Age trajectories of functional activation under conditions of low and high processing demands: an adult lifespan fMRI study of the aging brain

Brain age prediction via cross-stratified ensemble learning

As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R2) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.

Age differences in BOLD modulation to task difficulty as a function of amyloid burden

Effective cognitive performance often requires the allocation of additional neural resources (i.e. blood-oxygen-level-dependent [BOLD] activation) as task demands increase, and this demand-related modulation is affected by amyloid-beta deposition and normal aging. The present study investigated these complex relationships between amyloid, modulation, and cognitive function (i.e. fluid ability). Participants from the Dallas Lifespan Brain Study (DLBS, n = 252, ages 50-89) completed a semantic judgment task during functional magnetic resonance imaging (fMRI) where the judgments differed in classification difficulty. Amyloid burden was assessed via positron emission tomography (PET) using 18F-florbetapir. A quadratic relationship between amyloid standardized value uptake ratios (SUVRs) and BOLD modulation was observed such that modulation was weaker in those with moderately elevated SUVRs (e.g. just reaching amyloid-positivity), whereas those with very high SUVRs (e.g. SUVR > 1.5) showed strong modulation. Greater modulation was related to better fluid ability, and this relationship was strongest in younger participants and those with lower amyloid burden. These results support the theory that effective demand-related modulation contributes to healthy cognitive aging, especially in the transition from middle age to older adulthood, whereas high modulation may be dysfunctional in those with substantial amyloid deposition.

BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis

Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.

Registered report: Age-preserved semantic memory and the CRUNCH effect manifested as differential semantic control networks: An fMRI study

Semantic memory representations are generally well maintained in aging, whereas semantic control is thought to be more affected. To explain this phenomenon, this study tested the predictions of the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH), focusing on task demands in aging as a possible framework. The CRUNCH effect would manifest itself in semantic tasks through a compensatory increase in neural activation in semantic control network regions but only up to a certain threshold of task demands. This study compares 39 younger (20-35 years old) with 39 older participants (60-75 years old) in a triad-based semantic judgment task performed in an fMRI scanner while manipulating task demand levels (low versus high) through semantic distance. In line with the CRUNCH predictions, differences in neurofunctional activation and behavioral performance (accuracy and response times) were expected in younger versus older participants in the low- versus high-demand conditions, which should be manifested in semantic control Regions of Interest (ROIs). Our older participants had intact behavioral performance, as proposed in the literature for semantic memory tasks (maintained accuracy and slower response times (RTs)). Age-invariant behavioral performance in the older group compared to the younger one is necessary to test the CRUNCH predictions. The older adults were also characterized by high cognitive reserve, as our neuropsychological tests showed. Our behavioral results confirmed that our task successfully manipulated task demands: error rates, RTs and perceived difficulty increased with increasing task demands in both age groups. We did not find an interaction between age group and task demand, or a statistically significant difference in activation between the low- and high-demand conditions for either RTs or accuracy. As for brain activation, we did not find the expected age group by task demand interaction, or a significant main effect of task demand. Overall, our results are compatible with some neural activation in the semantic network and the semantic control network, largely in frontotemporoparietal regions. ROI analyses demonstrated significant effects (but no interactions) of task demand in the left and right inferior frontal gyrus, the left posterior middle temporal gyrus, the posterior inferior temporal gyrus and the prefrontal gyrus. Overall, our test did not confirm the CRUNCH predictions.

Cortical processes of multisensory plausibility modulation of vibrotactile perception in virtual environments in middled-aged and older adults

Digital technologies, such as virtual or augmented reality, can potentially support neurocognitive functions of the aging populations worldwide and complement existing intervention methods. However, aging-related declines in the frontal-parietal network and dopaminergic modulation which progress gradually across the later periods of the adult lifespan may affect the processing of multisensory congruence and expectancy based contextual plausibility. We assessed hemodynamic brain responses while middle-aged and old adults experienced car-riding virtual-reality scenarios where the plausibility of vibrotactile stimulations was manipulated by delivering stimulus intensities that were either congruent or incongruent with the digitalized audio-visual contexts of the respective scenarios. Relative to previous findings observed in young adults, although highly plausible vibrotactile stimulations confirming with contextual expectations also elicited higher brain hemodynamic responses in middle-aged and old adults, this effect was limited to virtual scenarios with extreme expectancy violations. Moreover, individual differences in plausibility-related frontal activity did not correlate with plausibility violation costs in the sensorimotor cortex, indicating less systematic frontal context-based sensory filtering in older ages. These findings have practical implications for advancing digital technologies to support aging societies.

Semantic processing in older adults is associated with distributed neural activation which varies by association and abstractness of words

The extent to which the neural systems underlying semantic processes degrade with advanced age remains unresolved, which motivated the current study of neural activation on functional magnetic resonance imaging (fMRI) during semantic judgments of associated vs. unassociated, semantic vs. rhyme, and abstract vs. rhyme word pairs. Thirty-eight older adults, 55-85 years of age, performed semantic association decision tasks in a mixed event-related block fMRI paradigm involving binary judgments as to whether word pairs were related (i.e., semantically associated). As hypothesized, significantly greater activation was evident during processing of associated (vs. unassociated) word pairs in cortical areas implicated in semantic processing, including the angular gyrus, temporal cortex, and inferior frontal cortex. Cortical areas showed greater activation to unassociated (vs. associated) word pairs, primarily within a large occipital cluster. Greater activation was evident in cortical areas when response to semantic vs. phonemic word pairs. Contrasting activation during abstract vs. concrete semantic processing revealed areas of co-activation to both semantic classes, and areas that had greater response to either abstract or concrete word pairs. Neural activation across conditions did not vary as a function of greater age, indicating only minimal age-associated perturbation in neural activation during semantic processing. Therefore, the response of the semantic hubs, semantic control, and secondary association areas appear to be largely preserved with advanced age among older adults exhibiting successful cognitive aging. These findings may provide a useful clinical contrast if compared to activation among adults experiencing cognitive decline due Alzheimer's, frontal-temporal dementia, and other neurodegenerative diseases.

