Age-related gray matter volume changes in the brain during non-elderly adulthood

Influence of the Onset of Menopause on the Risk of Developing Alzheimer's Disease

Menopause is a natural phase marked by the permanent cessation of menstrual cycles, occurring when the production of reproductive hormones from the ovaries stops for at least 12 consecutive months. Studies have suggested a potential connection between menopause and a heightened risk of developing Alzheimer's disease (AD), underscoring the significant role of reduced estrogen levels in the development of AD. Estrogen plays a crucial role in brain metabolism, influencing energy metabolism, synaptic plasticity, and cognitive functions. The cognitive benefits associated with hormone replacement therapy (HRT) are believed to be linked to estrogen's neuroprotective effects, either through direct action on the brain or indirectly by improving cardiovascular health. Extensive literature supports the positive impact of estrogen on brain cells. While the physiological effects of estrogen on the brain have not been consistently replicated in clinical trials, further research is crucial to provide more definitive recommendations to menopausal patients regarding the influence of HRT on AD. This review aims to comprehensively explore the interplay between menopause and AD, as well as the potential of HRT to mitigate cognitive decline in post-menopausal individuals.

MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters

BACKGROUND

Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases.

PURPOSE

This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.

METHODS

Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm.

RESULTS

With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.

CONCLUSIONS

This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.

Gray matter volume in women with the BRCA mutation with and without ovarian removal: evidence for increased risk of late-life Alzheimer's disease or dementia

OBJECTIVE

Ovarian removal prior to spontaneous/natural menopause (SM) is associated with increased risk of late life dementias including Alzheimer's disease. This increased risk may be related to the sudden and early loss of endogenous estradiol. Women with breast cancer gene mutations (BRCAm) are counseled to undergo oophorectomy prior to SM to significantly reduce their risk of developing breast, ovarian, and cervical cancers. There is limited evidence of the neurological effects of ovarian removal prior to the age of SM showing women without the BRCAm had cortical thinning in medial temporal lobe structures. A second study in women with BRCAm and bilateral salpingo-oophorectomy (BSO) noted changes in cognition.

METHODS

The present, cross-sectional study examined whole-brain differences in gray matter (GM) volume using high-resolution, quantitative magnetic resonance imaging in women with BRCAm and intact ovaries (BRCA-preBSO [study cohort with BRCA mutation prior to oophorectomy]; n = 9) and after surgery with (BSO + estradiol-based therapy [ERT]; n = 10) and without (BSO; n = 10) postsurgical estradiol hormone therapy compared with age-matched women (age-matched controls; n = 10) with their ovaries.

RESULTS

The BRCA-preBSO and BSO groups showed significantly lower GM volume in the left medial temporal and frontal lobe structures. BSO + ERT exhibited few areas of lower GM volume compared with age-matched controls. Novel to this study, we also observed that all three BRCAm groups exhibited significantly higher GM volume compared with age-matched controls, suggesting continued plasticity.

CONCLUSIONS

The present study provides evidence, through lower GM volume, to support both the possibility that the BRCAm, alone, and early life BSO may play a role in increasing the risk for late-life dementia. At least for BRCAm with BSO, postsurgical ERT seems to ameliorate GM losses.

The usefulness of combined analysis using CIScore and VSRAD parameters for differentiating between dementia with Lewy body and Alzheimer's disease

PURPOSE

The Cingulate Island score (CIScore) is useful index for differentiating between dementia with Lewy body (DLB) and Alzheimer's disease (AD) using regional cerebral blood flow (rCBF) SPECT. The Z score standing for medial temporal lobe (MTL) atrophy and the ratio of Z score between dorsal brain stem (DBS) to MTL are useful indices for differentiating between DLB and AD using MRI with VSRAD. The current study investigated the diagnostic ability by the combined use of rCBF SPECT and MRI in the differentiation between AD and DLB.

MATERIALS AND METHODS

In cases with 42 AD and 28 DLB undertaken Tc-99m-ECD SPECT and MRI, we analyzed differential diagnostic ability between AD and DLB among following conditions by single or combined settings. Namely, they were (1) the CIScore as a parameter of rCBF SPECT (DLB ≦ 0.25), (2) Z score value of MTL atrophy (DLB ≦ 2.05), (3) the ratio of Z score of DBS to medial temporal gray matter as a parameter of brain atrophy using VSRAD (DLB ≧ 0.38). Also, we analyzed them both including and omitting the elderly (over 75 years old).

RESULTS

The accuracy of differential diagnosis in this condition was 74% for (1), 69% for (2), and 67% for (3). The accuracy by combination condition was 84% for (1) and (2), 81% for (1) and (3), and 67% for (2) and (3), respectively. The combination method by CIScore and the Z score of MTL showed the best accuracy. When we confined condition to ages younger than 75 years, the accuracy improved to 94% in the combination method.

CONCLUSION

The combined use of CIScore and Z score of MTL was suggested to be useful in the differential diagnosis between DLB and AD particularly in younger than 75 years old.

The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J

PURPOSE

Aging is a major societal concern due to age-related functional losses. Synapses are crucial components of neural circuits, and synaptic density could be a sensitive biomarker to evaluate brain function. [11C]UCB-J is a positron emission tomography (PET) ligand targeting synaptic vesicle glycoprotein 2A (SV2A), which can be used to evaluate brain synaptic density in vivo.

METHODS

We evaluated age-related changes in gray matter synaptic density, volume, and blood flow using [11C]UCB-J PET and magnetic resonance imaging (MRI) in a wide age range of 80 cognitive normal subjects (21-83 years old). Partial volume correction was applied to the PET data.

RESULTS

Significant age-related decreases were found in 13, two, and nine brain regions for volume, synaptic density, and blood flow, respectively. The prefrontal cortex showed the largest volume decline (4.9% reduction per decade: RPD), while the synaptic density loss was largest in the caudate (3.6% RPD) and medial occipital cortex (3.4% RPD). The reductions in caudate are consistent with previous SV2A PET studies and likely reflect that caudate is the site of nerve terminals for multiple major tracts that undergo substantial age-related neurodegeneration. There was a non-significant negative relationship between volume and synaptic density reductions in 16 gray matter regions.

CONCLUSION

MRI and [11]C-UCB-J PET showed age-related decreases of gray matter volume, synaptic density, and blood flow; however, the regional patterns of the reductions in volume and SV2A binding were different. Those patterns suggest that MR-based measures of GM volume may not be directly representative of synaptic density.

