Age- and function-related regional changes in cortical folding of the default mode network in older adults

Aging Processes of Working Memory in Different Modalities

Background: Working memory (WM) involves temporarily storing and manipulating information. Research on the impact of aging on WM has shown inconsistent results regarding the decline in visual and verbal WM, with a lack of studies on tactile WM. This study aimed to assess the effects of aging on WM across verbal, visuospatial, and tactile modalities using span tasks of forward (storage) and backward (manipulation) stages. Methods: A total of 130 participants, divided into four age groups of 20-29, 60-69, 70-79, and 80-89, completed the Digit, Visuospatial, and Tactual Spans. Performance was analyzed using a 3 (Task) × 4 (Group) × 2 (Stage) mixed design repeated measures ANOVA. Results: The analysis revealed significant main effects for modality (p < 0.001, ηp2 = 0.15), age (p < 0.001, ηp2 = 0.48), and stage (p < 0.001, ηp2 = 0.30). Digit Span outperformed the other modalities, while Tactual Span showed the worst performance. Additionally, task performance declined with age, and the forward stage was superior to the backward stage. Interaction effects indicated that Digit Span was less affected by aging compared to the Visuospatial and Tactual Spans (p = 0.004, ηp2 = 0.07). Post hoc analyses further revealed that the Digit Span consistently outperformed the other modalities in both stages, with more pronounced differences observed in the forward stage. Conclusions: Verbal WM is more resilient to aging compared to the other modalities while tactile WM declines with age in a manner similar to verbal and visuospatial WM, suggesting a modality-specific impact of aging on WM.

Aging's Effect on Working Memory-Modality Comparison

Research exploring the impact of development and aging on working memory (WM) has primarily concentrated on visual and verbal domains, with limited attention paid to the tactile modality. The current study sought to evaluate WM encompassing storage and manipulation across these three modalities, spanning from childhood to old age. The study included 134 participants, divided into four age groups: 7-8, 11-12, 25-35, and 60-69. Each participant completed the Visuospatial Span, Digit Span, and Tactual Span, with forward and backward recall. The findings demonstrated a consistent trend in both forward and backward stages. Performance improved until young adulthood, progressively diminishing with advancing age. In the forward stage, the Tactual Span performance was worse than that of the Digit and Visuospatial Span for all participants. In the backward stage, the Visuospatial Span outperformed the Digit and Tactual Span across all age groups. Furthermore, the Tactual Span backward recall exhibited significantly poorer performance than the other modalities, primarily in the youngest and oldest age groups. In conclusion, age impacts WM differently across modalities, with tactile storage capacity being the most vulnerable. Additionally, tactile manipulation skills develop later in childhood but deteriorate sooner in adulthood, indicating a distinct component within tactile WM.

Cortical folding correlates to aging and Alzheimer's Disease's cognitive and CSF biomarkers

This manuscript presents the quantification and correlation of three aspects of Alzheimer's Disease evolution, including structural, biochemical, and cognitive assessments. We aimed to test a novel structural biomarker for neurodegeneration based on a cortical folding model for mammals. Our central hypothesis is that the cortical folding variable, representative of axonal tension in white matter, is an optimal discriminator of pathological aging and correlates with altered loadings in Cerebrospinal Fluid samples and a decline in cognition and memory. We extracted morphological features from T1w 3T MRI acquisitions using FreeSurfer from 77 Healthy Controls (age = 66 ± 8.4, 69% females), 31 Mild Cognitive Impairment (age = 72 ± 4.8, 61% females), and 13 Alzheimer's Disease patients (age = 77 ± 6.1, 62% females) of recruited volunteers in Brazil to test its discriminative power using optimal cut-point analysis. Cortical folding distinguishes the groups with reasonable accuracy (Healthy Control-Alzheimer's Disease, accuracy = 0.82; Healthy Control-Mild Cognitive Impairment, accuracy = 0.56). Moreover, Cerebrospinal Fluid biomarkers (total Tau, A[Formula: see text]1-40, A[Formula: see text]1-42, and Lipoxin) and cognitive scores (Cognitive Index, Rey's Auditory Verbal Learning Test, Trail Making Test, Digit Span Backward) were correlated with the global neurodegeneration in MRI aiming to describe health, disease, and the transition between the two states using morphology.

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

Introduction

Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission.

Methods

We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks.

Results

The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles.

Discussion

These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

Reorganization of resting-state brain network functional connectivity across human brain developmental stages

Cognitive brain aging can either be healthy or degenerative in nature. Multiple alterations occur in brain networks with healthy aging. Much of this has yet to be investigated. This study seeks to understand the typical healthy human brain's developmental stages using a publicly available dataset from the human connectome project. As the human brain's developmental stage varies, we also intend to identify a pattern of reorganization in the resting state functional connectivity of several brain networks. The results are specifically presented based on the resting state BOLD signals of 1096 healthy volunteers between the age group of 7-89 years. The k-means clustering method has been used to determine the various human brain developmental stages. Using the t-SNE technique, the clusters are visually separated. BrainNet Viewer is used to study the changes in resting state functional connectivity of the entire brain as the human brain developmental stages vary. The age-related pattern of change in the resting state functional connectivity of six Dosenbasch brain networks that were grouped using the k-means elbow approach has been additionally presented. For performance evaluation, three metrics of brain network connection including network segregation, between network connectivity, and within-network connectivity are used. As the age cohort changes, a consistent pattern in the variance of these connection indices is seen for different Dosenbasch brain networks. Thus, the study's findings suggest that healthy aging causes a functional reorganization of the resting state brain network connections.

