Age Differentiation within Gray Matter, White Matter, and between Memory and White Matter in an Adult Life Span Cohort

Childhood engagement in cognitively stimulating activities moderates relationships between brain structure and cognitive function in adulthood

Greater engagement in cognitively stimulating activities (CSA) during adulthood has been shown to protect against neurocognitive decline, but no studies have investigated whether CSA during childhood protects against effects of brain changes on cognition later in life. The current study tested the moderating role of childhood CSA in the relationships between brain structure and cognitive performance during adulthood. At baseline (N=250) and 5-year follow-up (N=204) healthy adults aged 20-80 underwent MRI to assess four structural brain measures and completed neuropsychological tests to measure three cognitive domains. Participants were categorized into low and high childhood CSA based on self-report questionnaires. Results of multivariable linear regressions analyzing interactions between CSA, brain structure, and cognition showed that higher childhood CSA was associated with a weaker relationship between cortical thickness and memory at baseline, and attenuated the effects of change in cortical thickness and brain volume on decline in processing speed over time. These findings suggest higher CSA during childhood may mitigate the effects of brain structure changes on cognitive function later in life.

Relationship of Klotho with cognition and dementia: Results from the NHANES 2011-2014 and Mendelian randomization study

The relationships of Klotho levels with cognition and dementia are poorly understood. This study aimed to investigate the association between Klotho levels and cognitive function and to determine causality between Klotho and dementia using Mendelian randomization (MR). Based on data from the National Health and Nutrition Survey (NHANES) 2011-2014, this study consisted of 1875 older adults aged 60-79 years. Cognitive function was assessed by the digit symbol substitution test (DSST). We performed weighted multivariable-adjusted linear regression to assess the association between Klotho concentrations and cognitive function. Then, 2-sample MR was conducted to assess the causal relationship between Klotho and dementia. The inverse variance weighted (IVW) method was used as the primary analysis. We observed a positive association between serum Klotho concentrations and the results of the Digit Symbol Substitution test (DSST) (T2: β 2.16, 95% CI: 0.30-4.01, P = 0.03, T3: β 2.48, 95% CI: 0.38-4.57, P = 0.02) after adjusting for the covariates. Moreover, there was also a potential nonlinear relationship between Klotho and DSST. The IVW method showed that genetically predicted high Klotho levels were not significantly associate with any type of dementia, including Alzheimer's disease (OR = 1.03, 95% CI: 0.96-1.10, P = 0.46), vascular dementia (OR = 1.04, 95% CI: 0.87-1.25, P = 0.66), frontotemporal dementia (OR = 0.73, 95% CI: 0.47-1.14, P = 0.16), or dementia with Lewy bodies (OR = 1.03, 95% CI: 0.87-1.23, P = 0.73). In the cross-sectional observational study, Klotho and cognitive function were significantly correlated; however, findings from MR studies did not indicate a causal relationship between Klotho and dementia.

In vivo evidence of microstructural hypo-connectivity of brain white matter in 22q11.2 deletion syndrome

22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.

Estimating Local Structural Equation Models

Local structural equation models (LSEM) are structural equation models that study model parameters as a function of a moderator. This article reviews and extends LSEM estimation methods and discusses the implementation in the R package sirt. In previous studies, LSEM was fitted as a sequence of models separately evaluated as each value of the moderator variables. In this article, a joint estimation approach is proposed that is a simultaneous estimation method across all moderator values and also allows some model parameters to be invariant with respect to the moderator. Moreover, sufficient details on the main estimation functions in the R package sirt are provided. The practical implementation of LSEM is demonstrated using illustrative datasets and an empirical example. Moreover, two simulation studies investigate the statistical properties of parameter estimation and significance testing in LSEM.

An attempt to model the causal structure behind white matter aging and cognitive decline

In this diffusion tension imaging study, voxel wise structural equation modeling was used to unravel the relation between white matter, cognition, and age. Four neurocognitive ageing models describing the interplay between age, white matter integrity, and cognition were investigated but only two models survived an Akaike information criterion-based model selection procedure. The independent factor model predicts that there is no relation between white matter integrity and cognition although both systems are affected by age. The cognitive mediation model predicts that the relation between age and white matter integrity is mediated through cognition. Roughly 60% of the observed voxels were in agreement with the independent factor model while 16% of the observed voxels were in agreement with the cognitive mediation model. Imaging results of the latter model suggest that the deterioration of fibers-that connect the two hemispheres with each other-is partly caused by an age-related decline in cognitive functioning.