Brain age prediction across the human lifespan using multimodal MRI data

Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.

Intracranial Volume Is Driven by Both Genetics and Early Life Exposures: The SOL-INCA-MRI Study

Intracranial volume (ICV) reflects maximal brain development and is associated with later-life cognitive abilities. We quantified ICV among first- and second-generation Hispanic and Latino adults from the Study of Latinos-Investigation of Cognitive Aging - MRI (SOL-INCA-MRI), estimated ICV heritability, and tested its associations with previously reported genetic variants, both individually and as a genetic risk score (GRS). We also estimated the association of ICV with early life environmental measures: nativity or age of immigration and parental education. The estimated heritability of ICV was 19% (95% CI, 0.1%-56%) in n=1781 unrelated SOL-INCA-MRI individuals. Four of 10 tested genetic variants were associated with ICV and an increase of 1 SD of the ICV-GRS was associated with an increase of 10.37 cm3 in the ICV (95% CI, 5.29-15.45). Compared to being born in the continental United States, immigrating to the United States at age 11 years or older was associated with 24 cm3 smaller ICV (95% CI, -39.97 to -8.06). Compared to both parents having less than high-school education, at least 1 parent completing high-school education was associated with 15.4 cm3 greater ICV (95% CI, 4.46-26.39). These data confirm the importance of early life health on brain development.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study

We investigated age-related trends in the topology and hierarchical organization of brain structural and functional networks using diffusion-weighted imaging and resting-state fMRI data from a large cohort of healthy aging adults. At the cross-modal level, we explored age-related patterns in the RC involvement of different functional subsystems using a high-resolution functional parcellation. We further assessed age-related differences in the structure-function coupling as well as the network vulnerability to damage to rich club connectivity. Regardless of age, the structural and functional brain networks exhibited a rich club organization and small-world topology. In older individuals, we observed reduced integration and segregation within the frontal-occipital regions and the cerebellum along the brain's medial axis. Additionally, functional brain networks displayed decreased integration and increased segregation in the prefrontal, centrotemporal, and occipital regions, and the cerebellum. In older subjects, structural networks also exhibited decreased within-network and increased between-network RC connectivity. Furthermore, both within-network and between-network RC connectivity decreased in functional networks with age. An age-related decline in structure-function coupling was observed within sensory-motor, cognitive, and subcortical networks. The structural network exhibited greater vulnerability to damage to RC connectivity within the language-auditory, visual, and subcortical networks. Similarly, for functional networks, increased vulnerability was observed with damage to RC connectivity in the cerebellum, language-auditory, and sensory-motor networks. Overall, the network vulnerability decreased significantly in subjects older than 70 in both networks. Our findings underscore significant age-related differences in both brain functional and structural RC connectivity, with distinct patterns observed across the adult lifespan.

Reduced Age-Related Gray Matter Loss in the Orbitofrontal Cortex in Long-Term Meditators

The orbitofrontal cortex (OFC) is a functionally heterogeneous brain region contributing to mental processes relating to meditation practices. The OFC has been reported to decline in volume with increasing age and differs in volume between meditation practitioners and non-practitioners. We hypothesized that the age-related decline of the OFC is diminished in meditation practitioners. We tested this hypothesis in a sample of 50 long-term meditators and 50 matched controls by correlating chronological age with regional gray matter volumes of the left and right OFC, as well as in seven left and right cytoarchitectonically defined subregions of the OFC (Fo1-Fo7). In both meditators and controls, we observed a negative relationship between age and OFC (sub)volumes, indicating that older participants have smaller OFC volumes. However, in meditators, the age-related decline was less steep compared to controls. These age-related differences reached significance for left and right Fo2, Fo3, Fo4, and Fo7, as well as left Fo5 and right Fo6. Since different subregions of the OFC are associated with distinct brain functions, further investigations are required to explore the functional implications of these findings in the context of meditation and the aging brain.

The Effects of Animation on the Guessability of Universal Healthcare Symbols for Middle-Aged and Older Adults

OBJECTIVE

The purpose of this study was to investigate whether animation can help to improve the comprehension of universal healthcare symbols for middle-aged and older adults.

BACKGROUND

The Hablamos Juntos (HJ) healthcare symbol system is a set of widely used universal healthcare symbols that were developed in the United States. Some studies indicated that HJ healthcare symbols are not well-understood by users in non-English-speaking areas. Other studies found that animations can improve users' comprehension of complex symbols. Thus, we wanted to test whether animation could help to improve users' comprehension of HJ symbols.

METHODS

The participants included 40 middle-aged and 40 older adults in Taiwan. We redesigned the 12 HJ symbols into three visual formats-static, basic animation, and detailed animation-and compared them to find which best improved the participants' guessability scores.

RESULTS

(1) Middle-aged adults' comprehension of static and basic animated symbols was significantly better than that of older adults, but there was no significant difference in the guessability scores between the two age groups in terms of detailed animated symbols; (2) In general, both basic animation and detailed animation significantly improved the guessability score, but the effect with detailed animation was significantly greater than that with basic animation; (3) Older women were more receptive to detailed animation and showed better guessing performance.

CONCLUSION

Detailed animation contains more details and provides a more complete explanation of the concept of the static symbols, helping to improve the comprehension of HJ symbols for middle-aged and older adult users.

APPLICATION

Our findings provide a reference for the possibility of new style symbol design in the digital and aging era, which can be applied to improve symbol comprehension.

Age-related vulnerability of the human brain connectome

Multifactorial models integrating brain variables at multiple scales are warranted to investigate aging and its relationship with neurodegeneration. Our aim was to evaluate how aging affects functional connectivity of pivotal regions of the human brain connectome (i.e., hubs), which represent potential vulnerability 'stations' to aging, and whether such effects influence the functional and structural changes of the whole brain. We combined the information of the functional connectome vulnerability, studied through an innovative graph-analysis approach (stepwise functional connectivity), with brain cortical thinning in aging. Using data from 128 cognitively normal participants (aged 20-85 years), we firstly investigated the topological functional network organization in the optimal healthy condition (i.e., young adults) and observed that fronto-temporo-parietal hubs showed a highly direct functional connectivity with themselves and among each other, while occipital hubs showed a direct functional connectivity within occipital regions and sensorimotor areas. Subsequently, we modeled cortical thickness changes over lifespan, revealing that fronto-temporo-parietal hubs were among the brain regions that changed the most, whereas occipital hubs showed a quite spared cortical thickness across ages. Finally, we found that cortical regions highly functionally linked to the fronto-temporo-parietal hubs in healthy adults were characterized by the greatest cortical thinning along the lifespan, demonstrating that the topology and geometry of hub functional connectome govern the region-specific structural alterations of the brain regions.