A correlational study between microstructural, macrostructural and functional age-related changes in the human visual cortex

Age-related changes in the human brain can be investigated from either structural or functional perspectives. Analysis of structural and functional age-related changes throughout the lifespan may help to understand the normal brain development process and monitor the structural and functional pathology of the brain. This study, combining dedicated electroencephalography (EEG) and magnetic resonance imaging (MRI) approaches in adults (20-78 years), highlights the complex relationship between micro/macrostructural properties and the functional responses to visual stimuli. Here, we aimed to relate age-related changes of the latency of visual evoked potentials (VEPs) to micro/macrostructural indexes and find any correlation between micro/macrostructural features, as well. We studied age-related structural changes in the brain, by using the MRI and diffusion-weighted imaging (DWI) as preferred imaging methods for extracting brain macrostructural parameters such as the cortical thickness, surface area, folding and curvature index, gray matter volume, and microstructural parameters such as mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). All the mentioned features were significantly correlated with age in V1 and V2 regions of the visual cortex. Furthermore, we highlighted, negative correlations between structural features extracted from T1-weighted images and DWI. The latency and amplitude of the three dominants peaks (C1, P1, N1) of the VEP were considered as the brain functional features to be examined for correlation with age and structural features of the corresponding age. We observed significant correlations between mean C1 latency and GM volume averaged in V1 and V2. In hierarchical regression analysis, the structural index did not contribute to significant variance in the C1 latency after regressing out the effect of age. However, the age explained significant variance in the model after regressing out the effect of structural feature.

Architecture and connectivity of the human angular gyrus and of its homolog region in the macaque brain

The angular gyrus roughly corresponds to Brodmann's area 39, which is a multimodal association brain region located in the posterior apex of the human inferior parietal lobe, at its interface with the temporal and occipital lobes. It encompasses two cyto- and receptor architectonically distinct areas: caudal PGp and rostral PGa. The macaque brain does not present an angular gyrus in the strict sense, and the establishment of homologies was further hindered by the fact that Brodmann defined a single cytoarchitectonic area covering the entire guenon inferior parietal lobule in the monkey brain, i.e. area 7. Latter architectonic studies revealed the existence of 6 architectonically distinct areas within macaque area 7, further connectivity and functional imaging studies supported the hypothesis that the most posterior of these macaque areas, namely Opt and PG, may constitute the homologs of human areas PGp and PGa, respectively. The present review provides an overview of the cyto-, myelo and receptor architecture of human areas PGp and PGa, as well as of their counterparts in the macaque brain, and summarizes current knowledge on the connectivity of these brain areas. Finally, the present study elaborates on the rationale behind the definition of these homologies and their importance in translational studies.

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

The angular gyrus (AG) has been associated with multiple cognitive functions, such as language, spatial and memory functions. Since the AG is thought to be a cross-modal hub region suffering from significant age-related structural atrophy, it may also play a key role in age-related cognitive decline. However, the exact relation between structural atrophy of the AG and cognitive decline in older adults is not fully understood, which may be related to two aspects: First, the AG is cytoarchitectonically divided into two areas, PGa and PGp, potentially sub-serving different cognitive functions. Second, the older adult population is characterized by high between-subjects variability which requires targeting individual phenomena during the aging process. We therefore performed a multimodal (gray matter volume [GMV], resting-state functional connectivity [RSFC] and structural connectivity [SC]) characterization of AG subdivisions PGa and PGp in a large older adult population, together with relations to age, cognition and lifestyle on the group level. Afterwards, we switched the perspective to the individual, which is especially important when it comes to the assessment of individual patients. The AG can be considered a heterogeneous structure in of the older brain: we found the different AG parts to be associated with different patterns of whole-brain GMV associations as well as their associations with RSFC, and SC patterns. Similarly, differential effects of age, cognition and lifestyle on the GMV of AG subdivisions were observed. This suggests each region to be structurally and functionally differentially involved in the older adult's brain network architecture, which was supported by differential molecular and genetic patterns, derived from the EBRAINS multilevel atlas framework. Importantly, individual profiles deviated considerably from the global conclusion drawn from the group study. Hence, general observations within the older adult population need to be carefully considered, when addressing individual conditions in clinical practice.

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging

Background

Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures.

Methods

MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject's age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD.

Findings

Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala.

Conclusion

Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

Comprehensive Cortical Structural Features Predict the Efficacy of Cognitive Behavioral Therapy in Obsessive-Compulsive Disorder

Although cognitive behavioral therapy (CBT) is effective for patients with obsessive-compulsive disorder (OCD), 40% of OCD patients show a poor response to CBT. This study aimed to identify the cortical structural factors that predict CBT outcomes in OCD patients. A total of 56 patients with OCD received baseline structural MRI (sMRI) scanning and 14 individual CBT sessions. The linear support vector regression (SVR) models were used to identify the predictive performance of sMRI indices, including gray matter volume, cortical thickness, sulcal depth, and gyrification value. The patients’ OC symptoms decreased significantly after CBT intervention (p < 0.001). We found the model with the comprehensive variables exhibited better performance than the models with single structural indices (MAE = 0.14, MSE = 0.03, R2 = 0.36), showing a significant correlation between the true value and the predicted value (r = 0.63, p < 0.001). The results indicated that a model integrating four cortical structural features can accurately predict the effectiveness of CBT for OCD. Future models incorporating other brain indicators, including brain functional indicators, EEG indicators, neurotransmitters, etc., which might be more accurate for predicting the effectiveness of CBT for OCD, are needed.

Age- and Episodic Memory-related Differences in Task-based Functional Connectivity in Women and Men

Aging is associated with episodic memory decline and changes in functional brain connectivity. Understanding whether and how biological sex influences age- and memory performance-related functional connectivity has important theoretical implications for the cognitive neuroscience of memory and aging. Here, we scanned 161 healthy adults between 19 and 76 years of age in an event-related fMRI study of face-location spatial context memory. Adults were scanned while performing easy and difficult versions of the task at both encoding and retrieval. We used multivariate whole-brain partial least squares connectivity to test the hypothesis that there are sex differences in age- and episodic memory performance-related functional connectivity. We examined how individual differences in age and retrieval accuracy correlated with task-related connectivity. We then repeated this analysis after disaggregating the data by self-reported sex. We found that increased encoding and retrieval-related connectivity within the dorsal attention network (DAN), and between DAN and frontoparietal network and visual networks, were positively correlated to retrieval accuracy and negatively correlated with age in both sexes. We also observed sex differences in age- and performance-related functional connectivity: (a) Greater between-networks integration was apparent at both levels of task difficulty in women only, and (b) increased DAN-default mode network connectivity with age was observed in men and was correlated with poorer memory performance. Therefore, the neural correlates of age-related episodic memory decline differ in women and men and have important theoretical and clinical implications for the cognitive neuroscience of memory, aging, and dementia prevention.