Neural manifestation of L2 novel concept acquisition from multi-contexts via both episodic memory and semantic memory systems

This study aims to examine the process of L2 novel word learning through the combination of episodic and semantic memory, and how the process differs between the formation of thematic and taxonomic relations. The major approach adopted was observing the neural effects of word learning, which is manifested in the N400 from event-related potentials (ERPs). Eighty-eight participants were recruited for the experiment. In the learning session, L2 contextual discourses related to novel words were learned by participants. In the testing session, discourses embedded with incongruous and congruous novel words in the final position were used for participants to judge the congruency which affected the N400 neural activity. The results showed that both recurrent and new-theme discourses elicited significant N400 effects, while taxonomic sentences did not. These results confirmed the formation of episodic and semantic memory during L2 new word learning, in which semantic memory was mainly supported by thematic relations.

Multilingual Language Diversity Protects Native Language Production under Different Control Demands

The use of multiple languages has been found to influence individuals' cognitive abilities. Although some studies have also investigated the effect of multilingualism on non-native language proficiency, fewer studies have focused on how multilingual experience affects native language production. This study investigated the effect of multilingualism on native language production, specifically examining control demands through a semantic Go/No-Go picture naming task. The multilingual experience was quantified using language entropy, which measures the uncertainty and diversity of language use. Control demands were achieved by manipulating the proportion of Go (i.e., naming) trials in different conditions. Results showed that as control demands increased, multilingual individuals exhibited poorer behavioral performance and greater brain activation throughout the brain. Moreover, more diverse language use was associated with higher accuracy in naming and more interconnected brain networks with greater involvement of domain-general neural resources and less domain-specific neural resources. Notably, the varied and balanced use of multiple languages enabled multilingual individuals to respond more efficiently to increased task demands during native language production.

Investigating the effects of the aging brain on real tool use performance-an fMRI study

Introduction

Healthy aging affects several domains of cognitive and motor performance and is further associated with multiple structural and functional neural reorganization patterns. However, gap of knowledge exists, referring to the impact of these age-related alterations on the neural basis of tool use-an important, complex action involved in everyday life throughout the entire lifespan. The current fMRI study aims to investigate age-related changes of neural correlates involved in planning and executing a complex object manipulation task, further providing a better understanding of impaired tool use performance in apraxia patients.

Methods

A balanced number of sixteen older and younger healthy adults repeatedly manipulated everyday tools in an event-related Go-No-Go fMRI paradigm.

Results

Our data indicates that the left-lateralized network, including widely distributed frontal, temporal, parietal and occipital regions, involved in tool use performance is not subjected to age-related functional reorganization processes. However, age-related changes regarding the applied strategical procedure can be detected, indicating stronger investment into the planning, preparatory phase of such an action in older participants.

Relationship of prefrontal brain lateralization to optimal cognitive function differs with age

There is considerable debate about whether additional fMRI-measured activity in the right prefrontal cortex readily observed in older adults represents compensatory activation that enhances cognition or whether maintenance of youthful brain activity best supports cognitive function in late adulthood. To investigate this issue, we tested a large lifespan sample of 461 adults (aged 20-89) and treated degree of left-lateralization in ventrolateral and dorsolateral prefrontal cortex during a semantic judgment fMRI task as an individual differences variable to predict cognition. We found that younger adults were highly left-lateralized, but lateralization did not predict better cognition, whereas higher left-lateralization of prefrontal cortex predicted better cognitive performance in middle-aged adults, providing evidence that left-lateralized, youth-like patterns are optimal in middle age. This relationship was reversed in older adults, with lower laterality scores associated with better cognition. The findings suggest that bilaterality in older adults facilitates cognition, but early manifestation of this pattern during middle age is characteristic of low performers. Implications of these findings for current theories of neurocognitive aging are discussed.

Egocentric distance perception in older adults: Results from a functional magnetic resonance imaging and driving simulator study

The ability to appropriately perceive distances in activities of daily living, such as driving, is necessary when performing complex maneuvers. With aging, certain driving behaviors and cognitive functions change; however, it remains unknown if egocentric distance perception (EDP) performance is altered and whether its neural activity also changes as we grow older. To that end, 19 young and 17 older healthy adults drove in a driving simulator and performed an functional magnetic resonance imaging (fMRI) experiment where we presented adults with an EDP task. We discovered that (a) EDP task performance was similar between groups, with higher response times in older adults; (b) older adults showed higher prefrontal and parietal activation; and (c) higher functional connectivity within frontal and parietal-occipital-cerebellar networks; and (d) an association between EDP performance and hard braking behaviors in the driving simulator was found. In conclusion, EDP functioning remains largely intact with aging, possibly due to an extended and effective rearrangement in functional brain resources, and may play a role in braking behaviors while driving.

Time course of right-hemisphere recruitment during word production following left-hemisphere damage: A single case of young stroke

Our understanding of post-stroke language function is largely based on older age groups, who show increasing age-related brain pathology and neural reorganisation. To illustrate language outcomes in the young-adult brain, we present the case of J., a 23-year-old woman with chronic aphasia from a left-hemisphere stroke affecting the temporal lobe. Diffusion MRI-based tractography indicated that J.'s language-relevant white-matter structures were severely damaged. Employing magnetoencephalography (MEG), we explored J.'s conceptual preparation and word planning abilities using context-driven and bare picture-naming tasks. These revealed naming deficits, manifesting as word-finding difficulties and semantic paraphasias about half of the time. Naming was however facilitated by semantically constraining lead-in sentences. Altogether, this pattern indicates disrupted lexical-semantic and phonological retrieval abilities. MEG revealed that J.'s conceptual and naming-related neural responses were supported by the right hemisphere, compared to the typical left-lateralised brain response of a matched control. Differential recruitment of right-hemisphere structures (330-440 ms post-picture onset) was found concurrently during successful naming (right mid-to-posterior temporal lobe) and word-finding attempts (right inferior frontal gyrus). Disconnection of the temporal lobes via corpus callosum was not critical for recruitment of the right hemisphere in visually guided naming, possibly due to neural activity right lateralising from the outset. Although J.'s right hemisphere responded in a timely manner during word planning, its lexical and phonological retrieval abilities remained modest.