New horizons in emotional well-being and brain aging: Potential lessons from cross-species research

Emotional wellbeing (EWB) is a multi-faceted concept of immediate relevance to human health. NIH recently initiated a series of research networks to advance understanding of EWB. Our network (NEW Brain Aging) focuses on mechanistic understanding of EWB in relation to brain aging. Here, by synthesizing the literature on emotional processing and the underlying brain circuit mechanisms in human and non-human animals, we propose a reactivity and reappraisal model for understanding EWB and its age-related changes. This model emphasizes the dynamic interactions between affective stimuli, behavioral/physiological responses, brain emotional states, and subjective feelings. It also aims to integrate the unique emotional processes involved in explaining EWB in aging humans with the emerging mechanistic insight of topologically conserved emotional brain networks from cross-species studies. We also highlight the research opportunities and challenges in EWB and brain aging research and the potential application of the model in addressing these issues.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Physical activity for cognitive health promotion: An overview of the underlying neurobiological mechanisms

Physical activity is one of the modifiable factors of cognitive decline and dementia with the strongest evidence. Although many influential reviews have illustrated the neurobiological mechanisms of the cognitive benefits of physical activity, none of them have linked the neurobiological mechanisms to normal exercise physiology to help the readers gain a more advanced, comprehensive understanding of the phenomenon. In this review, we address this issue and provide a synthesis of the literature by focusing on five most studied neurobiological mechanisms. We show that the body's adaptations to enhance exercise performance also benefit the brain and contribute to improved cognition. Specifically, these adaptations include, 1), the release of growth factors that are essential for the development and growth of neurons and for neurogenesis and angiogenesis, 2), the production of lactate that provides energy to the brain and is involved in the synthesis of glutamate and the maintenance of long-term potentiation, 3), the release of anti-inflammatory cytokines that reduce neuroinflammation, 4), the increase in mitochondrial biogenesis and antioxidant enzyme activity that reduce oxidative stress, and 5), the release of neurotransmitters such as dopamine and 5-HT that regulate neurogenesis and modulate cognition. We also discussed several issues relevant for prescribing physical activity, including what intensity and mode of physical activity brings the most cognitive benefits, based on their influence on the above five neurobiological mechanisms. We hope this review helps readers gain a general understanding of the state-of-the-art knowledge on the neurobiological mechanisms of the cognitive benefits of physical activity and guide them in designing new studies to further advance the field.

Among Gerontogens, Heavy Metals Are a Class of Their Own: A Review of the Evidence for Cellular Senescence

Advancements in modern medicine have improved the quality of life across the globe and increased the average lifespan of our population by multiple decades. Current estimates predict by 2030, 12% of the global population will reach a geriatric age and live another 3-4 decades. This swelling geriatric population will place critical stress on healthcare infrastructures due to accompanying increases in age-related diseases and comorbidities. While much research focused on long-lived individuals seeks to answer questions regarding how to age healthier, there is a deficit in research investigating what aspects of our lives accelerate or exacerbate aging. In particular, heavy metals are recognized as a significant threat to human health with links to a plethora of age-related diseases, and have widespread human exposures from occupational, medical, or environmental settings. We believe heavy metals ought to be classified as a class of gerontogens (i.e., chemicals that accelerate biological aging in cells and tissues). Gerontogens may be best studied through their effects on the "Hallmarks of Aging", nine physiological hallmarks demonstrated to occur in aged cells, tissues, and bodies. Evidence suggests that cellular senescence-a permanent growth arrest in cells-is one of the most pertinent hallmarks of aging and is a useful indicator of aging in tissues. Here, we discuss the roles of heavy metals in brain aging. We briefly discuss brain aging in general, then expand upon observations for heavy metals contributing to age-related neurodegenerative disorders. We particularly emphasize the roles and observations of cellular senescence in neurodegenerative diseases. Finally, we discuss the observations for heavy metals inducing cellular senescence. The glaring lack of knowledge about gerontogens and gerontogenic mechanisms necessitates greater research in the field, especially in the context of the global aging crisis.

Hippocampal and motor regions contribute to memory benefits after enacted encoding: cross-sectional and longitudinal evidence

The neurobiological underpinnings of action-related episodic memory and how enactment contributes to efficient memory encoding are not well understood. We examine whether individual differences in level (n = 338) and 5-year change (n = 248) in the ability to benefit from motor involvement during memory encoding are related to gray matter (GM) volume, white matter (WM) integrity, and dopamine-regulating genes in a population-based cohort (age range = 25-80 years). A latent profile analysis identified 2 groups with similar performance on verbal encoding but with marked differences in the ability to benefit from motor involvement during memory encoding. Impaired ability to benefit from enactment was paired with smaller HC, parahippocampal, and putamen volume along with lower WM microstructure in the fornix. Individuals with reduced ability to benefit from encoding enactment over 5 years were characterized by reduced HC and motor cortex GM volume along with reduced WM microstructure in several WM tracts. Moreover, the proportion of catechol-O-methyltransferase-Val-carriers differed significantly between classes identified from the latent-profile analysis. These results provide converging evidence that individuals with low or declining ability to benefit from motor involvement during memory encoding are characterized by low and reduced GM volume in regions critical for memory and motor functions along with altered WM microstructure.

Associations of Bioavailable Serum Testosterone With Cognitive Function in Older Men: Results From the National Health and Nutrition Examination Survey

BACKGROUND

Age-associated cognitive decline may be influenced by testosterone status. However, studies evaluating the impact of bioavailable testosterone, the active, free testosterone, on cognitive function are scarce. Our study determined the relationship between calculated bioavailable testosterone and cognitive performance in older men.

METHODS

We used data from the U.S. National Health and Nutrition Examination Survey (NHANES) between 2013 and 2014. This study consisted of 208 men aged ≥60 years. Bioavailable serum testosterone was calculated based on the total serum testosterone, sex hormone-binding globulin, and albumin levels, whereas cognitive performance was assessed through the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word List Learning Test (WLLT), Word List Recall Test (WLRT), and Intrusion Word Count Test (WLLT-IC and WLRT-IC), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). Multiple linear regression analyses were performed upon adjustment for age, ethnicity, socioeconomic status, education level, medical history, body mass index, energy, alcohol intake, physical activity levels, and sleep duration.