Association between sleep slow-wave activity and in-vivo estimates of myelin in healthy young men

Sleep has been suggested to contribute to myelinogenesis and associated structural changes in the brain. As a principal hallmark of sleep, slow-wave activity (SWA) is homeostatically regulated but also differs between individuals. Besides its homeostatic function, SWA topography is suggested to reflect processes of brain maturation. Here, we assessed whether interindividual differences in sleep SWA and its homeostatic response to sleep manipulations are associated with in-vivo myelin estimates in a sample of healthy young men. Two hundred twenty-six participants (18-31 y.) underwent an in-lab protocol in which SWA was assessed at baseline (BAS), after sleep deprivation (high homeostatic sleep pressure, HSP) and after sleep saturation (low homeostatic sleep pressure, LSP). Early-night frontal SWA, the frontal-occipital SWA ratio, as well as the overnight exponential SWA decay were computed over sleep conditions. Semi-quantitative magnetization transfer saturation maps (MTsat), providing markers for myelin content, were acquired during a separate laboratory visit. Early-night frontal SWA was negatively associated with regional myelin estimates in the temporal portion of the inferior longitudinal fasciculus. By contrast, neither the responsiveness of SWA to sleep saturation or deprivation, its overnight dynamics, nor the frontal/occipital SWA ratio were associated with brain structural indices. Our results indicate that frontal SWA generation tracks inter-individual differences in continued structural brain re-organization during early adulthood. This stage of life is not only characterized by ongoing region-specific changes in myelin content, but also by a sharp decrease and a shift towards frontal predominance in SWA generation.

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.

Age- and sex-related differences in baboon (Papio anubis) gray matter covariation

Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.

HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation

A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzheimer's disease (AD). Particularly, the HGM-cNet cascaded two identical convolutional neural networks (CNN), where each CNN was devised by incorporating Attention Block, Residual Block, and DropBlock into the typical encoder-decoder architecture. The two CNNs were skip-connected between encoder components at each scale. The adoption of the cascaded deep learning framework was to conveniently incorporate the HGM probability map with the feature map generated by the first CNN. Experiments on 135T1-weighted MRI scans and manual hippocampal labels from publicly available ADNI-HarP dataset demonstrated that the proposed HGM-cNet outperformed seven multi-atlas-based hippocampus segmentation methods and six deep learning methods under comparison in most evaluation metrics. The Dice (average > 0.89 for both left and right hippocampus) was increased by around or more than 1% over other methods. The HGM-cNet also achieved a superior hippocampus segmentation performance in each group of cognitive normal, mild cognitive impairment, and AD. The stability, conveniences and generalizability of the cascaded deep learning framework with integrated HGM probability map in improving hippocampus segmentation was validated by replacing the proposed CNN with 3D-UNet, Atten-UNet, HippoDeep, QuickNet, DeepHarp, and TransBTS models. The integration of the HGM probability map in the cascaded deep learning framework was also demonstrated to facilitate capturing hippocampal atrophy more accurately than alternative methods in AD analysis. The codes are publicly available at https://github.com/Liu1436510768/HGM-cNet.git.

Neuroimaging and verbal memory assessment in healthy aging adults using a portable low-field MRI scanner and a web-based platform: results from a proof-of-concept population-based cross-section study

Consumer wearables and health monitors, internet-based health and cognitive assessments, and at-home biosample (e.g., saliva and capillary blood) collection kits are increasingly used by public health researchers for large population-based studies without requiring intensive in-person visits. Alongside reduced participant time burden, remote and virtual data collection allows the participation of individuals who live long distances from hospital or university research centers, or who lack access to transportation. Unfortunately, studies that include magnetic resonance neuroimaging are challenging to perform remotely given the infrastructure requirements of MRI scanners, and, as a result, they often omit socially, economically, and educationally disadvantaged individuals. Lower field strength systems (< 100 mT) offer the potential to perform neuroimaging at a participant's home, enabling more accessible and equitable research. Here we report the first use of a low-field MRI "scan van" with an online assessment of paired-associate learning (PAL) to examine associations between brain morphometry and verbal memory performance. In a sample of 67 individuals, 18-93 years of age, imaged at or near their home, we show expected white and gray matter volume trends with age and find significant (p < 0.05 FWE) associations between PAL performance and hippocampus, amygdala, caudate, and thalamic volumes. High-quality data were acquired in 93% of individuals, and at-home scanning was preferred by all individuals with prior MRI at a hospital or research setting. Results demonstrate the feasibility of remote neuroimaging and cognitive data collection, with important implications for engaging traditionally under-represented communities in neuroimaging research.

Age- and sex-related differences in cortical morphology and their relationships with cognitive performance in healthy middle-aged and older adults

Background

The impacts of age and sex on brain structures related to cognitive function may be important for understanding the role of aging in Alzheimer disease for both sexes. We intended to investigate the age and sex differences of cortical morphology in middle-aged and older adults and their relationships with the decline of cognitive function.

Methods

In this cross-sectional study, we examined the cortical morphology in 204 healthy middle-aged and older adult participants aged 45 to 89 years using structural magnetic resonance imaging (sMRI) data from the Dallas Lifespan Brain Study data set. Brain cortical thickness, surface complexity, and gyrification index were analyzed through a completely automated surface-based morphometric analysis using the CAT12 toolbox. Furthermore, we explored the correlation between cortical morphology differences and test scores for processing speed and working memory.

Results

There were no significant interactions of age and sex with cortical thickness, fractal dimension, or gyrification index. Rather, we found that both males and females showed age-related decreases in cortical thickness, fractal dimension, and gyrification index. There were significant sex differences in the fractal dimension in middle-aged participants and the gyrification index in older adult participants. In addition, there were significant positive correlations between the cortical thickness of the right superior frontal gyrus and Wechsler Adult Intelligence Scale (WAIS)-III Letter-Number Sequencing test scores in males (r=0.394; P<0.001; 95% CI for r values 0.216-0.577) and females (r=0.344; P<0.001; 95% CI for r values 0.197-0.491), respectively. Furthermore, a significant relationship between the gyrification index of the right supramarginal gyrus (SupraMG) and WAIS-III Digit Symbol test scores was observed in older adult participants (r=0.375; P<0.001; 95% CI for r values 0.203-0.522).