Lifespan developmental changes in neural substrates and functional connectivity for visual semantic processing

Human learning and cognitive functions change with age and experience, with late-developed complex cognitive functions, particularly those served by the prefrontal cortex, showing more age-dependent variance. Reading as a complex process of constructing meaning from print uses the left prefrontal cortex and may show a similar aging pattern. In this study, we delineated the lifespan developmental changes in the neural substrates and functional connectivity for visual semantic processing from childhood (age 6) to late adulthood (age 74). Different from previous studies that reported aging as a form of activation or neuronal changes, we examined additionally how the functional connectivity networks changed with age. A cohort of 122 Chinese participants performed semantic and font-size judgment tasks during functional magnetic resonance imaging. Although a common left-lateralized neural system including the left mid-inferior prefrontal cortex was recruited across all participants, the effect of age, or reading experience, is evident as 2 contrastive developmental patterns: a declining trend in activation strength and extent and an increasing trend in functional connections of the network. This study suggests that visual semantic processing is not prone to cognitive decline, and that continuous reading until old age helps strengthen the functional connections of reading-related brain regions.

Don't forget the little brain: A framework for incorporating the cerebellum into the understanding of cognitive aging

With the rapidly growing population of older adults, an improved understanding of brain and cognitive aging is critical, given the impacts on health, independence, and quality of life. To this point, we have a well-developed literature on the cortical contributions to cognition in advanced age. However, while this work has been foundational for our understanding of brain and behavior in older adults, subcortical contributions, particularly those from the cerebellum, have not been integrated into these models and frameworks. Incorporating the cerebellum into models of cognitive aging is an important step for moving the field forward. There has also been recent interest in this structure in Alzheimer's dementia, indicating that such work may be beneficial to our understanding of neurodegenerative disease. Here, I provide an updated overview of the cerebellum in advanced age and propose that it serves as a critical source of scaffolding or reserve for cortical function. Age-related impacts on cerebellar function further impact cortical processing, perhaps resulting in many of the activation patterns commonly seen in aging.

Functional activation features of memory in successful agers across the adult lifespan

Much neuroimaging research has explored the neural mechanisms underlying successful cognitive aging. Two different patterns of functional activation, maintenance of youth-like activity and compensatory novel recruitment, have been proposed to represent different brain functional features underlying individual differences in cognitive aging. In this study, we investigated the functional features in individuals across the adult lifespan who appeared to resist age-related cognitive decline, in comparison to those with typical age-related declines, over the course of four years. We first implemented latent mixture modeling, a data-driven approach, to classify participants as successful and average agers in middle-aged, young-old, and very old groups, based on their baseline and longitudinal cognitive performance. Then, using fMRI with a subsequent memory paradigm at the follow-up visit, brain activation specifically related to successful encoding (i.e., subsequent memory effect: subsequently remembered with high confidence > subsequently forgotten) was compared between people who established successful cognitive aging versus average aging in the three age groups. Several differences in the subsequent memory effect were revealed. First, across core task-related regions commonly used during successful encoding, successful agers exhibited high subsequent memory effect, at a level comparable to the young control group, until very old age; in contrast, average agers showed reduced subsequent memory effect, compared to successful agers, beginning in young-old age when memory performance also reduced in average agers, compared to successful agers. Second, additional recruitment in prefrontal clusters, distant from the core task-related regions, were identified in the left superior frontal and right orbitofrontal cortices in successful agers of young-old age, possibly reflecting functional compensation in successful aging. In summary, successful agers demonstrate a pattern of youth-like activation spanning from middle age to young-old age, as well as novel frontal recruitment in young-old age. Overall, our study demonstrated evidence of two neural patterns related to successful cognitive aging, offering an integrated view of functional features underlying successful aging, and suggests the importance of studying individuals across the lifespan to understand brain changes occurring in mid and early-late life.

Prediction in the aging brain: Merging cognitive, neurological, and evolutionary perspectives

Although the aging brain is typically characterized by declines in a variety of cognitive functions, there has been growing attention to cognitive functions that may stabilize or improve with age. We integrate evidence from behavioral, computational, and neurological domains under the hypothesis that over the life span the brain becomes more effective at predicting (i.e., utilizing knowledge) compared to learning. Moving beyond mere description of the empirical literature—with the aim of arriving at a deeper understanding of cognitive aging—we provide potential explanations for a learning-to-prediction shift based on evolutionary models and principles of senescence and plasticity. The proposed explanations explore whether the occurrence of a learning-to-prediction shift can be explained by (changes in) the fitness effects of learning and prediction over the life span. Prediction may optimize (a) the allocation of limited resources across the life span, and/or (b) late-life knowledge transfer (social learning). Alternatively, late-life prediction may reflect a slower decline in prediction compared to learning. By discussing these hypotheses, we aim to provide a foundation for an integrative neurocognitive–evolutionary perspective on aging and to stimulate further theoretical and empirical work.