Musicianship-Related Structural and Functional Cortical Features Are Preserved in Elderly Musicians

Background

Professional musicians are a model population for exploring basic auditory function, sensorimotor and multisensory integration, and training-induced neuroplasticity. The brain of musicians exhibits distinct structural and functional cortical features; however, little is known about how these features evolve during aging. This multiparametric study aimed to examine the functional and structural neural correlates of lifelong musical practice in elderly professional musicians.

Methods

Sixteen young musicians, 16 elderly musicians (age >70), and 15 elderly non-musicians participated in the study. We assessed gray matter metrics at the whole-brain and region of interest (ROI) levels using high-resolution magnetic resonance imaging (MRI) with the Freesurfer automatic segmentation and reconstruction pipeline. We used BrainVoyager semiautomated segmentation to explore individual auditory cortex morphotypes. Furthermore, we evaluated functional blood oxygenation level-dependent (BOLD) activations in auditory and non-auditory regions by functional MRI (fMRI) with an attentive tone-listening task. Finally, we performed discriminant function analyses based on structural and functional ROIs.

Results

A general reduction of gray matter metrics distinguished the elderly from the young subjects at the whole-brain level, corresponding to widespread natural brain atrophy. Age- and musicianship-dependent structural correlations revealed group-specific differences in several clusters including superior, middle, and inferior frontal as well as perirolandic areas. In addition, the elderly musicians exhibited increased gyrification of auditory cortex like the young musicians. During fMRI, the elderly non-musicians activated predominantly auditory regions, whereas the elderly musicians co-activated a much broader network of auditory association areas, primary and secondary motor areas, and prefrontal and parietal regions like, albeit weaker, the young musicians. Also, group-specific age- and musicianship-dependent functional correlations were observed in the frontal and parietal regions. Moreover, discriminant function analysis could separate groups with high accuracy based on a set of specific structural and functional, mainly temporal and occipital, ROIs.

Conclusion

In conclusion, despite naturally occurring senescence, the elderly musicians maintained musicianship-specific structural and functional cortical features. The identified structural and functional brain regions, discriminating elderly musicians from non-musicians, might be of relevance for the aging musicians’ brain. To what extent lifelong musical activity may have a neuroprotective impact needs to be addressed further in larger longitudinal studies.

Multimodal investigation of the association between shift work and the brain in a population-based sample of older adults

Neuropsychological studies reported that shift workers show reduced cognitive performance and circadian dysfunctions which may impact structural and functional brain networks. Here we tested the hypothesis whether night shift work is associated with resting-state functional connectivity (RSFC), cortical thickness and gray matter volume in participants of the 1000BRAINS study for whom information on night shift work and imaging data were available. 13 PRESENT and 89 FORMER night shift workers as well as 430 control participants who had never worked in shift (NEVER) met these criteria and were included in our study. No associations between night shift work, three graph-theoretical measures of RSFC of 7 functional brain networks and brain morphology were found after multiple comparison correction. Preceding multiple comparison correction, our results hinted at an association between more years of shift work and higher segregation of the visual network in PRESENT shift workers and between shift work experience and lower gray matter volume of the left thalamus. Extensive neuropsychological investigations supplementing objective imaging methodology did not reveal an association between night shift work and cognition after multiple comparison correction. Our pilot study suggests that night shift work does not elicit general alterations in brain networks and affects the brain only to a limited extent. These results now need to be corroborated in studies with larger numbers of participants.

Long-term air pollution, noise, and structural measures of the Default Mode Network in the brain: Results from the 1000BRAINS cohort

BACKGROUND

While evidence suggests that long-term air pollution (AP) and noise may adversely affect cognitive function, little is known about whether environmental exposures also promote structural changes in underlying brain networks. We therefore investigated the associations between AP, traffic noise, and structural measures of the Default Mode Network (DMN), a functional brain network known to undergo specific changes with age.

METHODS

We analyzed data from 579 participants (mean age at imaging: 66.5 years) of the German 1000BRAINS study. Long-term residential exposure to particulate matter (diameter ≤10 μm [PM₁₀]; diameter ≤2.5 μm [PM_2.5]), PM_2.5 absorbance (PM_2.5abs), nitrogen dioxide (NO₂), and accumulation mode particulate number concentration (PN_AM) was estimated using validated land use regression and chemistry transport models. Long-term outdoor traffic noise was modeled at participants' homes based on a European Union's Environmental Noise Directive. As measures of brain structure, cortical thickness and local gyrification index (lGI) values were calculated for DMN regions from T1-weighted structural brain images collected between 2011 and 2015. Associations between environmental exposures and brain structure measures were estimated using linear regression models, adjusting for demographic and lifestyle characteristics.

RESULTS

AP exposures were below European Union standards but above World Health Organization guidelines (e.g., PM₁₀ mean: 27.5 μg/m³). A third of participants experienced outdoor 24-h noise above European recommendations. Exposures were not consistently associated with lGI values in the DMN. We observed weak inverse associations between AP and cortical thickness in the right anterior DMN (e.g., -0.010 mm [-0.022, 0.002] per 0.3 unit increase in PM_2.5abs) and lateral part of the posterior DMN.

CONCLUSION

Long-term AP and noise were not consistently associated with structural parameters of the DMN in the brain. While weak associations were present between AP exposure and cortical thinning of right hemispheric DMN regions, it remains unclear whether AP might influence DMN brain structure in a similar way as aging.