Conclusions

The results suggest that, compared with males, females have more extensive differences in cortical morphology. The gyrification index of the right SupraMG can be used as an imaging marker of sexual cognitive differences between males and females in older adults. This study helps to further understand sex differences in the aging of the brain and cognition.

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.

Age-associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study

Cortical morphology changes with ageing and age-related neurodegenerative diseases. Previous studies suggest that the age effect is more pronounced in the frontal lobe. However, our knowledge of structural complexity changes in male and female brains is still limited. We measured cortical ribbon complexity through fractal dimension (FD) analysis at the hemisphere and lobe level in 7010 individuals from the UK Biobank imaging cohort to study age-related sex differences (3332 males, age ranged 45-79 years). FD decreases significantly with age and sexual dimorphism exists. With correction for brain size, females showed higher complexity in the left hemisphere and left and right parietal lobes whereas males showed higher complexity in the right temporal and left and right occipital lobes. A nonlinear age effect was observed in the left and right frontal, and right temporal lobes. Differential patterns of age effects were observed in both sexes with relatively more age-affected regions in males. Significantly higher rightward asymmetries at hemisphere, frontal, parietal, and occipital lobe level and higher leftward asymmetry in temporal lobe were observed. There was no age-by-sex-by asymmetry interaction in any region. When controlling for brain size, the leftward hemispheric, and temporal lobe asymmetry decreased with age. Males had significantly lower asymmetry between hemispheres and higher asymmetry in the parietal and occipital lobes than females. This work provides distinct patterns of age-related sex and asymmetry differences that can aid in the future development of sex-specific models of the normal brain to ascribe cognitive functional significance of these patterns in ageing.

Brain and cardiovascular-related changes are associated with aging, hypertension, and atrial fibrillation

PURPOSE

The neural pathways in which the brain regulates the cardiovascular system is via sympathetic and parasympathetic control of the heart and sympathetic control of the systemic vasculature. Various cortical and sub-cortical sites are involved, but how these critical brain regions for cardiovascular control are altered in healthy aging and other risk conditions that may contribute to cardiovascular disease is uncertain.

METHODS

Here we review the functional and structural brain changes in healthy aging, hypertension, and atrial fibrillation - noting their potential influence on the autonomic nervous system and hence on cardiovascular control.

RESULTS

Evidence suggests that aging, hypertension, and atrial fibrillation are each associated with functional and structural changes in specific areas of the central nervous system involved in autonomic control. Increased muscle sympathetic nerve activity (MSNA) and significant alterations in the brain regions involved in the default mode network are commonly reported in aging, hypertension, and atrial fibrillation.

CONCLUSIONS

Further studies using functional and structural magnetic resonance imaging (MRI) coupled with autonomic nerve activity in healthy aging, hypertension, and atrial fibrillation promise to reveal the underlying brain circuitry modulating the abnormal sympathetic nerve activity in these conditions. This understanding will guide future therapies to rectify dysregulation of autonomic and cardiovascular control by the brain.

Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume

The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and their interaction network, using interpretable machine learning models for individuals. Our goal is not only to predict age accurately but more importantly, to explore the relationship between GMV and age. In addition to targeting each individual, we also investigate the dynamic properties of gray matter and their interaction network with individual age. The results show that the mean absolute error (MAE) of age prediction is 7.95 years. More notably, specific locations of gray matter and their interactions play different roles in age, and these roles change dynamically with age. The proposed method is a data-driven approach, which provides a new way to study aging mechanisms and even to diagnose degenerative brain diseases.

Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants

Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.

Brain age in chronic traumatic brain injury

Traumatic brain injury (TBI) is associated with greater 'brain age' that may be caused by atrophy in grey and white matter. Here, we investigated 'brain age' in a chronic TBI (≥10 years) sample. We examined whether 'brain age' increases with years post injury, and whether it is associated with injury severity, cognition and functional outcome. We recruited 102 participants with moderate to severe TBI aged between 40 and 85 years. TBI participants were assessed on average 22 years post-injury. Seventy-seven healthy controls were also recruited. Participants' 'brain age' was determined using T1-weighted MRI images. TBI participants were estimated to have greater 'brain age' compared to healthy controls. 'Brain age' gap was unrelated to time since injury or long-term functional outcome on the Glasgow Outcome Scale-Extended. Greater brain age was associated with greater injury severity measured by post traumatic amnesia duration and Glasgow Coma Scale. 'Brain age' was significantly and inversely associated with verbal memory, but unrelated to visual memory/ability and cognitive flexibility and processing speed. A longitudinal study is required to determine whether TBI leads to a 'one-off' change in 'brain age' or progressive ageing of the brain over time.

Test-retest reliability of multi-parametric maps (MPM) of brain microstructure

Multiparameter mapping (MPM) is a quantitative MRI protocol that is promising for studying microstructural brain changes in vivo with high specificity. Reliability values are an important prior knowledge for efficient study design and facilitating replicable findings in development, aging and neuroplasticity research. To explore longitudinal reliability of MPM we acquired the protocol in 31 healthy young subjects twice over a rescan interval of 4 weeks. We assessed the within-subject coefficient of variation (WCV), the between-subject coefficient of variation (BCV), and the intraclass correlation coefficient (ICC). Using these metrics, we investigated the reliability of (semi-) quantitative magnetization transfer saturation (MT_sat), proton density (PD), transversal relaxation (R2*) and longitudinal relaxation (R1). To increase relevance for explorative studies in development and training-induced plasticity, we assess reliability both on local voxel- as well as ROI-level. Finally, we disentangle contributions and interplay of within- and between-subject variability to ICC and assess the optimal degree of spatial smoothing applied to the data. We reveal evidence that voxelwise ICC reliability of MPMs is moderate to good with median values in cortex (subcortical GM): MT: 0.789 (0.447) PD: 0.553 (0.264) R1: 0.555 (0.369) R2*: 0.624 (0.477). The Gaussian smoothing kernel of 2 to 4 mm FWHM resulted in optimal reproducibility. We discuss these findings in the context of longitudinal intervention studies and the application to research designs in neuroimaging field.

Normal development of the brain: a survey of joint structural-functional brain studies

Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain's maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain's performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state and cognitive experiments. However, nonsignificant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were partially inconsistent with previous nonjoint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.

Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes

BACKGROUND

The human brain structure undergoes considerable changes throughout life. Cognitive function can be affected either negatively or positively. It is challenging to segregate normal brain aging from the accelerated one.