Bridging patterns of neurocognitive aging across the older adult lifespan

Magnetic resonance imaging (MRI) studies of brain and neurocognitive aging rarely include oldest-old adults (ages 80 +). But predictions of neurocognitive aging theories derived from MRI findings in younger-old adults (ages ~55-80) may not generalize into advanced age, particularly given the increased prevalence of cognitive impairment/dementia in the oldest-old. Here, we reviewed the MRI literature in oldest-old adults and interpreted findings within the context of regional variation, compensation, brain maintenance, and reserve theories. Structural MRI studies revealed regional variation in brain aging as larger age effects on medial temporal and posterior regions for oldest-old than younger-old adults. They also revealed that brain maintenance explained preserved cognitive functioning into the tenth decade of life. Very few functional MRI studies examined compensatory activity in oldest-old adults who perform as well as younger groups, although there was evidence that higher brain reserve in oldest-old adults may mediate effects of brain aging on cognition. Despite some continuity, different cognitive and neural profiles across the older adult lifespan should be addressed in modern neurocognitive aging theories.

Beta-amyloid moderates the relationship between cortical thickness and attentional control in middle- and older-aged adults

Although often unmeasured in studies of cognition, many older adults possess Alzheimer disease (AD) pathologies such as beta-amyloid (Aβ) deposition, despite being asymptomatic. We were interested in examining whether the behavior-structure relationship observed in later life was altered by the presence of preclinical AD pathology. A total of 511 cognitively unimpaired adults completed magnetic resonance imaging and three attentional control tasks; a subset (n = 396) also underwent Aβ-positron emissions tomography. A vertex-wise model was conducted to spatially represent the relationship between cortical thickness and average attentional control accuracy, while moderation analysis examined whether Aβ deposition impacted this relationship. First, we found that reduced cortical thickness in temporal, medial- and lateral-parietal, and dorsolateral prefrontal cortex, predicted worse performance on the attention task composite. Subsequent moderation analyses observed that levels of Aβ significantly influence the relationship between cortical thickness and attentional control. Our results support the hypothesis that preclinical AD, as measured by Aβ deposition, is partially driving what would otherwise be considered general aging in a cognitively normal adult population.

Across the adult lifespan the ipsilateral sensorimotor cortex negative BOLD response exhibits decreases in magnitude and spatial extent suggesting declining inhibitory control

Ipsilateral sensorimotor (iSM1) cortex negative BOLD responses (NBR) are observed to unilateral tasks and are thought to reflect a functionally relevant component of sensorimotor inhibition. Evidence suggests that sensorimotor inhibitory mechanisms degrade with age, along with aspects of motor ability and dexterity. However, understanding of age-related changes to NBR is restricted by limited comparisons between young vs old adults groups with relatively small samples sizes. Here we analysed a BOLD fMRI dataset (obtained from the CamCAN repository) of 581 healthy subjects, gender-balanced, sampled from the whole adult lifespan performing a motor response task to an audio-visual stimulus. We aimed to investigate how sensorimotor and default-mode NBR characteristics of magnitude, spatial extent and response shape alter at every decade of the aging process. A linear decrease in iSM1 NBR magnitude was observed across the whole lifespan whereas the contralateral sensorimotor (cSM1) PBR magnitude was unchanged. An age-related decrease in the spatial extent of NBR and an increase in the ipsilateral positive BOLD response (PBR) was observed. This occurred alongside an increasing negative correlation between subject's iSM1 NBR and cSM1 PBR magnitude, reflecting a change in the balance between cortical excitation and inhibition. Conventional GLM analysis, using a canonical haemodynamic response (HR) function, showed disappearance of iSM1 NBR in subjects over 50 years of age. However, a deconvolution analysis showed that the shape of the iSM1 HR altered throughout the lifespan, with delayed time-to-peak and decreased magnitude. The most significant decreases in iSM1 HR magnitude occurred in older age (>60 years) but the first changes in shape and timing occurred as early as 30 years, suggesting possibility of separate mechanisms underlying these alterations. Reanalysis using data-driven HRs for each decade detected significant sensorimotor NBR into late older age, showing the importance of taking changes in HR morphology into account in fMRI aging studies. These results may reflect fMRI measures of the age-related decreases in transcollosal inhibition exerted upon ipsilateral sensorimotor cortex and alterations to the excitatory-inhibitory balance in the sensorimotor network.

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.

Multivariate patterns of brain-behavior associations across the adult lifespan

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20–73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks’ FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Normal Aging Affects the Short-Term Temporal Stability of Implicit, But Not Explicit, Motor Learning following Visuomotor Adaptation

Normal aging is associated with a decline in memory and motor learning ability. However, the exact form of these impairments (e.g., the short-term temporal stability and affected learning mechanisms) is largely unknown. Here, we used a sensorimotor adaptation task to examine changes in the temporal stability of two forms of learning (explicit and implicit) because of normal aging. Healthy young subjects (age range, 19–28 years; 20 individuals) and older human subjects (age range, 63–85 years; 19 individuals) made reaching movements in response to altered visual feedback. On each trial, subjects turned a rotation dial to select an explicit aiming direction. Once selected, the display was removed and subjects moved the cursor from the start position to the target. After initial training with the rotational feedback perturbation, subjects completed a series of probe trials at different delay periods to systematically assess the short-term retention of learning. For both groups, the explicit aiming showed no significant decrease over 1.5 min. However, this was not the case for implicit learning; the decay pattern was markedly different between groups. Older subjects showed a linear decrease of the implicit component of adaptation over time, while young subjects showed an exponential decay over the same period (time constant, 25.61 s). Although older subjects adapted at a similar rate, these results suggest natural aging selectively impacts the short-term (seconds to minutes) temporal stability of implicit motor learning mechanisms. This understanding may provide a means to dissociate natural aging memory impairments from deficits caused by brain disorders that progress with aging.

Effects of physical exercise on working memory in older adults: a systematic and meta-analytic review

Background

This systematic and meta-analytic review aimed to investigate the effects of physical exercise on the working memory of older adults, and to identify the moderators of these effects.

Methods

We searched six electronic databases for randomized controlled trials on the effects of physical exercise on working memory that were published before or on May 15, 2020. The PEDro scale was used to evaluate the methodological quality of the included studies. Stata 14.0 software was used to perform the meta-analysis, subgroup analysis, and publication bias testing.