Dynamic interplay of frontoparietal cholinergic innervation and cortical reorganization in the regulation of attentional capacities in aging

Cortical remodeling is linked to age-related cognitive changes in humans; however, the mechanisms underlying cortical reorganization in aging remain unknown. Here we examined the consequences of mild cholinergic thinning of the prefrontal cortex (PFC) and parietal cortex (PC) on attention performance-associated changes in cortical activity in young and aged rats. Prefrontal manipulation produced attentional deficits in aged but not young rats regardless of cholinergic pruning. Stereological assessment of c-fos expression revealed age-related reductions in occipital activity and a corresponding increase in PC activity, but these patterns did not correlate with performance. PC cholinergic deafferentation produced opposite changes in PFC recruitment between young and aged rats. Cholinergic pruning reversed the effects of PFC/PC cholinergic manipulations on the activity of CaMKII- and GAD-positive neurons in aged rats. Our results indicate that cortical shifts depend on multiple factors including chronological age, cholinergic changes, and cortical insult, and that cortical reorganization is not necessarily compensatory. Moreover, the cholinergic system modulates excitation/inhibition homeostasis to improve the efficiency of reorganized cortical circuits and stabilize attentional performance.

Brain Shape Changes Associated With Cerebral Atrophy in Healthy Aging and Alzheimer's Disease

Both healthy and pathological brain aging are characterized by various degrees of cognitive decline that strongly correlate with morphological changes referred to as cerebral atrophy. These hallmark morphological changes include cortical thinning, white and gray matter volume loss, ventricular enlargement, and loss of gyrification all caused by a myriad of subcellular and cellular aging processes. While the biology of brain aging has been investigated extensively, the mechanics of brain aging remains vastly understudied. Here, we propose a multiphysics model that couples tissue atrophy and Alzheimer's disease biomarker progression. We adopt the multiplicative split of the deformation gradient into a shrinking and an elastic part. We model atrophy as region-specific isotropic shrinking and differentiate between a constant, tissue-dependent atrophy rate in healthy aging, and an atrophy rate in Alzheimer's disease that is proportional to the local biomarker concentration. Our finite element modeling approach delivers a computational framework to systematically study the spatiotemporal progression of cerebral atrophy and its regional effect on brain shape. We verify our results via comparison with cross-sectional medical imaging studies that reveal persistent age-related atrophy patterns. Our long-term goal is to develop a diagnostic tool able to differentiate between healthy and accelerated aging, typically observed in Alzheimer's disease and related dementias, in order to allow for earlier and more effective interventions.

Geometric deep learning on brain shape predicts sex and age

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Hypothesis-driven surface-based approaches have been shown to be particularly efficient in their ability to accurately describe unique features of the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features aiming to capture the relevant geometric properties of cortical folding. In this paper, we propose an entirely novel, data-driven deep-learning based method to analyze the brain's shape that eliminates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses traditional convolutional neural network architecture uniquely adapted to the surface representation of the cortical ribbon. This method is a complete departure from prior brain MRI CNN investigations, all of which have relied on three dimensional MRI data and interpreted features of the MRI signal for prediction. MRI data from 6410 healthy subjects obtained from 11 publicly available data repositories were used for analysis. Ages ranged from 6 to 89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. For purposes of method development, both a classification and regression challenge were introduced for network learning including sex and age prediction, respectively. Two independent graph convolutional neural networks (gCNNs) were trained, the first of which to predict subject's self-identified sex, the second of which to predict subject's age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed respectively to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject's sex with an average accuracy of 87.99 % and achieved a Person's coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject's age. We believe this shape-based convolutional classifier offers a novel, data-driven approach to define biomedically relevant features from the brain at both the population and single subject levels and therefore lays a critical foundation for future precision medicine applications.

Increased Hippocampal Excitability and Altered Learning Dynamics Mediate Cognitive Mapping Deficits in Human Aging

Learning the spatial layout of a novel environment is associated with dynamic activity changes in the hippocampus and in medial parietal areas. With advancing age, the ability to learn spatial environments deteriorates substantially but the underlying neural mechanisms are not well understood. Here, we report findings from a behavioral and a fMRI experiment where healthy human older and younger adults of either sex performed a spatial learning task in a photorealistic virtual environment (VE). We modeled individual learning states using a Bayesian state-space model and found that activity in retrosplenial cortex (RSC)/parieto-occipital sulcus (POS) and anterior hippocampus did not change systematically as a function learning in older compared with younger adults across repeated episodes in the environment. Moreover, effective connectivity analyses revealed that the age-related learning deficits were linked to an increase in hippocampal excitability. Together, these results provide novel insights into how human aging affects computations in the brain's navigation system, highlighting the critical role of the hippocampus.SIGNIFICANCE STATEMENT Key structures of the brain's navigation circuit are particularly vulnerable to the deleterious consequences of aging, and declines in spatial navigation are among the earliest indicators for a progression from healthy aging to neurodegenerative diseases. Our study is among the first to provide a mechanistic account about how physiological changes in the aging brain affect the formation of spatial knowledge. We show that neural activity in the aging hippocampus and medial parietal areas is decoupled from individual learning states across repeated episodes in a novel spatial environment. Importantly, we find that increased excitability of the anterior hippocampus might constitute a potential neural mechanism for cognitive mapping deficits in old age.

Resting-state networks in the course of aging-differential insights from studies across the lifespan vs. amongst the old

Resting-state functional connectivity (RSFC) has widely been used to examine reorganization of functional brain networks during normal aging. The extraction of generalizable age trends, however, is hampered by differences in methodological approaches, study designs and sample characteristics. Distinct age ranges of study samples thereby represent an important aspect between studies especially due to the increase in inter-individual variability over the lifespan. The current review focuses on comparing age-related differences in RSFC in the course of the whole adult lifespan versus later decades of life. We summarize and compare studies assessing age-related differences in within- and between-network RSFC of major resting-state brain networks. Differential effects of the factor age on resting-state networks can be identified when comparing studies focusing on younger versus older adults with studies investigating effects within the older adult population. These differential effects pertain to higher order and primary processing resting-state networks to a varying extent. Especially during later decades of life, other factors beyond age might come into play to understand the high inter-individual variability in RSFC.