RESULTS

A significant positive association between bioavailable testosterone and DSST (β: 0.049, p = .002) score was detected, with no signs of a plateau effect. No significant associations with CERAD WLLT (p = .132), WLRT (p = .643), WLLT-IC (p = .979), and WLRT-IC (p = .387), and AFT (p = .057) were observed.

CONCLUSION

Calculated bioavailable testosterone presented a significant positive association with processing speed, sustained attention, and working memory in older men above 60 years of age. Further research is warranted to elucidate the impact of the inevitable age-related decline in testosterone on cognitive function in older men.

Distinct components of cardiovascular health are linked with age-related differences in cognitive abilities

Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18-88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.

Decoding the radiomic features of dorsolateral prefrontal cortex in individuals with accelerated cortical changes: implications for personalized transcranial magnetic stimulation

Purpose

Image-guided transcranial magnetic stimulation (TMS) is an emerging research field in neuroscience and rehabilitation medicine. Cortical morphometry, as a radiomic phenotype of aging, plays a vital role in developing personalized TMS model, yet few studies are afoot to examine the aging effects on region-specific morphometry and use it in the estimation of TMS-induced electric fields. Our study was aimed to investigate the radiomic features of bilateral dorsolateral prefrontal cortex (DLPFC) and quantify the TMS-induced electric fields during aging.

Approach

Baseline, 1-year and 3-year structural magnetic resonance imaging (MRI) scans from normal aging (NA) adults ( n = 32 ) and mild cognitive impairment (MCI) converters ( n = 22 ) were drawn from the Open Access Series of Imaging Studies. The quantitative measures of radiomics included cortical thickness, folding, and scalp-to-cortex distance. Realistic head models were developed to simulate the impacts of radiomic features on TMS-induced E-fields using the finite-element method.

Results

A pronounced aging-related decrease was found in the gyrification of left DLPFC in MCI converters ( t = 2.21 , p = 0.035 ), which could predict the decline of global cognition at 3-year follow up. Along with the decreased gyrification in left DLPFC, the magnitude of TMS-induced E-fields was rapidly decreased in MCI converters ( t = 2.56 , p = 0.018 ).

Conclusions

MRI-informed radiomic features of the treatment targets have significant effects on the intensity and distribution of the stimulation-induced electric fields in prodromal dementia patients. Our findings highlight the importance of region-specific radiomics when conducting the transcranial brain stimulation in individuals with accelerated cortical changes, such as Alzheimer's disease.

Structural and Functional MRI Data Differentially Predict Chronological Age and Behavioral Memory Performance

Human cognitive abilities decline with increasing chronological age, with decreased explicit memory performance being most strongly affected. However, some older adults show "successful aging," that is, relatively preserved cognitive ability in old age. One explanation for this could be higher brain-structural integrity in these individuals. Alternatively, the brain might recruit existing resources more efficiently or employ compensatory cognitive strategies. Here, we approached this question by testing multiple candidate variables from structural and functional neuroimaging for their ability to predict chronological age and memory performance, respectively. Prediction was performed using support vector machine (SVM) classification and regression across and within two samples of young (N = 106) and older (N = 153) adults. The candidate variables were (1) behavioral response frequencies in an episodic memory test; (2) recently described functional magnetic resonance imaging (fMRI) scores reflecting preservation of functional memory networks; (3) whole-brain fMRI contrasts for novelty processing and subsequent memory; (4) resting-state fMRI maps quantifying voxel-wise signal fluctuation; and (5) gray matter volume estimated from structural MRIs. While age group could be reliably decoded from all variables, chronological age within young and older subjects was best predicted from gray matter volume. In contrast, memory performance was best predicted from task-based fMRI contrasts and particularly single-value fMRI scores, whereas gray matter volume has no predictive power with respect to memory performance in healthy adults. Our results suggest that superior memory performance in healthy older adults is better explained by efficient recruitment of memory networks rather than by preserved brain structure.

Association of Cardiovascular Risk Markers and Fitness with Task-Related Neural Activity during Animacy Perception

PURPOSE

Numerous studies have demonstrated the association between cardiovascular risk markers and fitness, and broad aspects of cognition; however, the possible association of cardiovascular risk markers and fitness with social cognition, which plays a significant role in the development and maintenance of social relationships, has largely been ignored. Herein, we investigated the relationship of cardiovascular risk markers and fitness with task-related neural activity during animacy perception.

METHODS

We analyzed data from the Human Connectome Project derived from 1027 adults age 22-37 yr. Canonical correlation analysis (CCA) was conducted to evaluate the association between participants' body mass index, systolic and diastolic blood pressure, submaximal endurance, gait speed, hand dexterity, and muscular strength with task-related neural activity during animacy perception.