OBJECTIVE

To work out a descriptive model of brain structural and functional changes in normal aging.

MATERIALS AND METHODS

By using voxel-based morphometry and lesion segmentation along with linear statistics and machine learning (ML), we analyzed the structural changes in the major brain compartments and modeled the dynamics of neurofunctional performance throughout life. We studied sex differences in lifelong dynamics of brain volumetric data with Mann-Whitney U-test. We tested the hypothesis that performance in some cognitive domains might decline as a linear function of age while other domains might have a non-linear dependence on it. We compared the volumetric changes in the major brain compartments with the dynamics of psychophysiological performance in 4 age groups. Then, we tested linear models of structural and functional decline for significant differences between the slopes in age groups with the T-test.

RESULTS

White matter hyperintensities (WMH) are not the major structural determinant of the brain normal aging. They should be viewed as signs of a disease. There is a sex difference in the speed and/or in the onset of the gray matter atrophy. It either starts earlier or goes faster in males. Marked sex difference in the proportion of total cerebrospinal fluid (CSF) and intraventricular CSF (iCSF) justifies that elderly men are more prone to age-related brain atrophy than women of the same age.

CONCLUSION

The article gives an overview and description of the conceptual structural changes in the brain compartments. The obtained data justify distinct patterns of age-related changes in the cognitive functions. Cross-life slowing of decision-making may follow the linear tendency of enlargement of the interhemispheric fissure because the center of task switching and inhibitory control is allocated within the medial wall of the frontal cortex, and its atrophy accounts for the expansion of the fissure. Free online tool at https://med-predict.com illustrates the tests and study results.

The Aged Striatum: Evidence of Molecular and Structural Changes Using a Longitudinal Multimodal Approach in Mice

To study the aging human brain requires significant resources and time. Thus, mice models of aging can provide insight into changes in brain biological functions at a fraction of the time when compared to humans. This study aims to explore changes in dopamine D₁ and D₂ receptor availability and of gray matter density in striatum during aging in mice and to evaluate whether longitudinal imaging in mice may serve as a model for normal brain aging to complement cross-sectional research in humans. Mice underwent repeated structural magnetic resonance imaging (sMRI), and [¹¹C]Raclopride and [¹¹C]SCH23390 positron emission tomography (PET) was performed on a subset of aging mice. PET and sMRI data were analyzed by binding potential (BP_ND), voxel- and tensor-based morphometry (VBM and TBM, respectively). Longitudinal PET revealed a significant reduction in striatal BP_ND for D₂ receptors over time, whereas no significant change was found for D₁ receptors. sMRI indicated a significant increase in modulated gray matter density (mGMD) over time in striatum, with limited clusters showing decreased mGMD. Mouse [¹¹C]Raclopride data is compatible with previous reports in human cross-sectional studies, suggesting that a natural loss of dopaminergic D₂ receptors in striatum can be assessed in mice, reflecting estimates from humans. No changes in D₁ were found, which may be attributed to altered [¹¹C]SCH23390 kinetics in anesthetized mice, suggesting that this tracer is not yet able to replicate human findings. sMRI revealed a significant increase in mGMD. Although contrary to expectations, this increase in modulated GM density may be attributed to an age-related increase in non-neuronal cells.

Regional Brain Volume, Brain Reserve and MMSE Performance in Healthy Aging From the NEUROAGE Cohort: Contributions of Sex, Education, and Depression Symptoms

Objective: The aim of this study was twofold. First, to investigate the relationship between age, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumes, brain reserve (BR), and specific regions of interest (ROIs) with global cognitive function in healthy older adults participating in a longitudinal study on aging in the island country of Cyprus. Second, to assess the contribution of important demographic and psychosocial factors on brain volume. Specifically, the effects of sex and years of education and the association between depression symptoms on brain volume were also explored in this Mediterranean cohort.

Methods: Eighty-seven healthy older adults (males = 37, females = 50) scoring ≥24 on the Mini-Mental State Examination (MMSE) were included, with a mean age of 72.75 years and a mean educational level of 10.48 years. The Geriatric Depression Scale was used to assess depression. T1-weighted magnetic resonance images were used to calculate global and regional volumes.

Results: Age was negatively correlated with GM, WM, BR, MMSE scores, and ROIs, including the hippocampus, amygdala, entorhinal cortex, prefrontal cortex, anterior cingulate gyrus, and positively with CSF. Higher MMSE scores positively correlated with GM volume. Women exhibited greater levels of depression than men. Depression was also negatively correlated with GM volume and MMSE scores. Men had greater ventricular size than women and participants with higher education had greater ventricular expansion than those with fewer years in education.

Conclusions: The reported structural changes provide evidence on the overlap between age-related brain changes and healthy cognitive aging and suggest that these age changes affect certain regions. Furthermore, sex, depressive symptomatology, and education are significant predictors of the aging brain. Brain reserve and higher education accommodate these changes and works against the development of clinical symptoms.

Manual Correction of Voxel Misclassifications in Mesiotemporal Structures Does Not Alter Brain-Behavioral Results in an Episodic Memory Task

Voxel-based morphometry (VBM) is an established method for assessing grey matter volumes across the brain. The quality of magnetic resonance imaging (MRI) and the chosen data preprocessing steps can affect the outcome of VBM analyses. We recognized a lack of publicly available and commonly used protocols, which indicates that standardized and optimized preprocessing protocols are needed. This paper focuses on the time- and resource-consuming manual correction of misclassifications of grey matter voxels in cortical structures important in Alzheimer's dementia. A total of 126 individuals, including 63 patients with very early Alzheimer's disease and 63 cognitively normal participants, received thorough neuropsychological testing and 3-Tesla MRI. Automated preprocessing of T1 MPRAGE images was performed, and misclassifications of grey matter voxels were manually identified and corrected. In a second run, the manual correction step was skipped. Multiple regression analyses using DARTEL in SPM8 were then conducted with the manually corrected and uncorrected sample, respectively. Manual correction of voxel misclassifications did not have a major impact on the correlation between episodic memory performance and structural brain imaging results. We conclude that, although performing all preprocessing steps remains the gold standard, skipping manual correction of voxel misclassifications is permitted when investigating populations on the Alzheimer's disease spectrum.

The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures

Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual’s brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.