Differential Patterns of Gyral and Sulcal Morphological Changes During Normal Aging Process

The cerebral cortex is a highly convoluted structure with distinct morphologic features, namely the gyri and sulci, which are associated with the functional segregation or integration in the human brain. During the lifespan, the brain atrophy that is accompanied by cognitive decline is a well-accepted aging phenotype. However, the detailed patterns of cortical folding change during aging, especially the changing age-dependencies of gyri and sulci, which is essential to brain functioning, remain unclear. In this study, we investigated the morphology of the gyral and sulcal regions from pial and white matter surfaces using MR imaging data of 417 healthy participants across adulthood to old age (21–92 years). To elucidate the age-related changes in the cortical pattern, we fitted cortical thickness and intrinsic curvature of gyri and sulci using the quadratic model to evaluate their age-dependencies during normal aging. Our findings show that comparing to gyri, the sulcal thinning is the most prominent pattern during the aging process, and the gyrification of pial and white matter surfaces were also affected differently, which implies the vulnerability of functional segregation during aging. Taken together, we propose a morphological model of aging that may provide a framework for understanding the mechanisms underlying gray matter degeneration.

When your brain looks older than expected: combined lifestyle risk and BrainAGE

Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.

White Matter Microstructure Underlies the Effects of Sleep Quality and Life Stress on Depression Symptomatology in Older Adults

Sleep complaints are the most prevalent syndromes in older adults, particularly in women. Moreover, they are frequently accompanied with a high level of depression and stress. Although several diffusion tensor imaging (DTI) studies reported associations between sleep quality and brain white matter (WM) microstructure, it is still unclear whether gender impacts the effect of sleep quality on structural alterations, and whether these alterations mediate the effects of sleep quality on emotional regulation. We included 389 older participants (176 females, age = 65.5 ± 5.5 years) from the 1000BRAINS project. Neuropsychological examinations covered the assessments of sleep quality, depressive symptomatology, current stress level, visual working memory, and selective attention ability. Based on the DTI dataset, the diffusion parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were calculated and normalized to a population-specific FA template. According to the global Pittsburgh Sleep Quality Index (PSQI), 119 poor sleepers (PSQI: 10∼17) and 120 good sleepers (PSQI: 3∼6) were identified. We conducted a two by two (good sleepers/poor sleepers) × (males/females) analysis of variance by using tract-based spatial statistics (TBSS) and JHU-ICBM WM atlas-based comparisons. Moreover, we performed a voxel-wise correlation analysis of brain WM microstructure with the neuropsychological tests. Finally, we applied a mediation analysis to explore if the brain WM microstructure mediates the relationship between sleep quality and emotional regulation. No significant differences in brain WM microstructure were detected on the main effect of sleep quality. However, the MD, AD, and RD of pontine crossing tract and bilateral inferior cerebellar peduncle were significant lower in the males than females. Voxel-wise correlation analysis revealed that FA and RD values in the corpus callosum were positively related with depressive symptomatology and negatively related with current stress levels. Additionally, we found a significantly positive association between higher FA values in visual-related WM tracts and better outcomes in a visual pattern recognition test. Furthermore, a mediation analysis suggested that diffusion metrics within the corpus callosum partially mediated the associations between poor sleep quality/high stress and depressive symptomatology.

Functional brain connectivity changes across the human life span: From fetal development to old age

The dynamic of the temporal correlations between brain areas, called functional connectivity (FC), undergoes complex transformations through the life span. In this review, we aim to provide an overview of these changes in the nonpathological brain from fetal life to advanced age. After a brief description of the main methods, we propose that FC development can be divided into four main phases: first, before birth, a strong change in FC leads to the emergence of functional proto-networks, involving mainly within network short-range connections. Then, during the first years of life, there is a strong widespread organization of networks which starts with segregation processes followed by a continuous increase in integration. Thereafter, from adolescence to early adulthood, a refinement of existing networks in the brain occurs, characterized by an increase in integrative processes until about 40 years. Middle age constitutes a pivotal period associated with an inversion of the functional brain trajectories with a decrease in segregation process in conjunction to a large-scale reorganization of between network connections. Studies suggest that these processes are in line with the development of cognitive and sensory functions throughout life as well as their deterioration. During aging, results support the notion of dedifferentiation processes, which refer to the decrease in functional selectivity of the brain regions, resulting in more diffuse and less specialized FC, associated with the disruption of cognitive functions with age. The inversion of developmental processes during aging is in accordance with the developmental models of neuroanatomy for which the latest matured regions are the first to deteriorate.

Associations of Air Pollution and Noise with Local Brain Structure in a Cohort of Older Adults

BACKGROUND

Despite the importance of understanding associations of air pollution and noise exposure with loss of neurocognitive performance, studies investigating these exposures and local brain structure are limited.

OBJECTIVE

We estimated associations of residential air pollution and noise exposures with neurocognitive test performance and the local gyrification index (lGI), a marker for local brain atrophy, among older adults.

METHODS

For n=615 participants from the population-based 1000BRAINS study, based on the German Heinz Nixdorf Recall study, we assessed residential exposures to particulate matter (PM10, PM2.5, PM2.5abs), accumulation mode particle number (PNAM), and nitrogen oxides (NOx, NO2), using land-use regression and chemistry transport models. Weighted 24-h and nighttime noise were modeled according to the European noise directive. We evaluated associations of air pollution and noise exposure at the participants' 2006-2008 residential addresses with neurocognitive test performance and region-specific lGI values (n=590) from magnetic resonance imaging, both assessed in 2011-2015, using linear regression and adjusting for demographic and personal characteristics.

RESULTS

Air pollution and noise were associated with language and short-term/working memory and with local atrophy of the fronto-parietal network (FPN), a functional resting-state network associated with these cognitive processes. For example, per 2-μg/m3 PM10, local brain atrophy was more pronounced in the posterior brain regions of the FPN, with a -0.02 [95% confidence interval (CI): -0.04, 0.00] lower lGI. In contrast, in the anterior regions of the FPN, weighted 24-h and nighttime noise were associated with less local brain atrophy [e.g., 0.02 (95% CI: 0.00, 0.04) for 10 dB(A) 24-h noise].

CONCLUSIONS

Air pollution and noise exposures were associated in opposite directions with markers of local atrophy of the FPN in the right brain hemisphere in older adults, suggesting that both chronic air pollution and noise exposure may influence the physiological aging process of the brain. https://doi.org/10.1289/EHP5859.