RESULTS

We observed a single significant CCA mode. Body mass index and blood pressure demonstrated negative cross-loadings with task-related neural activity in the temporoparietal, superior and anterior temporal, posterior cingulate, and inferior frontal regions, whereas submaximal endurance, hand dexterity, and muscular strength demonstrated positive cross-loadings. The observed CCA variates did not seem highly heritable, as the absolute differences in CCA variates in monozygotic twins, dizygotic twins, and nontwin siblings were not statistically different. Furthermore, the cardiovascular risk markers and fitness CCA variates were positively associated with animacy perception and emotion recognition accuracy, which was mediated by the task-related neural activity.

CONCLUSIONS

The present findings can provide new insights into the role of markers for cardiovascular health and fitness, specifically their association with social cognition and the underlying neural basis. The intervention for cardiovascular risk and fitness could be a potentially cost-effective method of targeting social cognition.

Brain structure and episodic learning rate in cognitively healthy ageing

Memory normally declines with ageing and these age-related cognitive changes are associated with changes in brain structure. Episodic memory retrieval has been widely studied during ageing, whereas learning has received less attention. Here we examined the neural correlates of episodic learning rate in ageing. Our study sample consisted of 982 cognitively healthy female and male older participants from the Vallecas Project cohort, without a clinical diagnosis of mild cognitive impairment or dementia. The learning rate across the three consecutive recall trials of the verbal memory task (Free and Cued Selective Reminding Test) recall trials was used as a predictor of grey matter (GM) using voxel-based morphometry, and WM microstructure using tract-based spatial statistics on fractional anisotropy (FA) and mean diffusivity (MD) measures. Immediate Recall improved by 1.4 items per trial on average, and this episodic learning rate was faster in women and negatively associated with age. Structurally, hippocampal and anterior thalamic GM volume correlated positively with learning rate. Learning also correlated with the integrity of WM microstructure (high FA and low MD) in an extensive network of tracts including bilateral anterior thalamic radiation, fornix, and long-range tracts. These results suggest that episodic learning rate is associated with key anatomical structures for memory functioning, motivating further exploration of the differential diagnostic properties between episodic learning rate and retrieval in ageing.

Individual differences in white matter microstructure of the face processing brain network are more differentiated from global fibers with increasing ability

Face processing—a crucial social ability—is known to be carried out in multiple dedicated brain regions which form a distinguishable network. Previous studies on face processing mainly targeted the functionality of face-selective grey matter regions. Thus, it is still partly unknown how white matter structures within the face network underpins abilities in this domain. Furthermore, how relevant abilities modulate the relationship between face-selective and global fibers remains to be discovered. Here, we aimed to fill these gaps by exploring linear and non-linear associations between microstructural properties of brain fibers (namely fractional anisotropy, mean diffusivity, axial and radial diffusivity) and face processing ability. Using structural equation modeling, we found significant linear associations between specific properties of fibers in the face network and face processing ability in a young adult sample (N = 1025) of the Human Connectome Project. Furthermore, individual differences in the microstructural properties of the face processing brain system tended toward stronger differentiation from global brain fibers with increasing ability. This is especially the case in the low or high ability range. Overall, our study provides novel evidence for ability-dependent specialization of brain structure in the face network, which promotes a comprehensive understanding of face selectivity.

What do neuroanatomical networks reveal about the ontology of human cognitive abilities?

Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment.

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Psychometric and neurometric cognitive ontologies are partly equivalent

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Ability-related brain areas are ontologically segregated with little to no overlap

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However, ability-related brain areas are densely interconnected by fiber tracts

Characterizing the Neural Correlates of Response Inhibition and Error Processing in Children With Symptoms of Irritability and/or Attention-Deficit/Hyperactivity Disorder in the ABCD Study®

Attention-deficit/hyperactivity disorder (ADHD), characterized by symptoms of inattention and/or hyperactivity and impulsivity, is a neurodevelopmental disorder associated with executive dysfunctions, including response inhibition and error processing. Research has documented a common co-occurrence between ADHD and pediatric irritability. The latter is more characterized by affective symptoms, specifically frequent temper outbursts and low frustration tolerance relative to typically developing peers. Shared and non-shared neural correlates of youths with varied profiles of ADHD and irritability symptoms during childhood remain largely unknown. This study first classified a large sample of youths in the Adolescent Brain Cognitive Development (ABCD) study at baseline into distinct phenotypic groups based on ADHD and irritability symptoms (N = 11,748), and then examined shared and non-shared neural correlates of response inhibition and error processing during the Stop Signal Task in a subset of sample with quality neuroimaging data (N = 5,948). Latent class analysis (LCA) revealed four phenotypic groups, i.e., high ADHD with co-occurring irritability symptoms (n = 787, 6.7%), moderate ADHD with low irritability symptoms (n = 901, 7.7%), high irritability with no ADHD symptoms (n = 279, 2.4%), and typically developing peers with low ADHD and low irritability symptoms (n = 9,781, 83.3%). Latent variable modeling revealed group differences in the neural coactivation network supporting response inhibition in the fronto-parietal regions, but limited differences in error processing across frontal and posterior regions. These neural differences were marked by decreased coactivation in the irritability only group relative to youths with ADHD and co-occurring irritability symptoms and typically developing peers during response inhibition. Together, this study provided initial evidence for differential neural mechanisms of response inhibition associated with ADHD, irritability, and their co-occurrence. Precision medicine attending to individual differences in ADHD and irritability symptoms and the underlying mechanisms are warranted when treating affected children and families.

