from the 1000BRAINS sample with two timepoints of assessments were included in this study (time interval = 3.7 ± 0.7 years). SDS were measured using the Beck Depression Inventory II (BDI-II < 20) and linked to resting-state functional magnetic resonance imaging derived FC within and between seven large scale functional brain networks.ResultsBoth cross-sectional and longitudinal analyses revealed that SDS were associated with decreased intra- and inter-network FC. After Bonferroni correction for multiple comparisons, reduced FC within the VAN at baseline significantly predicted a longitudinal increase in depressive symptoms. This association was primarily driven by the somatic symptom domain of the BDI-II. Post-hoc analyses highlighted the particular involvement of right-hemispheric VAN regions.ConclusionOur findings support the hypothesis that even minimal to mild depressive symptoms in older adults are linked to disrupted functional network architecture. Specifically, reduced FC within the VAN may serve as an early neural marker for the emergence of depressive symptoms and vulnerability for the progression into clinically manifest LLD. Thus, offering potential for early detection and targeted intervention in subjects at high risk.

Cognitive performance prediction may help identify early cognitive decline. However, the heterogeneity of research findings impedes the identification of key predictors. This study used 21,877 participants (25–74 years) from the German National Cohort (NAKO Gesundheitsstudie, NAKO) to systematically predict cognitive test scores based on brain structure, demographic, health-related, and cognitive data. Importantly, validation analyses were performed across study sites and external samples (1000BRAINS). Higher predictability was observed in the total sample compared to age-specific subgroups (10

Anxiety disorders are common and impairing mental health conditions. Using data from 26,378 adults in the German National Cohort Study (NAKO), we investigated psychosocial and neuroimaging predictors of generalized anxiety disorder (GAD) symptoms and panic attacks. We conducted machine-learning analyses of 246 regions of interest from whole-brain imaging data in combination with psychosocial variables. Neuroimaging data alone showed suboptimal classification performance, whereas psychosocial variables alone - particularly depressive symptoms, stress, and childhood trauma - achieved the strongest discrimination for GAD symptoms and panic attacks. Adding neuroimaging features to psychosocial models modestly improved unbalanced accuracy and specificity by reducing false-positive classifications, indicating a conditional and complementary contribution of neuroanatomical information. Within the multivariate models, features from anxiety-related circuits, including the amygdala and superior parietal lobule, were consistently selected. Overall, these findings suggest that psychosocial factors dominate classification of anxiety outcomes, while structural MRI measures may provide complementary information within multimodal frameworks aimed at refining classification and supporting the development of individualized risk profiles to guide tailored therapeutic and preventive strategies.

Anxiety disorders (ANX) are common and impairing mental health conditions. This study aimed to classify self-reported symptoms of generalized anxiety disorder (GAD) and panic attacks as two psychopathological manifestations of ANX by applying machine learning to a cross-sectional dataset of 26,378 adults from the German National Cohort Study (NAKO). We first explored linear relationships between preselected neuroimaging correlates in MRI scans and anxiety phenotypes. Overall, sex-stratified correlation coefficients - while partly highly signifi-cant - were extremely low with r ≤ .04 for panic attacks and r ≤ .06 for GAD symptoms after correction for confounding variables like childhood trauma and depression. We then examined the combined classifying value of whole-brain imaging data of 246 ROIs in addition to psycho-social variables such as self-reported depression symptoms, stress, and childhood trauma, using four machine learning algorithms (support-vector machines with linear and radial kernels, elastic-net regression, and random forest). Neuroimaging data, particularly gray-matter vol-umes in regions such as the amygdala and superior parietal lobule, contributed to classifica-tion, but performance was substantially better when psychosocial variables were added. For both GAD symptoms and panic attacks, depression, stress and childhood trauma were the clearest indicators the classification would show the condition was present. Random forest models based on psychosocial variables alone achieved the highest discrimination perfor-mance for GAD symptoms (area under the receiver operating characteristic curve, AUROC = 0.973) and panic attacks (AUROC = 0.933). Combining neuroimaging and psychosocial varia-bles in elastic-net regressions further improved specificity. These results support multimodal approaches to diagnose and investigate ANX that integrate structural brain abnormalities and psychosocial measures to capture the complexity of GAD and panic attacks, enabling the crea-tion of individual risk profiles based on multiple biomarkers. These profiles may guide tailored therapeutic and preventive interventions.

