Regional Cortical Thickness Predicts Top Cognitive Performance in the Elderly

Cortical structure of left superior parietal cortex is associated with cognition and dual tasking: A cross-sectional preliminary study between mild cognitive impairment and healthy controls

BACKGROUND

Individuals with mild cognitive impairment (MCI) exhibit poorer performance in cognition and dual-task paradigm, while the related cortical thickness and surface area alterations remains unclear.

METHODS

Thirty participants with MCI and thirty healthy controls (HC) were recruited. Magnetic resonance imaging, cognitive assessments and dual-task Timed Up and Go test (DT-TUG) were performed to assess cerebral cortical thickness and surface area, cognitive functions, and dual-task cost (DTC) of the execution time in TUG. Spearman correlations were conducted to assess the relationships between the cognitive, TUG performance with the cortical morphological measures.

RESULTS

MCI participants performed worse in the Montreal cognitive assessment (MoCA), WAIS Digit Span, TMT and the modified Posner peripheral cuing task. Their execution time on the DT-TUG was also prolonged. WAIS Digit Span Backwards was correlated with DT-TUG in HC group. A significant between-group difference was observed in the surface area of the left SPC. The cortical thickness of this brain region was positively correlated with the total scores and attention subdomain of MoCA in HC group. The cortical thickness and the surface area were correlated with the time of DT-TUG in HC group only.

CONCLUSIONS

Individuals with MCI demonstrated declines in both cognitive function and dual-task walking performance. This study provides further evidence of surface-based structural differences in the left SPC in individuals with and without MCI, and supports the role of the left SPC in cognition and dual-task walking.

Cognitive aging and reserve factors in the Metropolit 1953 Danish male cohort

Identifying early predictors of cognitive decline and at-risk individuals is essential for timely intervention and prevention of dementia. This study aimed to detect neurobiological changes and factors related to cognitive performance in the Metropolit 1953 Danish male birth cohort. We analyzed data from 582 participants, aged 57-68 years, using machine learning techniques to group cognitive trajectories into four clusters differentiating high- and low-performing groups. These clusters were then evaluated with MRI, EEG, and lifestyle/familial risk factors to identify predictors of cognitive decline. Low education and occupation, alcohol consumption, and type 2 diabetes were associated with lower cognitive performance. Declines in neocortical volume and increases in frontotemporal alpha and temporoparietal gamma activity preceded clinical symptoms of cognitive decline. Neocortical atrophy and disruptions in network activity were prominent in lower-performing groups, with higher education and IQ scores and a lower prevalence of lifestyle factors moderating cognitive decline.

Individual differences in internalizing symptoms in late childhood: A variance decomposition into cortical thickness, genetic and environmental differences

The brain undergoes extensive development during late childhood and early adolescence. Cortical thinning is a prominent feature of this development, and some researchers have suggested that differences in cortical thickness may be related to internalizing symptoms, which typically increase during the same period. However, research has yielded inconclusive results. We utilized a new method that estimates the combined effect of individual differences in vertex-wise cortical thickness on internalizing symptoms. This approach allows for many small effects to be distributed across the cortex and avoids the necessity of correcting for multiple tests. Using a sample of 8763 children aged 8.9 to 11.1 from the ABCD study, we decomposed the total variation in caregiver-reported internalizing symptoms into differences in cortical thickness, additive genetics, and shared family environmental factors and unique environmental factors. Our results indicated that individual differences in cortical thickness accounted for less than 0.5% of the variation in internalizing symptoms. In contrast, the analysis revealed a substantial effect of additive genetics and family environmental factors on the different components of internalizing symptoms, ranging from 06% to 48% and from 0% to 34%, respectively. Overall, while this study found a minimal association between cortical thickness and internalizing symptoms, additive genetics, and familial environmental factors appear to be of importance for describing differences in internalizing symptoms in late childhood. RESEARCH HIGHLIGHTS: We utilized a new method for modelling the total contribution of vertex-wise individual differences in cortical thickness to internalizing symptoms in late childhood. The total contribution of individual differences in cortical thickness accounted for <0.5% of the variance in internalizing symptoms. Additive genetics and shared family environmental variation accounted for 17% and 34% of the variance in internalizing symptoms, respectively. Our results suggest that cortical thickness is not an important indicator for internalizing symptoms in childhood, whereas genetic and environmental differences have a substantial impact.