Is Old Age Just a Number in Language Skills? Language Performance and Its Relation to Age, Education, Gender, Cognitive Screening, and Dentition in Very Old Finnish Speakers

PURPOSE

This study reports on how very old (VO) Finnish people without dementia perform in the Western Aphasia Battery (WAB) and two verbal fluency tasks and which demographic factors predict the performance.

METHOD

The study included fifty 80- to 100-year-old community-dwelling Finnish speakers with no dementing illnesses or speech-language disabilities, who completed the WAB and two verbal fluency tasks. Multifactorial statistical analyses with recursive partitioning were carried out to determine the significant predictors out of five predictor variables (age, gender, education, dentition, and Mini-Mental State Examination [MMSE]) for four response variables (WAB Aphasia Quotient [AQ], Language Quotient [LQ], semantic, and phonemic word fluencies).

RESULTS

Overall, individual variation was notable in VO speakers. All predictor variables were statistically significantly associated with one or more of the language skills. Age was the most significant predictor; the critical age of 85-86 years was associated with a decline in WAB-AQ and semantic fluency. Poor dentition and the MMSE score both predicted a decline in WAB-LQ and phonemic fluency. A high level of education was positively associated with the skills of the best-performing individuals in WAB-AQ, WAB-LQ, and semantic fluency.

CONCLUSIONS

VO age is a significant factor contributing to language performance. However, a younger age, a good cognitive performance, intact teeth, and a higher educational level also seem to have a preservative power as regards language skills. Gender differences should be interpreted with caution. The results of this study provide culture- and language-specific normative data, which aids in differentiating typical aging from the signs of acute or degenerative neuropathology to ensure appropriate medical and therapeutic interventions.

Brain and cognitive ageing: The present, and some predictions (…about the future)

Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.

Bilingualism contributes to reserve and working memory efficiency: Evidence from structural and functional neuroimaging

This study compared brain and behavioral outcomes for monolingual and bilingual older adults who reported no cognitive or memory problems on three types of memory that typically decline in older age, namely, working memory (measured by n-back), item, and associative recognition. The results showed that bilinguals were faster on the two-back working memory task than monolinguals but used a set of frontostriatal regions less than monolinguals. There was no group difference on an item/associative recognition task. In brain structure, gray matter volume and white matter integrity (fractional anisotropy) were generally lower in bilinguals than in monolinguals, but bilinguals had better white matter integrity than monolinguals in the bilateral superior corona radiata and better gray matter density in the left inferior temporal gyrus. These regions may help preserve bilinguals' executive functions despite generally more significant atrophy throughout the brain than monolinguals in that these structures contribute to efficient communication between executive frontal regions and subcortical motor regions, and perceptual pathways. Reliable negative correlations between brain structure and age were only observed in bilinguals, and to the extent that bilinguals (but not monolinguals) had better brain structure, their performance was enhanced. Collectively, the findings provide evidence for reserve in bilingual older adults.

Estimates of brain age for gray matter and white matter in younger and older adults: Insights into human intelligence

Aging entails a multifaceted complex of changes in macro- and micro-structural properties of human brain gray matter (GM) and white matter (WM) tissues, as well as in intellectual abilities. To better capture tissue-specific brain aging, we combined volume and distribution properties of diffusivity indices to derive subject-specific age scores for each tissue. We compared age-related variance between younger and older adults for GM and WM age scores, and tested whether tissue-specific age scores could explain different effects of aging on fluid (Gf) and crystalized (Gc) intelligence in younger and older adults. Chronological age was strongly associated with GM (R2 = 0.73) and WM (R2 = 0.57) age scores. The GM age score accounted for significantly more variance in chronological age in younger relative to older adults (p < 0.001), whereas the WM age score accounted for significantly more variance in chronological age in older compared to younger adults (p < 0.025). Consistent with existing literature, younger adults outperformed older adults in Gf while older adults outperformed younger adults in Gc. The GM age score was negatively associated with Gf in younger adults (p < 0.02), whereas the WM age score was negatively associated with Gc in older adults (p < 0.02). Our results provide evidence for differences in the effects of age on GM and WM in younger versus older adults that may contribute to age-related differences in Gf and Gc.

Age affects white matter microstructure and episodic memory across the older adult lifespan

Diffusion imaging studies have observed age-related degradation of white matter that contributes to cognitive deficits separately in younger-old (ages 65-89) and oldest-old (ages 90+) adults. But it remains unclear whether these age effects are magnified in advanced age groups, which may reflect disease-related pathology. Here, we tested whether age-related differences in white matter microstructure followed linear or nonlinear patterns across the entire older adult lifespan (65-98 years), these patterns were influenced by oldest-old adults at increased risk of dementia (cognitive impairment no dementia, CIND), and they explained age effects on episodic memory. Results revealed nonlinear microstructure declines across fiber classes (medial temporal, callosal, association, projection and/or thalamic) that were largest for medial temporal fibers. These patterns remained after excluding oldest-old participants with CIND, indicating that aging of white matter microstructure cannot solely be explained by pathology associated with early cognitive impairment. Moreover, finding that the effect of age on episodic memory was mediated by medial temporal fiber microstructure suggests it is essential for facilitating memory-related neural signals across the older adult lifespan.

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies.