Subthreshold depressive symptoms (SDS) increase with age and adversely affect cognition, physical health, and quality of life. Therefore, this retrospective study investigated the cross-sectional and longitudinal associations between the absence or presence of SDS and neuropsychological performance, physical health, and quality of life in a large, representative sample of older adults aged 55 + . Data on cognition, physical variables, and psychosocial measures from a healthy subsample of participants aged 55 to 87 years from the population-based 1000BRAINS study were retrospectively analyzed. Cross-sectional data were available for 6691000BRAINS participants, and longitudinal data for 255 participants. The relationship between the absence or presence of SDS and neuropsychological, physical health, and psychosocial measures was examined by linear regression models and repeated-measures analysis of covariance, adjusted for age, sex, educational level, and multiple comparisons. Greater severity of SDS at baseline was linked to poorer performance in cognitive domains, including processing speed, executive function, figural and verbal memory, and in the dementia screening test, lower physical activity levels, and more physical complaints. Increased SDS over time correlated with accelerated vascular ageing, higher body mass index, and lower quality of life. In older adults, SDS are associated with impaired cognitive function and lower physical activity levels and quality of life. Further research in the field of early recognition of late-life depression should assess cognition and physical health to identify clinical factors and their interaction in the development of late-life depression.

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

BACKGROUND:Magnetic resonance imaging (MRI) yields important information on the development and current status of many different diseases. Whole-body MRI was accordingly made a part of the multicenter, population-based NAKO Health Study. The present analysis concerns the feasibility of the baseline MRI examination and various aspects of quality assurance over the period 2014-2019. METHODS:32 252 participants in the NAKO Health Study, aged 20 to 74, who had no contraindication to MRI were invited to undergo scanning in one of five MRI study centers across Germany. The whole-body MRI scan took about one hour and consisted of sequences for the visualization of structural and functional features of the brain, musculoskeletal system, cardiovascular system, and thoracoabdominal system. A comprehensive quality-assurance assessment was carried out, with evaluation of adverse events, the completeness of the MRI protocols, the participants' subjective perceptions, and image quality. RESULTS:31 578 participants (97.9%) were successfully included in the MRI study. They reported a high level of comfort and suffered no severe adverse events (mild adverse events occurred in only four participants). Depending on the imaging sequence, the image quality was rated as excellent in 80.2% to 96.8% of cases. Quality assessment with respect to structural features of the brain revealed high consistency across study centers, as well as with regard to age- and sex-based differences in brain volume (men, 1203.81 ± 102.06 cm³; women, 1068.10 ± 86.69 cm³). CONCLUSION:Whole-body MRI was successfully implemented in the NAKO baseline examination and was associated with high patient comfort and very good image quality. The imaging biomarkers of the brain confirmed previously observed differences based on age and sex, underscoring the feasibility of data pooling.

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

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We performa genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimagingstudies showthat 17 variants affect specificbrain areas, butmost have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