Aging affects steaks more than knives: Evidence that the processing of words related to motor skills is relatively spared in aging

Lexical-processing declines are a hallmark of aging. However, the extent of these declines may vary as a function of different factors. Motivated by findings from neurodegenerative diseases and healthy aging, we tested whether 'motor-relatedness' (the degree to which words are associated with particular human body movements) might moderate such declines. We investigated this question by examining data from three experiments. The experiments were carried out in different languages (Dutch, German, English) using different tasks (lexical decision, picture naming), and probed verbs and nouns, in all cases controlling for potentially confounding variables (e.g., frequency, age-of-acquisition, imageability). Whereas 'non-motor words' (e.g., steak) showed age-related performance decreases in all three experiments, 'motor words' (e.g., knife) yielded either smaller decreases (in one experiment) or no decreases (in two experiments). The findings suggest that motor-relatedness can attenuate or even prevent age-related lexical declines, perhaps due to the relative sparing of neural circuitry underlying such words.

Brain volume changes after long-term injectable opioid treatment: A longitudinal voxel-based morphometry study

Clinical research has demonstrated the efficacy of injectable opioid treatment for long-term, treatment-refractory opioid-dependent patients. It has been hypothesized that compulsive drug use is particularly associated with neuroplasticity changes in the networks corresponding to withdrawal/negative affect and preoccupation/anticipation rather than binge/intoxication. However, as yet, no study has investigated the effect of long-term opioid treatment on key regions within these networks. Magnetic resonance imaging (MRI) was used to assess brain volumes changes during long-term (approximately 9 years) injectable opioid agonist treatment with diacetylmorphine (DAM) in 22 patients with opioid use disorder. Voxel-based morphometry was applied to detect volumetric changes within the networks of binge/intoxication (ventral/dorsal striatum, globus pallidus and thalamus), withdrawal/negative affect (amygdala and ventral striatum) and preoccupation/anticipation (hippocampus, orbitofrontal and anterior cingulate cortex). The relationships between significant volume changes and features of opioid use disorder were tested using Pearson correlation. Long-term opioid agonist treatment was associated with the enlargement of the right caudate nucleus, which was related to the duration of opioid use disorder. In contrast, reduced volume in the right amygdala, anterior cingulate cortex and orbitofrontal cortex were found that were related to opioid dose, onset of opioid consumption and state anxiety. These findings suggest that long-term opioid agonist treatment is related to structural changes in key brain regions underlying binge/intoxication, withdrawal/negative affect and preoccupation/anticipation, suggesting sustained interaction between these systems.

Prefrontal cortex activation during dual-task walking in older adults is moderated by thickness of several cortical regions

Dual tasking, a defined facet of executive control processes, is subserved, in part, by the prefrontal cortex (PFC). Previous functional near-infrared spectroscopy (fNIRS) studies revealed elevated PFC oxygenated hemoglobin (HbO₂) under Dual-Task-Walk (DTW) compared to Single-Task Walk (STW) conditions. Based on the concept of neural inefficiency (i.e., greater activation coupled with similar or worse performance), we hypothesized that decreased cortical thickness across multiple brain regions would be associated with greater HbO₂ increases from STW to DTW. Participants were 55 healthy community-dwelling older adults, whose cortical thickness was measured via MRI. HbO₂ levels in the PFC, measured via fNIRS, were assessed during active walking under STW and DTW conditions. Statistical analyses were adjusted for demographics and behavioral performance. Linear mixed-effects models revealed that the increase in HbO₂ from STW to DTW was moderated by cortical thickness in several regions. Specifically, thinner cortex in specific regions of the frontal, parietal, temporal, and occipital lobes, cingulate cortex, and insula was associated with greater increases in HbO₂ levels from single to dual-task walking. In conclusion, participants with thinner cortex in regions implicated in higher order control of walking employed greater neural resources, as measured by increased HbO₂, in the PFC during DTW, without demonstrating benefits to behavioral performance. To our knowledge, this is the first study to examine cortical thickness as a marker of neural inefficiency during active walking.

In vivo biomarkers of structural and functional brain development and aging in humans

Brain aging is a major determinant of aging. Along with the aging population, prevalence of neurodegenerative diseases is increasing, therewith placing economic and social burden on individuals and society. Individual rates of brain aging are shaped by genetics, epigenetics, and prenatal environmental. Biomarkers of biological brain aging are needed to predict individual trajectories of aging and the risk for age-associated neurological impairments for developing early preventive and interventional measures. We review current advances of in vivo biomarkers predicting individual brain age. Telomere length and epigenetic clock, two important biomarkers that are closely related to the mechanistic aging process, have only poor deterministic and predictive accuracy regarding individual brain aging due to their high intra- and interindividual variability. Phenotype-related biomarkers of global cognitive function and brain structure provide a much closer correlation to age at the individual level. During fetal and perinatal life, autonomic activity is a unique functional marker of brain development. The cognitive and structural biomarkers also boast high diagnostic specificity for determining individual risks for neurodegenerative diseases.

Similar Theory of Mind Deficits in Community Dwelling Older Adults with Vascular Risk Profile and Patients with Mild Cognitive Impairment: The Case of Paradoxical Sarcasm Comprehension

Recent studies deal with disorders and deficits caused by vascular syndrome in efforts for prediction and prevention. Cardiovascular health declines with age due to vascular risk factors, and this leads to an increasing risk of cognitive decline. Mild cognitive impairment (MCI) is defined as the negative cognitive changes beyond what is expected in normal aging. The purpose of the study was to compare older adults with vascular risk factors (VRF), MCI patients, and healthy controls (HC) in social cognition and especially in theory of mind ability (ToM). The sample comprised a total of 109 adults, aged 50 to 85 years (M = 66.09, SD = 9.02). They were divided into three groups: (a) older adults with VRF, (b) MCI patients, and (c) healthy controls (HC). VRF and MCI did not differ significantly in age, educational level or gender as was the case with HC. Specifically, for assessing ToM, a social inference test was used, which was designed to measure sarcasm comprehension. Results showed that the performance of the VRF group and MCI patients is not differentiated, while HC performed higher compared to the other two groups. The findings may imply that the development of a vascular disorder affecting vessels of the brain is associated from its “first steps” to ToM decline, at least regarding specific aspects of it, such as paradoxical sarcasm understanding.