Age-related disintegration in functional connectivity: Evidence from Reference Ability Neural Network (RANN) cohort

Aging is typically marked by a decline in some domains of cognition. Some theories have linked this decline to a reduction in distinctiveness of processing at the neural level that in turn leads to cognitive decline. Increasing correlations with age among tasks formerly considered independent have been posited, supporting dedifferentiation, although results have been mixed. An alternative view is that tasks become more, and not less, independent of one another with increasing age, suggesting age-related differentiation, or what has also been termed disintegration. In the current study, we investigated if the aging process leads to a loss of behavioral and neural specificity within latent cognitive abilities. To this end, we tested 287 participants (20-80 years) on a battery of 12 in-scanner tests, three each tapping one of four reference abilities. We performed between-task correlations within domain (pertaining to convergent validity), and between domain (pertaining to discriminant validity) at both the behavioral and neural level and found that neural convergent validity was positively associated with behavioral convergent validity. In examining neural validity across the lifespan, we found significant reductions in both within- and between-domain task correlations, with a significant decrease in construct validity (convergent or discriminant) with age. Furthermore, the effect of age on total cognition was significantly mediated by neural construct validity. Taken together, contrary to a hypothesis of dedifferentiation, these correlation reductions suggest that tasks indeed become more independent with advancing age, favoring a differentiation/disintegration hypothesis of aging.

Predicting Students' Attitudes Toward Collaboration: Evidence From Structural Equation Model Trees and Forests

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources (split by "above-average or not"), home possessions (split by "disadvantaged or not"), mother's education (split by "below high school or not"), and gender (split by "male or female") were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by "above-average or not") and sense of belonging at school (split by "above-average or not" and "disadvantaged or not") were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.

Exploratory factor analysis with structured residuals for brain network data

Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.

Does bilingualism protect against dementia? A meta-analysis

Evidence suggests that bilingualism may contribute to neuroplasticity and cognitive reserve, allowing individuals to resist cognitive decline associated with Alzheimer's disease progression, although the idea remains controversial. Here, we argue that the reason for the discrepancy stems from conflating incidence rates of dementia and the age at which the symptoms first appear, as well as statistical and methodological issues in the study designs. To clarify the issues, we conducted a comprehensive meta-analysis on the available literature regarding bilingualism and Alzheimer's disease, including both retrospective and prospective studies, as well as age of onset and incidence rates. Results revealed a moderate effect size for the protective effect of bilingualism on age of onset of symptoms of Alzheimer's disease (Cohen's d = 0.32), and weaker evidence that bilingualism prevents the occurrence of disease incidence itself (Cohen's d = 0.10). Moreover, our results cannot be explained by SES, education, or publication bias. We conclude with a discussion on how bilingualism contributes to cognitive reserve and protects against Alzheimer's disease and recommend that future studies report both age of onset as well as incidence rates when possible.

Score-Guided Structural Equation Model Trees

Structural equation model (SEM) trees are data-driven tools for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios is computationally demanding. As a remedy, we propose to guide the construction of SEM trees by a family of score-based tests that have recently been popularized in psychometrics (Merkle and Zeileis, 2013; Merkle et al., 2014). These score-based tests monitor fluctuations in case-wise derivatives of the likelihood function to detect parameter differences between groups. Compared to the likelihood-ratio approach, score-based tests are computationally efficient because they do not require refitting the model for every possible split. In this paper, we introduce score-guided SEM trees, implement them in semtree, and evaluate their performance by means of a Monte Carlo simulation.

Association between vestibular function and rotational spatial orientation perception in older adults

BACKGROUND

Spatial orientation is a complex process involving vestibular sensory input and possibly cognitive ability. Previous research demonstrated that rotational spatial orientation was worse for individuals with profound bilateral vestibular dysfunction.

OBJECTIVE

Determine whether rotational and linear vestibular function were independently associated with large amplitude rotational spatial orientation perception in healthy aging.

METHODS

Tests of rotational spatial orientation accuracy and vestibular function [vestibulo-ocular reflex (VOR), ocular and cervical vestibular evoked myogenic potentials (VEMP)] were administered to 272 healthy community-dwelling adults participating in the Baltimore Longitudinal Study of Aging. Using a mixed model multiple linear regression we regressed spatial orientation errors on lateral semicircular canal function, utricular function (ocular VEMP), and saccular function (cervical VEMP) in a single model controlling for rotation size, age, and sex.

RESULTS

After adjusting for age, and sex, individuals with bilaterally low VOR gain (β= 20.9, p = 0.014) and those with bilaterally absent utricular function (β= 9.32, p = 0.017) made significantly larger spatial orientation errors relative to individuals with normal vestibular function.

CONCLUSIONS

The current results demonstrate for the first time that either bilateral lateral semicircular canal dysfunction or bilateral utricular dysfunction are associated with worse rotational spatial orientation. We also demonstrated in a healthy aging cohort that increased age also contributes to spatial orientation ability.