Results

A total of 28 studies and 2156 participants were included. The methodological quality of the included studies was fair to excellent, and there was no publication bias. Overall, we found that physical exercise had a significant effect on working memory in older adults (standardized mean difference = 0.30, p < 0.0001). The effects of physical exercise on working memory were moderated by exercise frequency, intensity, type, duration, cognitive status, and control subgroup (active/passive), but not by intervention period or age of participant.

Conclusion

Physical exercise can effectively improve the working memory of older adults. The recommended physical exercise is multi-component exercise or mind–body exercise of moderate intensity for 45–60 min 3 times a week, for more than 6 months.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11556-021-00272-y.

Pitfalls in brain age analyses

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R² will be artificially inflated to the extent that it is highly improbable that an R² value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Reconfiguration and dedifferentiation of functional networks during cognitive control across the adult lifespan

Healthy aging is accompanied by reduced cognitive control and widespread alterations in the underlying brain networks; but the extent to which large-scale functional networks in older age show reduced specificity across different domains of cognitive control is unclear. Here we use cov-STATIS (a multi-table multivariate technique) to examine similarity of functional connectivity during different domains of cognitive control-inhibition, initiation, shifting, and working memory-across the adult lifespan. We report two major findings: (1) Functional connectivity patterns during initiation, inhibition, and shifting were more similar in older ages, particularly for control and default networks, a pattern consistent with dedifferentiation of the neural correlates associated with cognitive control; and (2) Networks exhibited age-related reconfiguration such that frontal, default, and dorsal attention networks were more integrated whereas sub-networks of somato-motor system were more segregated in older age. Together these findings offer new evidence for dedifferentiation and reconfiguration of functional connectivity underlying different aspects of cognitive control in normal aging.

Age-preserved semantic memory and the CRUNCH effect manifested as differential semantic control networks: An fMRI study

Semantic memory representations are overall well-maintained in aging whereas semantic control is thought to be more affected. To explain this phenomenon, this study aims to test the predictions of the Compensation Related Utilization of Neural Circuits Hypothesis (CRUNCH) focusing on task demands in aging as a possible framework. The CRUNCH effect would manifest itself in semantic tasks through a compensatory increase in neural activation in semantic control network regions but only up to a certain threshold of task demands. This study will compare 40 young (20-35 years old) with 40 older participants (60-75 years old) in a triad-based semantic judgment task performed in an fMRI scanner while manipulating levels of task demands (low vs. high) through semantic distance. In line with the CRUNCH predictions, differences in neurofunctional activation and behavioral performance (accuracy and response times) are expected in young vs. old participants in the low- vs. high-demand conditions manifested in semantic control Regions of Interest.

The relationship of functional hippocampal activity, amyloid deposition, and longitudinal memory decline to memory complaints in cognitively healthy older adults

We evaluated whether self-reports of worse cognition in older adults with normal cognitive function reflected actual memory decline, amyloid pathology, and subtle vulnerabilities in hippocampal function. We measured subjective cognitive decline (SCD) in 156 older participants from the Dallas Lifespan Brain Study. Functional hippocampal activation during encoding, measured with fMRI, and longitudinal memory change that was measured in the four years preceding the SCD measures were used to predict the magnitude of SCD. A subsample (N=105) also underwent ¹⁸F-Florbetapir PET imaging that measured amyloid burden. Results showed that increased SCD were associated with greater prior memory decline and amyloid deposition. Importantly, decreased hippocampal activation during encoding was a significant predictor of SCD, particularly in young-old adults below 69 years old, above and beyond prior memory change and amyloid deposition. These results indicate that multiple measures of neural and cognitive dysfunction are simultaneously associated with SCD. Moreover, SCD in younger seniors appears to reflect deficient hippocampal activity that increases their reports of poorer memory, independent of amyloid. This manuscript is part of the Special Issue entitled "Cognitive Neuroscience of Healthy and Pathological Aging" edited by Drs. M. N. Rajah, S. Belleville, and R. Cabeza. This article is part of the Virtual Special Issue titled COGNITIVE NEU-ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

Age Differences in the Subcomponents of Executive Functioning

OBJECTIVES

Across the life span, deficits in executive functioning (EF) are associated with poor behavioral control and failure to achieve goals. Though EF is often discussed as one broad construct, a prominent model of EF suggests that it is composed of three subdomains: inhibition, set shifting, and updating. These subdomains are seen in both younger (YA) and older adults (OA), with performance deficits across subdomains in OA. Therefore, our goal was to investigate whether subdomains of EF might be differentially affected by age, and how these differences may relate to broader global age differences in EF.

METHODS

To assess these age differences, we conducted a meta-analysis at multiple levels, including task level, subdomain level, and of global EF. Based on previous work, we hypothesized that there would be overall differences in EF in OA.

RESULTS

Using 1,268 effect sizes from 401 articles, we found overall differences in EF with age. Results suggested that differences in performance are not uniform, such that variability in age effects emerged at the task level, and updating was not as affected by age as other subdomains.

DISCUSSION

These findings advance our understanding of age differences in EF, and stand to inform early detection of EF decline.

Functional plasticity abnormalities over the lifespan of first-episode patients with major depressive disorder: a resting state fMRI study

Background

Neurodevelopmental and neurodegenerative theories of depression suggest that patients with major depressive disorder (MDD) may follow abnormal developmental, maturational, and aging processes. However, a lack of lifespan studies has precluded verification of these theories. Herein, we analyzed functional magnetic resonance imaging (fMRI) data to comprehensively characterize age-related functional trajectories, as measured by the fractional amplitude of low frequency fluctuations (fALFF), over the course of MDD.