Impact of age on the circadian visual system and the sleep-wake cycle in mus musculus

Age plays a critical role in disease development and tolerance to cancer treatment, often leading to an increased risk of developing negative symptoms including sleep disturbances. Circadian rhythms and sleep become disrupted as organisms age. In this study, we explored the behavioral alterations in sleep, circadian rhythms, and masking using a novel video system and interrogate the long-term impact of age-based changes in the non-image forming visual pathway on brain anatomy. We demonstrated the feasibility and utility of the novel system and establish that older mice have disruptions in sleep, circadian rhythms, and masking behaviors that were associated with major negative volume alterations in the non-imaging forming visual system, critical for the induction and rhythmic expression of sleep. These results provide important insights into a mechanism, showing brain atrophy is linked to age in distinct non-image forming visual regions, which may predispose older individuals to developing circadian and sleep dysfunction when further challenged by disease or treatment.

Relationship Between Age and Cerebral Hemodynamic Response to Breath Holding: A Functional Near-Infrared Spectroscopy Study

Cerebrovascular reactivity (CVR) is routinely measured as a predictor of stroke in people with a high risk of ischemic attack. Neuroimaging techniques such as emission tomography, magnetic resonance imaging, and transcranial doppler are frequently used to measure CVR even though each technique has its limitations. Functional near-infrared spectroscopy (fNIRS), also based on the principle of neurovascular coupling, is relatively inexpensive, portable, and allows for the quantification of oxy- and deoxy-hemoglobin concentration changes at a high temporal resolution. This study examines the relationship between age and CVR using fNIRS in 45 young healthy adult participants aged 18-41 years (6 females, 26.64 ± 5.49 years) performing a simple breath holding task. Eighteen of the 45 participants were scanned again after a week to evaluate the feasibility of fNIRS in reliably measuring CVR. Results indicate (a) a negative relationship between age and hemodynamic measures of breath holding task in the sensorimotor cortex of 45 individuals and (b) widespread positive coactivation within medial sensorimotor regions and between medial sensorimotor regions with supplementary motor area and prefrontal cortex during breath holding with increasing age. The intraclass correlation coefficient (ICC) indicated only a low to fair/good reliability of the breath hold hemodynamic measures from sensorimotor and prefrontal cortices. However, the average hemodynamic response to breath holding from the two sessions were found to be temporally and spatially in correspondence. Future improvements in the sensitivity and reliability of fNIRS metrics could facilitate fNIRS-based assessment of cerebrovascular function as a potential clinical tool.

Reversal of Age-Related Neuronal Atrophy by α5-GABAA Receptor Positive Allosteric Modulation

Aging is associated with reduced brain volume, altered neural activity, and neuronal atrophy in cortical-like structures, comprising the frontal cortex and hippocampus, together contributing to cognitive impairments. Therapeutic efforts aimed at reversing these deficits have focused on excitatory or neurotrophic mechanisms, although recent findings show that reduced dendritic inhibition mediated by α5-subunit containing GABA-A receptors (α5-GABAA-Rs) occurs during aging and contributes to cognitive impairment. Here, we aimed to confirm the beneficial effect on working memory of augmenting α5-GABAA-R activity in old mice and tested its potential at reversing age-related neuronal atrophy. We show that GL-II-73, a novel ligand with positive allosteric modulatory activity at α5-GABAA-R (α5-PAM), increases dendritic branching complexity and spine numbers of cortical neurons in vitro. Using old mice, we confirm that α5-PAM reverses age-related working memory deficits and show that chronic treatment (3 months) significantly reverses age-related dendritic shrinkage and spine loss in frontal cortex and hippocampus. A subsequent 1-week treatment cessation (separate cohort) resulted in loss of efficacy on working memory but maintained morphological neurotrophic effects. Together, the results demonstrate the beneficial effect on working memory and neurotrophic efficacy of augmenting α5-GABAA-R function in old mice, suggesting symptomatic and disease-modifying potential in age-related brain disorders.

Dynamic changes of region-specific cortical features and scalp-to-cortex distance: implications for transcranial current stimulation modeling

BACKGROUND

Transcranial current stimulation in rehabilitation is a fast-growing field featured with computational and biophysical modeling. Cortical features and scalp-to-cortex distance (SCD) are key variables for determining the strength and distribution of the electric field, yet longitudinal studies able to capture these dynamic changes are missing. We sought to investigate and quantify the ageing effect on the morphometry and SCD of left primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) in normal ageing adults and mild cognitive impairment (MCI) converters.

METHODS

Baseline, 1-year and 3-year follow-up structural magnetic resonance imaging scans from normal ageing adults (n = 32), and MCI converters (n = 22) were drawn from the Open Access Series of Imaging Studies. We quantified the changes of the cortical features and SCDs of left M1 and DLPFC, including grey matter volume, white matter volume, cortical thickness, and folding. Head model was developed to simulate the impact of SCD on the electric field induced by transcranial current stimulation.

RESULTS

Pronounced ageing effect was found on the SCD of left DLPFC in MCI converters. The SCD change of left DLPFC from baseline to 3-year follow-up demonstrated better performance to discriminate MCI converters from normal ageing adults than the other morphometric measures. The strength of electric field was consequently decreased with SCD in MCI converters.

CONCLUSION

Ageing has a prominent, but differential effect on the region-specific SCD and cortical features in older adults with cognitive impairments. Our findings suggest that SCD, cortical thickness, and folding of the targeted regions could be used as valuable imaging markers when conducting transcranial brain stimulation in individuals with brain atrophy.

Role of Sirtuins in Modulating Neurodegeneration of the Enteric Nervous System and Central Nervous System

Neurodegeneration of the central and enteric nervous systems is a common feature of aging and aging-related diseases, and is accelerated in individuals with metabolic dysfunction including obesity and diabetes. The molecular mechanisms of neurodegeneration in both the CNS and ENS are overlapping. Sirtuins are an important family of histone deacetylases that are important for genome stability, cellular response to stress, and nutrient and hormone sensing. They are activated by calorie restriction (CR) and by the coenzyme, nicotinamide adenine dinucleotide (NAD+). Sirtuins, specifically the nuclear SIRT1 and mitochondrial SIRT3, have been shown to have predominantly neuroprotective roles in the CNS while the cytoplasmic sirtuin, SIRT2 is largely associated with neurodegeneration. A systematic study of sirtuins in the ENS and their effect on enteric neuronal growth and survival has not been conducted. Recent studies, however, also link sirtuins with important hormones such as leptin, ghrelin, melatonin, and serotonin which influence many important processes including satiety, mood, circadian rhythm, and gut homeostasis. In this review, we address emerging roles of sirtuins in modulating the metabolic challenges from aging, obesity, and diabetes that lead to neurodegeneration in the ENS and CNS. We also highlight a novel role for sirtuins along the microbiota-gut-brain axis in modulating neurodegeneration.