Longitudinal Functional Brain Mapping in Supernormals

Prevention of age-related cognitive decline is an increasingly important topic. Recently, increased attention is being directed at understanding biological models of successful cognitive aging. Here, we examined resting-state brain regional low-frequency oscillations using functional magnetic resonance imaging in 19 older adults with excellent cognitive abilities (Supernormals), 28 older adults with normative cognition, 57 older adults with amnestic mild cognitive impairment, and 26 with Alzheimer's disease. We identified a "Supernormal map", a set of regions whose oscillations were resistant to the aging-associated neurodegenerative process, including the right fusiform gyrus, right middle frontal gyrus, right anterior cingulate cortex, left middle temporal gyrus, left precentral gyrus, and left orbitofrontal cortex. The map was unique to the Supernormals, differentiated this group from cognitive average-ager comparisons, and predicted a 1-year change in global cognition (indexed by the Montreal Cognitive Assessment scores, adjusted R2 = 0.68). The map was also correlated to Alzheimer's pathophysiological features (beta-amyloid/pTau ratio, adjusted R2 = 0.66) in participants with and without cognitive impairment. These findings in phenotypically successful cognitive agers suggest a divergent pattern of brain regions that may either reflect inherent neural integrity that contributes to Supernormals' cognitive success, or alternatively indicate adaptive reorganization to the demands of aging.

The relationship between bilingual experience and gyrification in adulthood: A cross-sectional surface-based morphometry study

Neuroimaging evidence suggests that bilingualism may act as a source of neural plasticity. However, prior work has mostly focused on bilingualism-induced alterations in gray matter volume and white matter tract microstructure, with additional effects related to other neurostructural indices that might have remained undetected. The degree of cortical folding or gyrification is a morphometric parameter which provides information about changes on the brain's surface during development, aging and disease. We used Surface-based Morphometry (SBM) to investigate the contribution of bilingual experience to gyrification from early adulthood to old age in a sample of bilinguals and monolingual controls. Despite widespread cortical folding reductions for all participants with increasing age, preserved gyrification exclusive to bilinguals was detected in the right cingulate and entorhinal cortices, regions vulnerable with normal and pathological brain aging. Our results provide novel insights on experience-related cortical reshaping and bilingualism-induced cortical plasticity in adulthood.

Age-Related Decline of Low-Spatial-Frequency Bias in Context-Dependent Visual Size Perception

Global precedence has been found to decline or even shift to local precedence with increasing age. Little is known about the consequence of this age-related decline of global precedence on other aspects of older adults’ vision. The global and local processing has been preferentially associated with the low-spatial-frequency (LSF) and high-spatial-frequency (HSF) channels, respectively. Here, we used low- and high-pass filtered faces together with the Ebbinghaus illusion whose magnitude is an index of context sensitivity. The results demonstrated that, relative to HSF faces, prior exposure to LSF faces increased the illusion magnitude for younger participants, but it reduced the illusion magnitude for older participants. Significant age group difference was observed only with prior exposure to LSF faces but not to HSF faces. Moreover, similar patterns of results were observed when the filtered faces were rendered invisible with backward masking, and the magnitude of age-related decline was comparable to the visible condition. Our study reveals that LSF-related enhancement of context sensitivity declines with advancing age, and this age-related decline was independent of the awareness of the spatial frequency information. Our findings support the right hemi-aging model and suggest that the magnocellular projections from subcortical to cortical regions might also be vulnerable to age-related changes.

Morphometric correlates of anomia in patients with small left temporopolar lesions

Visual object naming is a complex cognitive process that engages an interconnected network of cortical regions moving from occipitotemporal to anterior-inferior temporal cortices, and extending into the inferior frontal cortex. Naming can fail for diverse reasons, and different stages of the naming multi-step process appear to be reliant upon the integrity of different neuroanatomical locations. While the neural correlates of semantic errors have been extensively studied, the neural basis of omission errors remains relatively unspecified. Although a strong line of evidence supports an association between anterior temporal lobe damage and semantic errors, there are some studies suggesting that the anterior temporal lobe could be also associated with omissions. However, support for this hypothesis comes from studies with patients in whom damage affected extensive brain regions, sometimes bilaterally. Here, we availed of a group of 12 patients with epilepsy associated with a small lesion at the tip of the left temporal pole. Using an unbiased surface-based morphometry methodology, we correlated two morphological features with errors observed during visual naming. Analyses revealed a correlation between omission errors and reduced local gyrification index in three cortical clusters: one in the left anteromedial temporal lobe region (AMTL) and two in the left anterior cingulate cortex (ACC). Our findings support the view that regions in ACC and AMTL are critical structures within a network engaged in word selection from semantics.

Retinal Nerve Fiber Layer Thinning Is Associated With Brain Atrophy: A Longitudinal Study in Nondemented Older Adults

Backgrounds: Abnormal retinal nerve fiber layer (RNFL) thickness has been observed in patients with Alzheimer’s disease (AD) and therefore suggested to be a potential biomarker of AD. However, whether the changes in RNFL thickness are associated with the atrophy of brain structure volumes remains unknown. We, therefore, set out a prospective investigation to determine the association between longitudinal changes of RNFL thickness and brain atrophy in nondemented older participants over a period of 12 months.

Materials and Methods: We measured the RNFL thickness using optical coherence tomography (OCT) and brain structure volumes by 3T magnetic resonance imaging (MRI) before and after 12 months. Cognitive function was assessed using the Chinese version of Mini-Mental State Examination (CMMSE) and Repeatable Battery for the Assessment of Neurological Status. Associations among the changes of RNFL, brain structures and cognitive function were analyzed with Spearman correlation and multiple linear regression models adjusting for the confounding factors.

Results: Fifty old participants were screened and 40 participants (mean age 71.8 ± 3.9 years, 60% were male) were enrolled at baseline. Among them, 28 participants completed the follow-up assessments. The average reduction of RNFL thickness was inversely associated with the decrease of central cingulate cortex volume after the adjustment of age and total intracranial volume (β = −0.41, P = 0.039). Specifically, the reduction of RNFL thickness in the inferior, not other quadrants, was independently associated with the decline of central cingulate cortex volume after the adjustment (β = −0.52, P = 0.006). Moreover, RNFL thinning, central cingulate cortex atrophy and the aggregation of white matter hyperintensities (WMH) were found associated with episodic memory in these older adults with normal cognition.