Effect of apolipoprotein E polymorphism on cognition and brain in the Cambridge Centre for Ageing and Neuroscience cohort

Polymorphisms in the apolipoprotein E (APOE) gene have been associated with individual differences in cognition, brain structure and brain function. For example, the ε4 allele has been associated with cognitive and brain impairment in old age and increased risk of dementia, while the ε2 allele has been claimed to be neuroprotective. According to the ‘antagonistic pleiotropy’ hypothesis, these polymorphisms have different effects across the lifespan, with ε4, for example, postulated to confer benefits on cognitive and brain functions earlier in life. In this stage 2 of the Registered Report – https://osf.io/bufc4, we report the results from the cognitive and brain measures in the Cambridge Centre for Ageing and Neuroscience cohort (www.cam-can.org). We investigated the antagonistic pleiotropy hypothesis by testing for allele-by-age interactions in approximately 600 people across the adult lifespan (18–88 years), on six outcome variables related to cognition, brain structure and brain function (namely, fluid intelligence, verbal memory, hippocampal grey-matter volume, mean diffusion within white matter and resting-state connectivity measured by both functional magnetic resonance imaging and magnetoencephalography). We found no evidence to support the antagonistic pleiotropy hypothesis. Indeed, Bayes factors supported the null hypothesis in all cases, except for the (linear) interaction between age and possession of the ε4 allele on fluid intelligence, for which the evidence for faster decline in older ages was ambiguous. Overall, these pre-registered analyses question the antagonistic pleiotropy of APOE polymorphisms, at least in healthy adults.

Age-related differences in myeloarchitecture measured at 7 T

We have used the magnetisation transfer (MT) MRI measure as a primary measure of myelination in both the gray matter (GM) of the 78 cortical automated anatomical labeling (AAL) regions of the brain, and the underlying white matter in each region, in a cohort of healthy adults (aged 19-62 year old). The results revealed a significant quadratic trend in myelination with age, with average global myelination peaking at 42.9 year old in gray matter, and at 41.7 year old in white matter. We also explored the possibility of using the Nuclear Overhauser Enhancement (NOE) effect, which is acquired in a similar method to MT, as an additional measure of myelination. We found that the MT and NOE signals were strongly correlated in the brain and that the NOE effects displayed similar (albeit weaker) parabolic trends with age. We also investigated differences in cortical thickness with age, and confirmed a previous result of a linear decline of 4.5 ± 1.2 μm/y.

SEM-Tree hybrid models in the preferences analysis of the members of Polish households

The purpose of the paper is to identify the dimensions of the strategy of resources allocation of Polish households members and test the hypothesis concerning risky shift effect in the relationship between strategy of family decision making and trade-off in family scarce resources allocation. These dimensions were identified on the basis of nationwide empirical data gathered on a representative sample of 1020 respondents nested in 410 households. SEM-Tree hybrid models are used in the analysis of the results, which combine the confirmatory structural equation models with exploratory and predictive classification and regression trees. This allows to apply structural modeling for the study of heterogeneous populations and to assess the hierarchical impact of exogenous predictors on the identification of segments with separate and unique model structural parameters. The approach combines the advantages of a model approach (at the stage of constructing hypotheses on structural relationships and specifications of measurement models) and exploration-based data (at the stage of recursive division of the sample).

Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder

Inconsistencies across studies investigating subcortical correlates of autism spectrum disorder (ASD) may stem from small sample size, sample heterogeneity, and omitting or linearly adjusting for total brain volume (TBV). To properly adjust for TBV, brain allometry—the nonlinear scaling relationship between regional volumes and TBV—was considered when examining subcortical volumetric differences between typically developing (TD) and ASD individuals. Autism Brain Imaging Data Exchange I (ABIDE I; N = 654) data was analyzed with two methodological approaches: univariate linear mixed effects models and multivariate multiple group confirmatory factor analyses. Analyses were conducted on the entire sample and in subsamples based on age, sex, and full scale intelligence quotient (FSIQ). A similar ABIDE I study was replicated and the impact of different TBV adjustments on neuroanatomical group differences was investigated. No robust subcortical allometric or volumetric group differences were observed in the entire sample across methods. Exploratory analyses suggested that allometric scaling and volume group differences may exist in certain subgroups defined by age, sex, and/or FSIQ. The type of TBV adjustment influenced some reported volumetric and scaling group differences. This study supports the absence of robust volumetric differences between ASD and TD individuals in the investigated volumes when adjusting for brain allometry, expands the literature by finding no group difference in allometric scaling, and further suggests that differing TBV adjustments contribute to the variability of reported neuroanatomical differences in ASD.

Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences

The locus coeruleus (LC), the origin of noradrenergic modulation of cognitive and behavioral function, may play an important role healthy ageing and in neurodegenerative conditions. We investigated the functional significance of age-related differences in mean normalized LC signal intensity values (LC-CR) in magnetization-transfer (MT) images from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort - an open-access, population-based dataset. Using structural equation modelling, we tested the pre-registered hypothesis that putatively noradrenergic (NA)-dependent functions would be more strongly associated with LC-CR in older versus younger adults. A unidimensional model (within which LC-CR related to a single factor representing all cognitive and behavioral measures) was a better fit with the data than the a priori two-factor model (within which LC-CR related to separate NA-dependent and NA-independent factors). Our findings support the concept that age-related reduction of LC structural integrity is associated with impaired cognitive and behavioral function.