Methods

In total, 235 MDD patients with age-differentiated onsets and 235 age- and sex-matched healthy controls (HC) were included in this study. We determined the pattern of age-related fALFF changes by cross-sectionally establishing the general linear model (GLM) between fALFF and age over a lifespan. Furthermore, the subjects were divided into four age groups to assess age-related neural changes in detail. Inter-group fALFF comparison (MDD vs. HC) was conducted in each age group and Granger causal analysis (GCA) was applied to investigate effective connectivity between regions.

Results

Compared with the HC, no significant quadratic or linear age effects were found in MDD over the entire lifespan, suggesting that depression affects the normal developmental, maturational, and degenerative process. Inter-group differences in fALFF values varied significantly at different ages of onset. This implies that MDD may impact brain functions in a highly dynamic way, with different patterns of alterations at different stages of life. Moreover, the GCA analysis results indicated that MDD followed a distinct pattern of effective connectivity relative to HC, and this may be the neural basis of MDD with age-differentiated onsets.

Conclusions

Our findings provide evidence that normal developmental, maturational, and ageing processes were affected by MDD. Most strikingly, functional plasticity changes in MDD with different ages of onset involved dynamic interactions between neuropathological processes in a tract-specific manner.

Cortical Thickness, Volume, and Surface Area in the Motoric Cognitive Risk Syndrome

BACKGROUND

The motoric cognitive risk (MCR) syndrome is a pre-clinical stage of dementia characterized by slow gait and cognitive complaint. Yet, the brain substrates of MCR are not well established.

OBJECTIVE

To examine cortical thickness, volume, and surface area associated with MCR in the MCR-Neuroimaging Consortium, which harmonizes image processing/analysis of multiple cohorts.

METHODS

Two-hundred MRIs (M age 72.62 years; 47.74%female; 33.17%MCR) from four different cohorts (50 each) were first processed with FreeSurfer 6.0, and then analyzed using multivariate and univariate general linear models with 1,000 bootstrapped samples (n-1; with resampling). All models adjusted for age, sex, education, white matter lesions, total intracranial volume, and study site.

RESULTS

Overall, cortical thickness was lower in individuals with MCR than in those without MCR. There was a trend in the same direction for cortical volume (p = 0.051). Regional cortical thickness was also lower among individuals with MCR than individuals without MCR in prefrontal, insular, temporal, and parietal regions.

CONCLUSION

Cortical atrophy in MCR is pervasive, and include regions previously associated with human locomotion, but also social, cognitive, affective, and motor functions. Cortical atrophy in MCR is easier to detect in cortical thickness than volume and surface area because thickness is more affected by healthy and pathological aging.

Neural sensitivity to phonological characteristics is stable across the lifespan

Aging is often associated with declines in language production. For example, compared to younger adults, older adults experience more tip-of-the-tongue (TOT) states, show decreased speed and accuracy in naming objects, and have more pauses and fillers in speech, all of which indicate age-related increases in retrieval difficulty. While prior work has suggested that retrieval difficulty may be phonologically based, it is unclear whether there are age-related differences in the organization of phonological information per se or whether age-related difficulties may arise from accessing that information. Here we used fMRI to investigate the neural and behavioral basis of phonological neighborhood denisty (PND) effects on picture naming across the lifespan (N=91, ages 20-75). Consistent with prior work, behavioral results revealed that higher PND led to faster picture naming times and higher accuracies overall, and that older adults were less accurate in their responses. Consistent with the behavioral analyses, fMRI analyses showed that increasing PND was associated with decreased activation in auditory and motor language regions, including bilateral superior temporal gyri and bilateral precentral gyri. Interestingly, although there were age-related increases in functional activation to picture naming, there were no age-related modulations of neural sensitivity to PND. Overall, these results suggest that having a large cohort of phonological neighbors facilitates language production, and although aging is associated with increases in language production difficulty, sensitivity to phonological features during language production is stable across the lifespan.

Cognitive Persistence and Executive Function in the Multilingual Brain During Aging

Researchers have debated the extent to which the experience of speaking more than two languages induces long-term neuroplasticity that protects multilinguals from the adverse cognitive effects of aging. In this review, I propose a novel theory that multilingualism affects cognitive persistence, the application of effort to improve performance on challenging tasks. I review recent evidence demonstrating that the cingulo-opercular network, consisting of the bilateral inferior frontal gyrus (IFG) and dorsal anterior cingulate cortex (dACC), supports cognitive persistence. I then show that this same network is involved in multilingual language control and changes with multilingual language experience. While both early and late multilinguals exhibit differences in the cingulo-opercular network compared to monolinguals, I find that early multilinguals have a pattern of decreased dACC activity and increased left IFG activity that may enable more efficient cognitive control, whereas late multilinguals show larger dACC responses to conflict that may be associated with higher cognitive persistence. I further demonstrate that multilingual effects on the cingulo-opercular network are present in older adults and have been implicated in the mitigation of cognitive symptoms in age-related neurodegenerative disorders. Finally, I argue that mixed results in the literature are due, in part, to the confound between cognitive persistence and ability in most executive function tasks, and I provide guidance for separating these processes in future research.

Risk for Alzheimer's disease: A review of long-term episodic memory encoding and retrieval fMRI studies

Many risk factors have been identified that predict future progression to Alzheimer's disease (AD). However, clear links have yet to be made between these risk factors and how they affect brain functioning in early stages of AD. We conducted a narrative review and a quantitative analysis to better understand the relationship between nine categories of AD risk (i.e., brain pathology, genetics/family history, vascular health, head trauma, cognitive decline, engagement in daily life, late-life depression, sex/gender, and ethnoracial group) and task-evoked fMRI activity during episodic memory in cognitively-normal older adults. Our narrative review revealed widespread regional alterations of both greater and lower brain activity with AD risk. Nevertheless, our quantitative analysis revealed that a subset of studies converged on two patterns: AD risk was associated with (1) greater brain activity in frontal and parietal regions, but (2) reduced brain activity in hippocampal and occipital regions. The brain regions affected depended on the assessed memory stage (encoding or retrieval). Although the results clearly indicate that AD risks impact brain activity, we caution against using fMRI as a diagnostic tool for AD at the current time because the above consistencies were present among much variability, even among the same risk factor.