A previously published genome-wide association study (GWAS) meta-analysis across eight neuropsychiatric disorders identified antagonistic single-nucleotide polymorphisms (SNPs) at eleven genomic loci where the same allele was protective against one neuropsychiatric disorder and increased the risk for another. Until now, these antagonistic SNPs have not been further investigated regarding their link to brain structural phenotypes. Here, we explored their associations with cortical surface area and cortical thickness (in 34 brain regions and one global measure each) as well as the volumes of eight subcortical structures using summary statistics of large-scale GWAS of brain structural phenotypes. We assessed if significantly associated brain structural phenotypes were previously reported to be associated with major neuropsychiatric disorders in large-scale case-control imaging studies by the ENIGMA consortium. We further characterized the effects of the antagonistic SNPs on gene expression in brain tissue and their association with additional cognitive and behavioral phenotypes, and performed an exploratory voxel-based whole-brain analysis in the FOR2107 study (n = 754 patients with major depressive disorder and n = 847 controls). We found that eight antagonistic SNPs were significantly associated with brain structural phenotypes in regions such as anterior parts of the cingulate cortex, the insula, and the superior temporal gyrus. Case-control differences in implicated brain structural phenotypes have previously been reported for bipolar disorder, major depressive disorder, and schizophrenia. In addition, antagonistic SNPs were associated with gene expression changes in brain tissue and linked to several cognitive-behavioral traits. In our exploratory whole-brain analysis, we observed significant associations of gray matter volume in the left superior temporal pole and left superior parietal region with the variants rs301805 and rs1933802, respectively. Our results suggest that multiple antagonistic SNPs for neuropsychiatric disorders are linked to brain structural phenotypes. However, to further elucidate these findings, future case-control genomic imaging studies are required.

Background:Bilingualism is associated with higher gray matter volume (GMV) as a form of brain reserve in brain regions such as the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL). A recent cross-sectional study reported the age-related GMV decline in the left IFG and IPL to be steeper for bilinguals than for monolinguals. The present study aimed at supporting this finding for the first time with longitudinal data.Methods:In the current study, 200 participants aged 19 to 79 years (87 monolinguals, 113 sequential bilinguals, mostly native German speakers with variable second language background) were included. Trajectories of GMV decline in the bilateral IFG and IPL were analyzed in mono- and bilinguals over two time points (mean time interval: 3.6 years). For four regions of interest (left/right IFG and left/right IPL), mixed Analyses of Covariance were conducted to assess (i) GMV changes over time, (ii) GMV differences for language groups (monolinguals/bilinguals), and (iii) the interaction between time point and language group. Corresponding analyses were conducted for the two factors of GMV, surface area (SA) and cortical thickness (CT).Results:There was higher GMV in bilinguals compared to monolinguals in the IPL, but not IFG. While the left and right IFG and the right IPL displayed a similar GMV change in mono- and bilinguals, GMV decline within the left IPL was significantly steeper in bilinguals. There was greater SA in bilinguals in the bilateral IPL and a steeper CT decline in bilinguals within in the left IPL.Conclusion:The cross-sectional observations of a steeper GMV decline in bilinguals could be confirmed for the left IPL. Additionally, the higher GMV in bilinguals in the bilateral IPL may indicate that bilingualism contributes to brain reserve especially in posterior brain regions. SA appeared to contribute to bilinguals' higher GMV in the bilateral IPL, while CT seemed to account for the steeper structural decline in bilinguals in the left IPL. The present findings demonstrate the importance of time as an additional factor when assessing the neuroprotective effects of bilingualism on structural features of the human brain.

Structural brain imaging parameters may successfully predict cognitive performance in neurodegenerative diseases but mostly fail to predict cognitive abilities in healthy older adults. One important aspect contributing to this might be sex differences. Behaviorally, older males and females have been found to differ in terms of cognitive profiles, which cannot be captured by examining them as one homogenous group. In the current study, we examined whether the prediction of cognitive performance from brain structure, i.e. region-wise grey matter volume (GMV), would benefit from the investigation of sex-specific cognitive profiles in a large sample of older adults (1000BRAINS; N = 634; age range 55–85 years). Prediction performance was assessed using a machine learning (ML) approach. Targets represented a) a whole-sample cognitive component solution extracted from males and females, and b) sex-specific cognitive components. Results revealed a generally low predictability of cognitive profiles from region-wise GMV. In males, low predictability was observed across both, the whole sample as well as sex-specific cognitive components. In females, however, predictability differences across sex-specific cognitive components were observed, i.e. visual working memory (WM) and executive functions showed higher predictability than fluency and verbal WM. Hence, results accentuated that addressing sex-specific cognitive profiles allowed a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings not only emphasize the need to further investigate the predictive power of each cognitive component, but they also emphasize the importance of sex-specific analyses in older adults.