Fluid intelligence is associated with cortical volume and white matter tract integrity within multiple-demand system across adult lifespan

BACKGROUND

Fluid intelligence (Gf) is the innate ability of an individual to respond to complex and unexpected situations. Although some studies have considered that the multiple-demand (MD) system of the brain was the biological foundation for Gf, further characterization of their relationships in the context of aging is limited. The present study hypothesized that the structural metrics of the MD system, including cortical thickness, cortical volumes, and white matter (WM) tract integrity, was the brain correlates for Gf across the adult life span. Partial correlation analysis was performed to investigate whether the MD system could still explain Gf independent of the age effect. Moreover, the partial correlations between Gf and left/right structural metrics within the MD regions were compared to test whether the correlations displayed distinct lateralization.

METHODS

The participants were recruited from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) databank, comprising the images of 603 healthy participants aged 18-88 years acquired on a 3-T system. The MRI data included high-resolution T1-weighted and diffusion-weighted images, from which gray matter and WM structural metrics of the MD system were analyzed, respectively. The structural metrics of gray matter were quantified in terms of cortical volume/thickness of five pairs of cortical regions, and those of WM were quantified in terms of the mean axial diffusivity (D_A), radial diffusivity (D_R), mean diffusivity (D_M), and generalized fractional anisotropy (GFA) on five pairs of tracts. Partial correlation controlling for age and sex effects, was performed to investigate the associations of Gf scores with the mean D_A, D_R, D_M and GFA of all tracts in the MD system, those of left and right hemispheric tracts, and those of each tract. Fisher's exact test was used to compare the partial correlations between left and right MD regions.

RESULTS

The linear relationship between cortical volumes and Gf was evident across all levels of the MD system even after controlling for age and sex. For the WM integrity, diffusion indices including D_A, D_R, D_M and GFA displayed linear relationships with Gf scores at various levels of the MD system. Among the 10 WM tracts connecting the MD regions, bilateral superior longitudinal fasciculus I and bilateral frontal aslant tracts exhibited the strongest and significant associations. Our results did not show significant inter-hemispheric differences in the associations between structural metrics of the MD system and Gf.

CONCLUSION

Our results demonstrate significant associations between Gf and both cortical volumes and tract integrity of the MD system across the adult lifespan in a population-based cohort. We found that the association remained significant in the entire adult lifespan despite simultaneous decline of Gf and the MD system. Our results suggest that the MD system might be a structural underpinning of Gf and support the fronto-parietal model of cognitive aging. However, we did not find hemispheric differences in the Gf-MD correlations, not supporting the hemi-aging hypothesis.

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts

Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N = 551 (N = 165 imaging), age range: 5-18 years, NKI-Rockland: N = 337 (N = 65 imaging), age range: 6-18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf) abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7-8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.

A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence

Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.

Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

Cognitive Diversity in a Healthy Aging Cohort: Cross-Domain Cognition in the Cam-CAN Project

Objective: Studies of "healthy" cognitive aging often focus on a limited set of measures that decline with age. The current study argues that defining and supporting healthy cognition requires understanding diverse cognitive performance across the lifespan. Method: Data from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) cohort was examined across a range of cognitive domains. Performance was related to lifestyle including education, social engagement, and enrichment activities. Results: Results indicate variable relationships between cognition and age (positive, negative, or no relationship). Principal components analysis indicated maintained cognitive diversity across the adult lifespan, and that cognition-lifestyle relationships differed by age and domain. Discussion: Our findings support a view of normal cognitive aging as a lifelong developmental process with diverse relationships between cognition, lifestyle, and age. This reinforces the need for large-scale studies of cognitive aging to include a wider range of both ages and cognitive tasks.

A Practical Guide to Variable Selection in Structural Equation Models with Regularized MIMIC Models

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters. We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p setting, but has so far mostly been used in linear regression models. Here we show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts, and how it can be extended to regularized structural equation modeling (Jacobucci, Grimm, & McArdle, 2016). Our approach is evaluated through the use of a simulation study, showing that regularized SEM outperforms traditional SEM estimation methods in situations with a large number of predictors and small sample size. We illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short term memory, as well as identifying demographic correlates of stress, anxiety and depression. We illustrate the performance of the method and discuss practical aspects of modeling empirical data, and provide a step-by-step online tutorial.

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.

Brain Atrophy Estimated from Structural Magnetic Resonance Imaging as a Marker of Large-Scale Network-Based Neurodegeneration in Aging and Stroke

Brain atrophy is a normal part of healthy aging, and stroke appears to have neurodegenerative effects, accelerating this atrophy to pathological levels. The distributed pattern of atrophy in healthy aging suggests that large-scale brain networks may be involved. At the same time, the network wide effects of stroke are beginning to be appreciated. There is now widespread use of network methods to understand the brain in terms of coordinated brain activity or white matter connectivity. Examining brain morphology on a network level presents a powerful method of understanding brain structure and has been successfully applied to charting the course of brain development. This review will introduce recent advances in structural magnetic resonance imaging (MRI) acquisition and analyses that have allowed for reliable and reproducible estimates of atrophy in large-scale brain networks in aging and after stroke. These methods are currently underutilized despite their ease of acquisition and potential to clarify the progression of brain atrophy as a normal part of healthy aging and in the context of stroke. Understanding brain atrophy at the network level may be key to clarifying healthy aging processes and the pathway to neurodegeneration after stroke.