Frequency of cognitive "super-aging" in three Australian samples using different diagnostic criteria

OBJECTIVES

To investigate the frequency of exceptional cognition (cognitive super-aging) in Australian older adults using different published definitions, agreement between definitions, and the relationship of super-aging status with function, brain imaging markers, and incident dementia.

DESIGN

Three longitudinal cohort studies.

SETTING

Participants recruited from the electoral roll, Australian Twins Registry, and community advertisements.

PARTICIPANTS

Older adults (aged 65-106) without dementia from the Sydney Memory and Ageing Study (n = 1037; median age 78), Older Australian Twins Study (n = 361; median age 68), and Sydney Centenarian Study (n = 217; median age 97).

MEASUREMENTS

Frequency of super-aging was assessed using nine super-aging definitions based on performance on neuropsychological testing. Levels of agreement between definitions were calculated, and associations between super-aging status for each definition and functioning (Bayer ADL score), structural brain imaging measures, and incident dementia were explored.

RESULTS

Frequency of super-aging varied between 2.9 and 43.4 percent with more stringent definitions associated with lower frequency. Agreement between different criteria varied from poor (K = 0.04, AC1 = .24) to very good (K = 0.83, AC1 = .91) with better agreement between definitions using similar tests and cutoffs. Super-aging was associated with better functional performance (4.7-11%) and lower rates of incident dementia (hazard ratios 0.08-0.48) for most definitions. Super-aging status was associated with a lower burden of white matter hyperintensities (3.8-33.2%) for all definitions.

CONCLUSIONS

The frequency of super-aging is strongly affected by the demographic and neuropsychological testing parameters used. Greater consistency in defining super-aging would enable better characterization of this exceptional minority.

Structure-Function Interactions in the Hippocampus and Prefrontal Cortex Are Associated with Episodic Memory in Healthy Aging

Aging comes with declines in episodic memory. Memory decline is accompanied by structural and functional alterations within key brain regions, including the hippocampus and lateral prefrontal cortex, as well as their affiliated default and frontoparietal control networks. Most studies have examined how structural or functional differences relate to memory independently. Here we implemented a multimodal, multivariate approach to investigate how interactions between individual differences in structural integrity and functional connectivity relate to episodic memory performance in healthy aging. In a sample of younger (N = 111; mean age, 22.11 years) and older (N = 78; mean age, 67.29 years) adults, we analyzed structural MRI and multiecho resting-state fMRI data. Participants completed measures of list recall (free recall of words from a list), associative memory (cued recall of paired words), and source memory (cued recall of the trial type, or the sensory modality in which a word was presented). The findings revealed that greater structural integrity of the posterior hippocampus and middle frontal gyrus were linked with a pattern of increased within-network connectivity, which together were related to better associative and source memory in older adulthood. Critically, older adults displayed better memory performance in the context of decreased hippocampal volumes when structural differences were accompanied by functional reorganization. This functional reorganization was characterized by a pruning of connections between the hippocampus and the limbic and frontoparietal control networks. Our work provides insight into the neural mechanisms that underlie age-related compensation, revealing that the functional architecture associated with better memory performance in healthy aging is tied to the structural integrity of the hippocampus and prefrontal cortex.