Conclusions: RNFL thinning was associated with cingulate cortex atrophy and episodic memory decline in old participants. The longitudinal changes of RNFL thickness are suggested to be a useful complementary index of neurocognitive aging or neurodegeneration.

A study of within-subject reliability of the brain's default-mode network

Objective

Resting-state functional magnetic resonance imaging (fMRI) is promising for Alzheimer’s disease (AD). This study aimed to examine short-term reliability of the default-mode network (DMN), one of the main haemodynamic patterns of the brain.

Materials and methods

Using a 1.5 T Philips Achieva scanner, two consecutive resting-state fMRI runs were acquired on 69 healthy adults, 62 patients with mild cognitive impairment (MCI) due to AD, and 28 patients with AD dementia. The anterior and posterior DMN and, as control, the visual-processing network (VPN) were computed using two different methodologies: connectivity of predetermined seeds (theory-driven) and dual regression (data-driven). Divergence and convergence in network strength and topography were calculated with paired t tests, global correlation coefficients, voxel-based correlation maps, and indices of reliability.

Results

No topographical differences were found in any of the networks. High correlations and reliability were found in the posterior DMN of healthy adults and MCI patients. Lower reliability was found in the anterior DMN and in the VPN, and in the posterior DMN of dementia patients.

Discussion

Strength and topography of the posterior DMN appear relatively stable and reliable over a short-term period of acquisition but with some degree of variability across clinical samples.

Combining lifestyle risks to disentangle brain structure and functional connectivity differences in older adults

Lifestyle contributes to inter-individual variability in brain aging, but previous studies focused on the effects of single lifestyle variables. Here, we studied the combined and individual contributions of four lifestyle variables - alcohol consumption, smoking, physical activity, and social integration - to brain structure and functional connectivity in a population-based cohort of 549 older adults. A combined lifestyle risk score was associated with decreased gyrification in left premotor and right prefrontal cortex, and higher functional connectivity to sensorimotor and prefrontal cortex. While structural differences were driven by alcohol consumption, physical activity, and social integration, higher functional connectivity was driven by smoking. Results suggest that combining differentially contributing lifestyle variables may be more than the sum of its parts. Associations generally were neither altered by adjustment for genetic risk, nor by depressive symptomatology or education, underlining the relevance of daily habits for brain health.

Lifestyle factors such as smoking and exercise contribute to the health of the brain during aging, but previous studies have focused on the effects of single lifestyle variables. Here, the authors examine the combined and individual effects of four lifestyle variables on brain structure and function.

Generalizing age effects on brain structure and cognition: A two-study comparison approach

Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross-validating age-related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well-being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age-related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two-study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age-related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used.

Effect of Ibuprofen on BrainAGE: A Randomized, Placebo-Controlled, Dose-Response Exploratory Study

BACKGROUND

The age of a person's brain can be estimated from structural brain images using an aggregate measure of variation in morphology across the whole brain. The brain age gap estimation (BrainAGE) score is computed as the difference between kernel-estimated brain age and chronological age. In this exploratory study, we investigated the application of the BrainAGE measure to identify potential novel effects of pharmacological agents on brain morphology.

METHODS

Twenty healthy participants (23-47 years of age) completed three structural magnetic resonance imaging scans 45 minutes after administration of placebo or 200 or 600 mg of ibuprofen in a double-blind, crossover study. An externally derived BrainAGE model from a sample of 480 healthy participants was used to examine the acute effect of ibuprofen on temporary neuroanatomical changes in healthy individuals.

RESULTS

The BrainAGE model produced age prediction for each participant with a mean absolute error of 6.7 years between the estimated and chronological age. The intraclass correlation coefficient for BrainAGE was 0.96. Relative to placebo, 200 and 600 mg of ibuprofen significantly decreased BrainAGE by 1.18 and 1.15 years, respectively (p < .05). The trained BrainAGE model identified the medial prefrontal cortex to be the strongest age predictor.

CONCLUSIONS

BrainAGE is a potentially useful construct to examine neurological effects of therapeutic drugs. Ibuprofen temporarily reduces BrainAGE by approximately 1 year, which is likely due to its acute anti-inflammatory effects.

Network-based decoupling of local gyrification in obsessive-compulsive disorder

Gyrification is associated with cortical maturation and closely linked to neurodevelopmental processes. Obsessive-compulsive disorder has previously been associated with neurodevelopmental risk factors. Using graph theoretical modeling we examined structural covariance patterns to assess potential disruptions in processes associated with neurodevelopment in OCD. In total 97 patients and 92 healthy controls underwent magnetic resonance imaging. Structural covariance networks based on local gyrification indices were constructed using an atlas-based parcellation scheme. Network properties were assessed using the network-based statistic as well as global and local graph theoretical measures. Correlations between gyrification and symptom severity as well as age of disease onset were examined. Network-based statistic analysis revealed one cluster with significantly decreased structural covariance in patients comprising mainly ventral brain regions (p = .041). Normalized characteristic path length was found to be impaired in patients (p = .051). On a nodal level, left middle frontal sulcus displayed a significantly decreased local clustering coefficient (p < .001). Finally, gyrification in several inferior frontal nodes significantly correlated with age of onset but not symptom severity. The decrease in a gyrification-based covariance network in OCD appears to be mostly confined to ventral areas in which gyrification starts the latest during development. This pattern may indicate that alterations taking place during development are potentially time locked to specific periods. Correlations between gyrification in inferio-frontal nodes and age of onset potentially indicate a structural trait rather than state marker for OCD. Finally, a trend in impaired global integration capabilities may point towards potentially widespread global alterations during neurodevelopment in patients.