BACKGROUND:Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS:Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS:For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS:We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.

White matter hyperintensities of presumed vascular origin (WMH) are frequent in cerebral magnetic resonance imaging of older people. They are promoted by vascular risk factors, especially hypertension, and are associated with cognitive deficits at the group level. It has been suggested that not only the severity, but also the location, of lesions might critically influence cognitive deficits and represent different pathologies. In 560 participants (65.2 ± 7.5 years, 51.4% males) of the population-based 1000BRAINS study, we analyzed the association of regional WMH using Fazekas scoring separately for cerebral lobes, with hypertension and cognition. WMH most often affected the frontal lobe (83.7% score >0), followed by the parietal (75.8%), temporal (32.7%), and occipital lobe (7.3%). Higher Fazekas scores in the frontal, parietal, and temporal lobe were associated with higher blood pressure and antihypertensive treatment in unadjusted ordinal regression models and in models adjusted for age, sex, and vascular risk factors (e.g., age- and sex-adjusted odds ratio = 1.14, 95% confidence interval = 1.03–1.25 for the association of frontal lobe WMH Fazekas score with systolic blood pressure [SBP] [per 10 mm Hg]; 1.13 [1.02–1.23] for the association of parietal lobe score with SBP; 1.72 [1.19–2.48] for the association of temporal lobe score with antihypertensive medications). In linear regressions, higher frontal lobe scores were associated with lower performance in executive function and non-verbal memory, and higher parietal lobe scores were associated with lower performance in executive function, verbal-, and non-verbal memory. Hypertension promotes WMH in the frontal, parietal, and temporal lobe. WMH in the frontal and parietal lobe are associated with reduced executive function and memory.

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.

Some individuals seem less susceptible to the effect of high white matter hyperintensity (WMH) load on cognition reflecting differences in individual cognitive reserve (CR). Little is known about young- or middle-aged participants. The aim of the present study was to examine (1) the effect of WMH on global cognition in three different age groups and (2) if education (as proxy for CR) moderates this association. We included 707 healthy participants (18-85 years) without evidence of cardiovascular/neurological disease (young-aged: 18-44 years (Ø33.5±6.7): n = 108; middle-aged: 45-65 years (Ø57.9±5.5): n = 341; old-aged: >65 years (Ø72.0±4.1): n = 258) from the population-based 1000BRAINS study. An extensive cognitive assessment was conducted. The sum of all cognitive domain z-scores defined the global score. Education was classified according to the International Standard Classification of Education as total years of formal education, combining school and vocational training. Magnetic resonance imaging (MRI) was carried out on a 3-Tesla-MR-scanner (Tim-TRIO, Siemens Medical Systems, Erlangen, Germany). WMH volume was determined using the Brain-Intensity-Abnormality-Classification-Algorithm. The associations of global cognition as outcome with WMH volume (in cm 3 ) as predictor were analyzed using linear models (PROCESS v4.1 macro for SPPS) stratified by age group resulting in regression coefficient b with 95% confidence intervals (CI; adjusted for age, sex, depression, diabetes mellitus). To examine moderation effects of education, all models contained an interaction term (WMH x education). Higher WMH volume was associated with lower global cognition in middle-aged participants (b:-0.27 (-0.52 to -0.03, all reported results are fully adjusted). This effect was moderated by education (interaction term: b:-0.07 (-0.014 to -0.01)). In the young-aged group, the association between WMH load and cognition was -1.27 (-3.30 to 0.77). No association was found in the old-aged group (b: -0.03 (-0.20 to 0.14)). Higher WMH load was associated with lower cognitive performance only in middle-aged participants and was moderated to a small degree by education. Overall, the influence of WMH on global cognition in our cohort of healthy participants seems limited. Future analyses will focus on specific cognitive domains that might be more vulnerable to higher WMH load and will examine participants with certain cardiovascular risk profile.

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.