Alzheimer's disease detection using data fusion with a deep supervised encoder

Alzheimer's disease (AD) is affecting a growing number of individuals. As a result, there is a pressing need for accurate and early diagnosis methods. This study aims to achieve this goal by developing an optimal data analysis strategy to enhance computational diagnosis. Although various modalities of AD diagnostic data are collected, past research on computational methods of AD diagnosis has mainly focused on using single-modal inputs. We hypothesize that integrating, or "fusing," various data modalities as inputs to prediction models could enhance diagnostic accuracy by offering a more comprehensive view of an individual's health profile. However, a potential challenge arises as this fusion of multiple modalities may result in significantly higher dimensional data. We hypothesize that employing suitable dimensionality reduction methods across heterogeneous modalities would not only help diagnosis models extract latent information but also enhance accuracy. Therefore, it is imperative to identify optimal strategies for both data fusion and dimensionality reduction. In this paper, we have conducted a comprehensive comparison of over 80 statistical machine learning methods, considering various classifiers, dimensionality reduction techniques, and data fusion strategies to assess our hypotheses. Specifically, we have explored three primary strategies: (1) Simple data fusion, which involves straightforward concatenation (fusion) of datasets before inputting them into a classifier; (2) Early data fusion, in which datasets are concatenated first, and then a dimensionality reduction technique is applied before feeding the resulting data into a classifier; and (3) Intermediate data fusion, in which dimensionality reduction methods are applied individually to each dataset before concatenating them to construct a classifier. For dimensionality reduction, we have explored several commonly-used techniques such as principal component analysis (PCA), autoencoder (AE), and LASSO. Additionally, we have implemented a new dimensionality-reduction method called the supervised encoder (SE), which involves slight modifications to standard deep neural networks. Our results show that SE substantially improves prediction accuracy compared to PCA, AE, and LASSO, especially in combination with intermediate fusion for multiclass diagnosis prediction.

Defining exceptional cognition in older adults: A systematic review of cognitive super-ageing

OBJECTIVE

A consistent approach to defining cognitive super-ageing is needed to increase the value of research insights that may be gained from studying this population including ageing well and preventing and treating neurodegenerative conditions. This review aims to evaluate the existing definitions of 'super-ageing' with a focus on cognition.

METHODS

A systematic literature search was conducted across PubMed, Embase, Web of Science, Scopus, PsycINFO and Google Scholar from inception to 24 July 2023.

RESULTS

Of 44 English language studies that defined super-ageing from a cognitive perspective in older adults (60-97 years), most (n = 33) were based on preserved verbal episodic memory performance comparable to that of younger adult in age range 16-65 years. Eleven studies defined super-agers as the top cognitive performers for their age group based upon standard deviations or percentiles above the population mean. Only nine studies included longitudinal cognitive performance in their definitions.

CONCLUSIONS

Equivalent cognitive abilities to younger adults, exceptional cognition for age and a lack of cognitive deterioration over time are all meaningful constructs and may provide different insights into cognitive ageing. Using these criteria in combination or individually to define super-agers, with a clear rationale for which elements have been selected, could be fit for purpose depending on the research question. However, major discrepancies including the age range of super-agers and comparator groups and the choice of cognitive domains assessed should be addressed to reach some consensus in the field.

Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features

Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.

Neural similarity across task load relates to cognitive reserve and brain maintenance measures on the Letter Sternberg task: a longitudinal study

The aging process is characterized by change across several measures that index cognitive status and brain integrity. In the present study, 54 cognitively-healthy younger and older adults, were analyzed, longitudinally, on a verbal working memory task to investigate the effect of brain maintenance (i.e., cortical thickness) and cognitive reserve (i.e., NART IQ as proxy) factors on a derived measure of neural efficiency. Participants were scanned using fMRI while presented with the Letter Sternberg task, a verbal working memory task consisting of encoding, maintenance and retrieval phases, where cognitive load is manipulated by varying the number of presented items (i.e., between one and six letters). Via correlation analysis, we looked at region-level and whole-brain relationships between load levels within each phase and then computed a global task measure, what we term phase specificity, to analyze how similar neural responses were across load levels within each phase compared to between each phase. We found that longitudinal change in phase specificity was positively related to longitudinal change in cortical thickness, at both the whole-brain and regional level. Additionally, baseline NART IQ was positively related to longitudinal change in phase specificity over time. Furthermore, we found a longitudinal effect of sex on change in phase specificity, such that females displayed higher phase specificity over time. Cross-sectional findings aligned with longitudinal findings, with the notable exception of behavioral performance being positively linked to phase specificity cross-sectionally at baseline. Taken together, our findings suggest that phase specificity positively relates to brain maintenance and reserve factors and should be better investigated as a measure of neural efficiency.

Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions

It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.