Predicting brain age with complex networks: From adolescence to adulthood

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.

Cognitive function and cerebellar morphometric changes relate to abnormal intra-cerebellar and cerebro-cerebellum functional connectivity in old adults

INTRODUCTION

Numerous structural studies have already reported volumetric reduction in cerebellum with aging. However, there are still limited studies particularly focusing on analysis of the cerebellar resting state FC in old adults. Even so, the least related studies were unable to include some important cerebellar lobules due to limited cerebellum segmentation methods.

OBJECTIVE

The purpose of this study is to explore cognitive function in relation to cerebellar lobular morphometry and cortico-cerebellar connectivity changes in old adults' lifespan by incorporating previously undetected cerebellar lobules.

METHODS

This study includes a sample of 264 old adults subdivided into five cognitively normal age groups (G1 through G5). Cerebellum Segmentation (CERES) software was used to obtain morphometric measures and brain masks of all the 24 cerebellar lobules. We then defined individual lobules as seed regions and mapped the whole-brain to get functional connectivity maps. To analyze age group differences in cortico-cerebellar connectivity and cerebellar lobular volume, we used one way ANOVA and post hoc analysis was performed for multiple comparisons using Bonferroni method.

RESULTS

Our results report cerebellar lobular volumetric reduction, disrupted intra-cerebellar connectivity and significant differences in cortico-cerebellar resting state FC across age groups. In addition, our results show that disrupted FC between left Crus-II and right ACC relates to well emotion regulation and cognitive decline and is associated with poor performance on TMT-B and logical memory tests in older adults.

CONCLUSION

Overall, our findings confirm that as humans get older and older, the cerebellar lobular volumes as well as the cortico-cerebellar functional connectivity are affected and hence reduces cognition.

Differential Impact of Cognitive Impairment in MCI Patients: A Case-Based Report

Mild cognitive impairment (MCI) traditionally refers to an intermediate stage between healthy individuals and early Alzheimer disease. Evidence shows grey and white matter volume changes and decrease in several executive functions, albeit the relation between cognitive performance and brain volume remains unclear. Here, we discuss 3 individual cases of MCI by investigating their MRI scans and cognitive test performance. We also recruited age-matched healthy older adults serving as gold standard for both grey and white matter volume and cognitive test outcomes. Our results show the impact of cognitive impairment on cognitive test performance and grey and white matter volumes, and the role played by cognitive and brain reserve on mitigating cognitive decline. Furthermore, we add evidence to previous studies by showing an increase in white matter volume compared to healthy controls, in all 3 patients. This pattern of increased white matter volume might help us to better understand the pathological mechanisms underlying MCI which in turn could contribute to future investigations.

Cognitive Ageing in Developing Societies: An Overview and a Cross-sectional Study on Young, Middle-aged and Older Adults in the Indian Context

Cognitive ageing in developing societies is marked with psychosocial issues such as education, occupation, lifestyle, social support, social interaction and exclusion that may affect cognitive–affective–behavioural changes with ageing. We also present a study based on cognitive profiling of young (N = 79), middle-aged (N = 54) and older adults (N = 43) in India, which examined learning and memory for verbal and visuospatial information, overall cognitive functions, subjective complaints about cognitive difficulties, neuropsychiatric problems, anxiety and emotion regulation. The study shows cognitive changes compared to young and comparable rate of learning and retrieval among middle-aged and older adults for verbal and visuospatial material, correlated with general cognitive ability. The subjective complaints were not correlated with the objective measures of cognitive functions, highlighting the importance of both to show early cognitive changes. The relationship between cognitive functions and emotion regulation or behavioural/emotional changes was observed for young and middle-aged adults but not for older adults. Findings are discussed in the context of the lifespan perspective of cognitive ageing, cognitive reserve, psychosocial environment and social–emotional selectivity theory.

Longitudinal Structural MRI in Neurologically Healthy Adults

BACKGROUND

Structural brain MRI measures are frequently examined in both healthy and clinical groups, so an understanding of how these measures vary over time is desirable.

PURPOSE

To test the stability of structural brain MRI measures over time.

POPULATION

In all, 112 healthy volunteers across four sites.

STUDY TYPE

Retrospective analysis of prospectively acquired data.

FIELD STRENGTH/SEQUENCE

3 T, magnetization prepared - rapid gradient echo, and single-shell diffusion sequence.

ASSESSMENT

Diffusion, cortical thickness, and volume data from the sensorimotor network were assessed for stability over time across 3 years. Two sites used a Siemens MRI scanner, two sites a Philips scanner.

STATISTICAL TESTS

The stability of structural measures across timepoints was assessed using intraclass correlation coefficients (ICC) for absolute agreement, cutoff ≥0.80, indicating high reliability. Mixed-factorial analysis of variance (ANOVA) was used to examine between-site and between-scanner type differences in individuals over time.

RESULTS

All cortical thickness and gray matter volume measures in the sensorimotor network, plus all diffusivity measures (fractional anisotropy plus mean, axial and radial diffusivities) for primary and premotor cortices, primary somatosensory thalamic connections, and the cortico-spinal tract met ICC. The majority of measures differed significantly between scanners, with a trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and volume measures than sites using Philips scanners.

DATA CONCLUSION

Levels of reliability over time for all tested structural MRI measures were generally high, indicating that any differences between measurements over time likely reflect underlying biological differences rather than inherent methodological variability.

LEVEL OF EVIDENCE

4.

TECHNICAL EFFICACY STAGE

1.

Age-related structural and functional variations in 5,967 individuals across the adult lifespan

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Quantitative evaluation of brain volume among elderly individuals in São Paulo, Brazil: a population-based study

Objective

To perform a quantitative analysis of the brain volume of elderly individuals in a population-based sample.

Materials and Methods

This was a radiological assessment and voxel-based quantitative analysis, with surface alignment, of 525 magnetic resonance imaging scans of individuals between 60 and 103 years of age who participated in the Saúde, Bem-estar e Envelhecimento (Health, Well-being, and Aging) study in the city of São Paulo, Brazil.

Results

We noted a median rate of reduction in total brain volume of 2.4% per decade after 60 years of age. Gray and white matter both showed volume reductions with age. The total brain volume/intracranial brain volume ratio differed between males and females.

Conclusion

We have corroborated the findings of studies conducted in the United States and Europe. The total brain volume/intracranial brain volume ratio is higher in men, representing a potential bias for the conventional radiological assessment of atrophy, which is typically based on the evaluation of the cerebrospinal fluid spaces.