Neural correlates of working memory training: Evidence for plasticity in older adults

Brain activity typically increases with increasing working memory (WM) load, regardless of age, before reaching an apparent ceiling. However, older adults exhibit greater brain activity and reach ceiling at lower loads than younger adults, possibly reflecting compensation at lower loads and dysfunction at higher loads. We hypothesized that WM training would bolster neural efficiency, such that the activation peak would shift towards higher memory loads after training. Pre-training, older adults showed greater recruitment of the WM network than younger adults across all loads, with decline at the highest load. Ten days of adaptive training on a verbal WM task improved performance and led to greater brain responsiveness at higher loads for both groups. For older adults the activation peak shifted rightward towards higher loads. Finally, training increased task-related functional connectivity in older adults, both within the WM network and between this task-positive network and the task-negative/default-mode network. These results provide new evidence for functional plasticity with training in older adults and identify a potential signature of improvement at the neural level.

Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults

Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

Grey-Matter Thickness of the Left But Not the Right Primary Visual Area Correlates with Autism Traits in Typically Developing Adults

We examined whether functional and structural variability in the primary visual area (V1) correlated with autism traits. Twenty-nine participants (16 males; M_Age = 26.4 years, SD_Age = 4.0 years) completed the autism-spectrum quotient (AQ) questionnaire prior to a magnetic resonance imaging session. The total AQ scores was used to assess the degree of self-reported autism traits. The average functional activation in V1 to visual stimulation and its average grey-matter thickness were calculated. There were no correlations between functional activation in V1 and autism traits. Conversely, grey-matter thickness of the left but not the right V1 correlated with autism traits. We conclude that structural changes in the left V1 could be a marker for the presence of autism traits.

Contributions of White Matter Connectivity and BOLD Modulation to Cognitive Aging: A Lifespan Structure-Function Association Study

The ability to flexibly modulate brain activation to increasing cognitive challenge decreases with aging. This age-related decrease in dynamic range of function of regional gray matter may be, in part, due to age-related degradation of regional white matter tracts. Here, a lifespan sample of 171 healthy adults (aged 20-94) underwent magnetic resonance imaging (MRI) scanning including diffusion-weighted imaging (for tractography) and functional imaging (a digit n-back task). We utilized structural equation modeling to test the hypothesis that age-related decrements in white matter microstructure are associated with altered blood-oxygen-level-dependent (BOLD) modulation, and both in turn, are associated with scanner-task accuracy and executive function performance. Specified structural equation model evidenced good fit, demonstrating that increased age negatively affects n-back task accuracy and executive function performance in part due to both degraded white matter tract microstructure and reduced task-difficulty-related BOLD modulation. We further demonstrated that poorer white matter microstructure integrity was associated with weakened BOLD modulation, particularly in regions showing positive modulation effects, as opposed to negative modulation effects. This structure-function association study provides further evidence that structural connectivity influences functional activation, and the two mechanisms in tandem are predictive of cognitive performance, both during the task, and for cognition measured outside the scanner environment.

Striatal iron content is linked to reduced fronto-striatal brain function under working memory load

Non-heme iron accumulation contributes to age-related decline in brain structure and cognition via a cascade of oxidative stress and inflammation, although its effect on brain function is largely unexplored. Thus, we examine the impact of striatal iron on dynamic range of BOLD modulation to working memory load. N ​= ​166 healthy adults (age 20-94) underwent cognitive testing and an imaging session including n-back (0-, 2-, 3-, and 4-back fMRI), R2*-weighted imaging, and pcASL to measure cerebral blood flow. A statistical model was constructed to predict voxelwise BOLD modulation by age, striatal iron content and an age ​× ​iron interaction, controlling for cerebral blood flow, sex, and task response time. A significant interaction between age and striatal iron content on BOLD modulation was found selectively in the putamen, caudate, and inferior frontal gyrus. Greater iron was associated with reduced modulation to difficulty, particularly in middle-aged and younger adults with greater iron content. Further, iron-related decreases in modulation were associated with poorer executive function in an age-dependent manner. These results suggest that iron may contribute to differences in functional brain activation prior to older adulthood, highlighting the potential role of iron as an early factor contributing to trajectories of functional brain aging.

Reorganization of brain networks and its association with general cognitive performance over the adult lifespan

Healthy aging is associated with structural and functional changes in the brain even in individuals who are free of neurodegenerative diseases. Using resting state functional magnetic resonance imaging data from a carefully selected cohort of participants, we examined cross sectional changes in the functional organization of several large-scale brain networks over the adult lifespan and its potential association with general cognitive performance. Converging results from multiple analyses at the voxel, node, and network levels showed widespread reorganization of functional brain networks with increasing age. Specifically, the primary processing (visual and sensorimotor) and visuospatial (dorsal attention) networks showed diminished network integrity, while the so-called core neurocognitive (executive control, salience, and default mode) and basal ganglia networks exhibited relatively preserved between-network connections. The visuospatial and precuneus networks also showed significantly more widespread increased connectivity with other networks. Graph analysis suggested that this reorganization progressed towards a more integrated network topology. General cognitive performance, assessed by Addenbrooke's Cognitive Examination-Revised total score, was positively correlated with between-network connectivity among the core neurocognitive and basal ganglia networks and the integrity of the primary processing and visuospatial networks. Mediation analyses further indicated that the observed association between aging and relative decline in cognitive performance could be mediated by changes in relevant functional connectivity measures. Overall, these findings provided further evidence supporting widespread age-related brain network reorganization and its potential association with general cognitive performance during healthy aging.

Functional Neuroimaging in the New Era of Big Data

The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.