Frontoparietal tDCS Benefits Visual Working Memory in Older Adults With Low Working Memory Capacity

Working memory (WM) permits maintenance of information over brief delays and is an essential executive function. Unfortunately, WM is subject to age-related decline. Some evidence supports the use of transcranial direct current stimulation (tDCS) to improve visual WM. A gap in knowledge is an understanding of the mechanism characterizing these tDCS linked effects. To address this gap, we compared the effects of two tDCS montages designed on visual working memory (VWM) performance. The bifrontal montage was designed to stimulate the heightened bilateral frontal activity observed in aging adults. The unilateral frontoparietal montage was designed to stimulate activation patterns observed in young adults. Participants completed three sessions (bilateral frontal, right frontoparietal, sham) of anodal tDCS (20 min, 2 mA). During stimulation, participants performed a visual long-term memory (LTM) control task and a visual WM task. There was no effect of tDCS on the LTM task. Participants receiving right unilateral tDCS showed a WM benefit. This pattern was most robust in older adults with low WM capacity. To address the concern that the key difference between the two tDCS montages could be tDCS over the posterior parietal cortex (PPC), we included new analyses from a previous study applying tDCS targeting the PPC paired with a recognition VWM task. No significant main effects were found. A subsequent experiment in young adults found no significant effect of either tDCS montage on either task. These data indicate that tDCS montage, age and WM capacity should be considered when designing tDCS protocols. We interpret these findings as suggestive that protocols designed to restore more youthful patterns of brain activity are superior to those that compensate for age-related changes.

Predicting age from cortical structure across the lifespan

Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.

A Complex Interplay of Vitamin B1 and B6 Metabolism with Cognition, Brain Structure, and Functional Connectivity in Older Adults

Aging is associated with brain atrophy, functional brain network reorganization and decline of cognitive performance, albeit characterized by high interindividual variability. Among environmental influencing factors accounting for this variability, nutrition and particularly vitamin supply is thought to play an important role. While evidence exists that supplementation of vitamins B6 and B1 might be beneficial for cognition and brain structure, at least in deficient states and neurodegenerative diseases, little is known about this relation during healthy aging and in relation to reorganization of functional brain networks. We thus assessed the relation between blood levels of vitamins B1 and B6 and cognitive performance, cortical folding, and functional resting-state connectivity in a large sample of older adults (N > 600; age: 55-85 years), drawn from the population-based 1000BRAINS study. In addition to blood sampling, subjects underwent structural and functional resting-state neuroimaging as well as extensive neuropsychological testing in the domains of executive functions, (working) memory, attention, and language. Brain regions showing changes in the local gyrification index as calculated using FreeSurfer in relation to vitamin levels were used for subsequent seed-based resting-state functional connectivity analysis. For B6, a positive correlation with local cortical folding was found throughout the brain, while only slight changes in functional connectivity were observed. Contrarily, for B1, a negative correlation with cortical folding as well as problem solving and visuo-spatial working memory performance was found, which was accompanied by pronounced increases of interhemispheric and decreases of intrahemispheric functional connectivity. While the effects for B6 expand previous knowledge on beneficial effects of B6 supplementation on brain structure, they also showed that additional effects on cognition might not be recognizable in healthy older subjects with normal B6 blood levels. The cortical atrophy and pronounced functional reorganization associated with B1, contrarily, was more in line with the theory of a disturbed B1 metabolism in older adults, leading to B1 utilization deficits, and thus, an effective B1 deficiency in the brain, despite normal to high-normal blood levels.

Transcriptomic Perspectives on Neocortical Structure, Development, Evolution, and Disease

The cerebral cortex is the source of our most complex cognitive capabilities and a vulnerable target of many neurological and neuropsychiatric disorders. Transcriptomics offers a new approach to understanding the cortex at the level of its underlying genetic code, and rapid technological advances have propelled this field to the high-throughput study of the complete set of transcribed genes at increasingly fine resolution to the level of individual cells. These tools have revealed features of the genetic architecture of adult cortical areas, layers, and cell types, as well as spatiotemporal patterning during development. This has allowed a fresh look at comparative anatomy as well, illustrating surprisingly large differences between mammals while at the same time revealing conservation of some features from avians to mammals. Finally, transcriptomics is fueling progress in understanding the causes of neurodevelopmental diseases such as autism, linking genetic association studies to specific molecular pathways and affected brain regions.

A posterior-to-anterior shift of brain functional dynamics in aging

Convergent evidence from task-based functional magnetic resonance imaging (fMRI) studies suggests a posterior-to-anterior shift as an adaptive compensatory scaffolding mechanism for aging. This study aimed to investigate whether brain functional dynamics at rest follow the same scaffolding mechanism for aging using a large Chinese sample aged from 22 to 79 years (n = 277). We defined a probability of brain regions being hubs over a period of time to characterize functional hub dynamic, and defined variability of the functional connectivity to characterize dynamic functional connectivity using resting-state fMRI. Our results revealed that both functional hub dynamics and dynamic functional connectivity posited an age-related posterior-to-anterior shift. Specifically, the posterior brain region showed attenuated dynamics, while the anterior brain regions showed augmented dynamics in aging. Interestingly, our analysis further indicated that the age-related episodic memory decline was associated with the age-related decrease in the brain functional dynamics of the posterior regions. Hence, these findings provided a new dimension to view the scaffolding mechanism for aging based on the brain functional dynamics.

Lifespan Gyrification Trajectories of Human Brain in Healthy Individuals and Patients with Major Psychiatric Disorders

Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.

Distinct Aging Effects on Functional Networks in Good and Poor Cognitive Performers

Brain network hubs are susceptible to normal aging processes and disruptions of their functional connectivity are detrimental to decline in cognitive functions in older adults. However, it remains unclear how the functional connectivity of network hubs cope with cognitive heterogeneity in an aging population. This study utilized cognitive and resting-state functional magnetic resonance imaging data, cluster analysis, and graph network analysis to examine age-related alterations in the network hubs’ functional connectivity of good and poor cognitive performers. Our results revealed that poor cognitive performers showed age-dependent disruptions in the functional connectivity of the right insula and posterior cingulate cortex (PCC), while good cognitive performers showed age-related disruptions in the functional connectivity of the left insula and PCC. Additionally, the left PCC had age-related declines in the functional connectivity with the left medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). Most interestingly, good cognitive performers showed age-related declines in the functional connectivity of the left insula and PCC with their right homotopic structures. These results may provide insights of neuronal correlates for understanding individual differences in aging. In particular, our study suggests prominent protection roles of the left insula and PCC and bilateral ACC in good performers.