Shape-related characteristics of age-related differences in subcortical structures

OBJECTIVES

With an increasing aging population, it is important to understand biological markers of aging. Subcortical volume is known to differ with age; additionally considering shape-related characteristics may provide a better index of age-related differences. Fractal dimensionality is more sensitive to age-related differences, but is borne out of mathematical principles, rather than neurobiological relevance. We considered four distinct measures of shape and how they relate to aging and fractal dimensionality: surface-to-volume ratio, sphericity, long-axis curvature, and surface texture.

METHODS

Structural MRIs from a combined sample of over 600 healthy adults were used to measure age-related differences in the structure of the thalamus, putamen, caudate, and hippocampus. For each, volume and fractal dimensionality were calculated, as well as four distinct shape measures. These measures were examined for their utility in explaining age-related variability in brain structure.

RESULTS

The four shape measures were able to account for 80%-90% of the variance in fractal dimensionality. Of the distinct shape measures, surface-to-volume ratio was the most sensitive biomarker.

CONCLUSION

Though volume is often used to characterize inter-individual differences in subcortical structures, our results demonstrate that additional measures can be useful complements. Our results indicate that shape characteristics are useful biological markers of aging.

Robust estimation of sulcal morphology

While it is well established that cortical morphology differs in relation to a variety of inter-individual factors, it is often characterized using estimates of volume, thickness, surface area, or gyrification. Here we developed a computational approach for estimating sulcal width and depth that relies on cortical surface reconstructions output by FreeSurfer. While other approaches for estimating sulcal morphology exist, studies often require the use of multiple brain morphology programs that have been shown to differ in their approaches to localize sulcal landmarks, yielding morphological estimates based on inconsistent boundaries. To demonstrate the approach, sulcal morphology was estimated in three large sample of adults across the lifespan, in relation to aging. A fourth sample is additionally used to estimate test–retest reliability of the approach. This toolbox is now made freely available as supplemental to this paper: https://cmadan.github.io/calcSulc/.

Age Affects How Task Difficulty and Complexity Modulate Perceptual Decision-Making

Decisions differ in difficulty and rely on perceptual information that varies in richness (complexity); aging affects cognitive function including decision-making, and yet, the interaction between difficulty and perceptual complexity have rarely been addressed in aging. Using a parametric fMRI modulation analysis and psychophysics, we address how task difficulty affects decision-making when controlling for the complexity of the perceptual context in which decisions are made. Perceptual complexity was varied in a factorial design while participants made perceptual judgments on the spatial frequency of two patches that either shared the same orientation (simple condition) or were orthogonal in orientation (complex condition). Psychophysical thresholds were measured for each participant in each condition and served to set individualized levels of difficulty during scanning. Findings indicate that discriminability interacts with complexity, to influence decisional difficulty. Modulation as a function of difficulty is maintained with age, as indicated by coupling between increased activation in fronto-parietal regions and suppression in the lateral hubs, however, age has a specific effect in the ventral anterior cingulate cortex (ACC), driven by performance at near-threshold (difficult) levels for the simpler stimulus combination condition, but not the more complex one. Taken together, our findings suggest that the context of difficulty, or what is perceived as important, changes with age, and that decisions that would seem neutral to younger participants, may carry more emphasis with age.

The intrinsic neural architecture of inhibitory control: The role of development and emotional experience

Inhibitory control is a key determinant of goal-directed behavior. Its susceptibility to reward implies that its variations may not only reflect cognitive ability, but also sensitivity to goal-relevant information. Since cognitive ability and motivational sensitivity vary as a function of age and mood, we hypothesized that their relevance for predicting individual differences in inhibition would similarly vary. Here, we tested this prediction with respect to the brain's intrinsic functional architecture. Specifically, we reasoned that age and affective functioning would both moderate the relationship between inhibition and resting state expression of the dynamic neural organization patterns linked to engaging in cognitive effort versus those involved in manipulating motivationally salient information. First, we used task fMRI data from the Human Connectome Project (N = 359 participants) to identify the brain organization patterns unique to effortful cognitive processing versus manipulation of motivationally relevant information. We then assessed the association between inhibitory control and relative expression of these two neural patterns in an independent resting state dataset from the Nathan Kline Institute-Rockland lifespan sample (N = 247). As hypothesized, the relation between inhibition and intrinsic functional brain architecture varied as a function of age and affective functioning. Among those with superior affective functioning, better inhibitory control in adolescence and early adulthood was associated with stronger resting state expression of the brain pattern that typified processing of motivationally salient information. The opposite effect emerged beyond the age of 49. Among individuals with poorer affective functioning, a significant link between inhibition and brain architecture emerged only before the age of 28. In this group, superior inhibition was associated with stronger resting state expression of the neural pattern that typified effortful cognitive processing. Our results thus imply that motivational relevance makes a unique contribution to superior cognitive functioning during earlier life stages. However, its relevance to higher-order mentation decreases with aging and increased prevalence of mood-related problems, which raises the possibility that patterns of neurobehavioral responsiveness to motivational salience may constitute sensitive markers of successful lifespan development.

Defining Cognitive Reserve and Implications for Cognitive Aging

PURPOSE OF REVIEW

The aim of this review is to summarize current conceptual models of cognitive reserve (CR) and related concepts and to discuss evidence for these concepts within the context of aging and Alzheimer's disease.

RECENT FINDINGS

Evidence to date supports the notion that higher levels of CR, as measured by proxy variables reflective of lifetime experiences, are associated with better cognitive performance, and with a reduced risk of incident mild cognitive impairment/dementia. However, the impact of CR on longitudinal cognitive trajectories is unclear and may be influenced by a number of factors. Although there is promising evidence that some proxy measures of CR may influence structural brain measures, more research is needed. The protective effects of CR may provide an important mechanism for preserving cognitive function and cognitive well-being with age, in part because it can be enhanced throughout the lifespan. However, more research on the mechanisms by which CR is protective is needed.

The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization

Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21-32 years), the adult (age 41-54 years), and the old (age 60-86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing.