A common brain network links development, aging, and vulnerability to disease

Structure-function coupling in macroscale human brain networks

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

Aging impairs reactive attentional control but not proactive distractor inhibition

Older adults tend to be more prone to distraction compared with young adults, and this age-related deficit has been attributed to a deficiency in inhibitory processing. However, recent findings challenge the notion that aging leads to global impairments in inhibition. To reconcile these mixed findings, we investigated how aging modulates multiple mechanisms of attentional control by tracking the timing and direction of eye movements. When engaged in feature-search mode and proactive distractor suppression, older adults made fewer first fixations to the target but inhibited the task-irrelevant salient distractor as effectively as did young adults. However, when engaged in singleton-search mode and required to reactively disengage from the distractor, older adults made significantly more first saccades toward the task-irrelevant salient distractor and showed increased fixation times in orienting to the target, longer dwell times on incorrect saccades, and increased saccadic reaction times compared with young adults. Our findings reveal that aging differently impairs attentional control depending on whether visual search requires proactive distractor suppression or reactive distractor disengagement. Furthermore, our oculomotor measures reveal both age-related deficits and age equivalence in various mechanisms of attention, including goal-directed orienting, selection history, disengagement, and distractor inhibition. These findings help explain why conclusions of age-related declines or age equivalence in mechanisms of attentional control are task specific and reveal that older adults do not exhibit global impairments in mechanisms of inhibition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Brain age of rhesus macaques over the lifespan

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

Population clustering of structural brain aging and its association with brain development

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the "last in, first out" mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Association between hearing ability and cortical morphology in the elderly: multiparametric mapping, cognitive relevance, and neurobiological underpinnings

BACKGROUND

Hearing impairment is a common condition in the elderly. However, a comprehensive understanding of its neural correlates is still lacking.

METHODS

We recruited 284 elderly adults who underwent structural MRI, magnetic resonance spectroscopy, audiometry, and cognitive assessments. Individual hearing abilities indexed by pure tone average (PTA) were correlated with multiple structural MRI-derived cortical morphological indices. For regions showing significant correlations, mediation analyses were performed to examine their role in the relationship between hearing ability and cognitive function. Finally, the correlation maps between hearing ability and cortical morphology were linked with publicly available connectomic gradient, transcriptomic, and neurotransmitter maps.

FINDINGS

Poorer hearing was related to cortical thickness (CT) reductions in widespread regions and gyrification index (GI) reductions in the right Area 52 and Insular Granular Complex. The GI in the right Area 52 mediated the relationship between hearing ability and executive function. This mediating effect was further modulated by glutamate and N-acetylaspartate levels in the right auditory region. The PTA-CT correlation map followed microstructural connectomic hierarchy, were related to genes involved in certain biological processes (e.g., glutamate metabolic process), cell types (e.g., excitatory neurons and astrocytes), and developmental stages (i.e., childhood to young adulthood), and covaried with dopamine receptor 1, dopamine transporter, and fluorodopa. The PTA-GI correlation map was related to 5-hydroxytryptamine receptor 2a.

INTERPRETATION

Poorer hearing is associated with cortical thinning and folding reductions, which may be engaged in the relationship between hearing impairment and cognitive decline in the elderly and have different neurobiological substrates.

FUNDING

See the Acknowledgements section.

Non-linear relationship between serum cholesterol levels and cognitive change among older people in the preclinical and prodromal stages of dementia: a retrospective longitudinal study in Taiwan

BACKGROUND

Adverse effects of rigorously lowering low-density lipoprotein cholesterol on cognition have been reported; therefore, we aimed to study the contribution of serum cholesterol in cognitive decline in older people with or without dementia.

METHODS

Cognitive function was assessed by the Cognitive Abilities Screening Instrument (CASI). We investigated associations between serum cholesterol with cognitive decline using multiple regressions controlling for the effects of demographics, vascular risk factors, and treatments.

RESULTS

Most associations between cholesterol and CASI scores could be explained by non-linear and inverted U-shaped relationships (R2 = 0.003-0.006, p < 0.016, Šidákcorrection). The relationships were most evident between changes in cholesterol and CASI scores in older people at the preclinical or prodromal stages of dementia (R2 = 0.02-0.064, p values < 0.016). There were no differences in level of changes in CASI scores between individuals in 1st decile and 10th decile groups of changes in cholesterol (p = 0.266-0.972). However, individuals in the 1st decile of triglyceride changes and with stable and normal cognitive functions showed significant improvement in CASI scores compared to those in the 10th decile (t(202) = 2.275, p values < 0.05).

CONCLUSION

These findings could implicate that rigorously lowering cholesterol may not be suitable for the prevention of cognitive decline among older people, especially among individuals in preclinical or prodromal stages of dementia.

Aging Brain from a Lifespan Perspective

Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.

A Review of the Current Status of Disease-Modifying Therapies and Prevention of Alzheimer's Disease

Alzheimer's disease is the most common form of dementia and includes cognitive, personality, and behavioral changes. The 2024 report from the Alzheimer's Association estimated that 6.9 million adults >65 years in the US are currently living with Alzheimer's disease. Modeling studies predict that this number will double by 2050, and associated healthcare costs will reach $1 trillion. In June 2021, regulatory approval of aducanumab, a humanized recombinant monoclonal antibody to amyloid ß, initially raised expectations for improved disease-modifying therapy. However, in February 2024, production of aducanumab and a post-marketing clinical trial ceased in the US due to the costs and limitations of aducanumab therapy. In March 2024, biobank data identified significant modifiable risk factors for Alzheimer's disease, including diabetes mellitus, exposure to nitrogen dioxide (a proxy for air pollution), and the frequency of alcohol intake. Therefore, modification of identifiable risk factors, combined with testing for disease-susceptibility genes, could be the most effective approach to reduce the incidence. This article aims to review the current status of disease-modifying therapies and prevention of Alzheimer's disease.

Finding the Words: How Does the Aging Brain Process Language? A Focused Review of Brain Connectivity and Compensatory Pathways

As people age, there is a natural decline in cognitive functioning and brain structure. However, the relationship between brain function and cognition in older adults is neither straightforward nor uniform. Instead, it is complex, influenced by multiple factors, and can vary considerably from one person to another. Reserve, compensation, and maintenance mechanisms may help explain why some older adults can maintain high levels of performance while others struggle. These mechanisms are often studied concerning memory and executive functions that are particularly sensitive to the effects of aging. However, language abilities can also be affected by age, with changes in production fluency. The impact of brain changes on language abilities needs to be further investigated to understand the dynamics and patterns of aging, especially successful aging. We previously modeled several compensatory profiles of language production and lexical access/retrieval in aging within the Lexical Access and Retrieval in Aging (LARA) model. In the present paper, we propose an extended version of the LARA model, called LARA-Connectivity (LARA-C), incorporating recent evidence on brain connectivity. Finally, we discuss factors that may influence the strategies implemented with aging. The LARA-C model can serve as a framework to understand individual performance and open avenues for possible personalized interventions.

Cross-sectional associations between multisensory impairment and brain volumes in older adults: Baltimore Longitudinal Study of Aging

Sensory impairment and brain atrophy is common among older adults, increasing the risk of dementia. Yet, the degree to which multiple co-occurring sensory impairments (MSI across vision, proprioception, vestibular function, olfactory, and hearing) are associated with brain morphometry remain unexplored. Data were from 208 cognitively unimpaired participants (mean age 72 ± 10 years; 59% women) enrolled in the Baltimore Longitudinal Study of Aging. Multiple linear regression models were used to estimate cross-sectional associations between MSI and regional brain imaging volumes. For each additional sensory impairment, there were associated lower orbitofrontal gyrus and entorhinal cortex volumes but higher caudate and putamen volumes. Participants with MSI had lower mean volumes in the superior frontal gyrus, orbitofrontal gyrus, superior parietal lobe, and precuneus compared to participants with < 2 impairments. While MSI was largely associated with lower brain volumes, our results suggest the possibility that MSI was associated with higher basal ganglia volumes. Longitudinal analyses are needed to evaluate the temporality and directionality of these associations.

Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.

Multimodal neuroimaging correlates of physical-cognitive covariation in Chilean adolescents. The Cogni-Action Project

Health-related behaviours have been related to brain structural features. In developing settings, such as Latin America, high social inequality has been inversely associated with several health-related behaviours affecting brain development. Understanding the relationship between health behaviours and brain structure in such settings is particularly important during adolescence when critical habits are acquired and ingrained. In this cross-sectional study, we carry out a multimodal analysis identifying a brain region associated with health-related behaviours (i.e., adiposity, fitness, sleep problems and others) and cognitive/academic performance, independent of socioeconomic status in a large sample of Chilean adolescents. Our findings suggest that the relationship between health behaviours and cognitive/academic performance involves a particular brain phenotype that could play a mediator role. These findings fill a significant gap in the literature, which has largely focused on developed countries and raise the possibility of promoting healthy behaviours in adolescence as a means to influence brain structure and thereby cognitive/academic achievement, independently of socioeconomic factors. By highlighting the potential impact on brain structure and cognitive/academic achievement, policymakers could design interventions that are more effective in reducing health disparities in developing countries.

Large-scale animal model study uncovers altered brain pH and lactate levels as a transdiagnostic endophenotype of neuropsychiatric disorders involving cognitive impairment

Increased levels of lactate, an end-product of glycolysis, have been proposed as a potential surrogate marker for metabolic changes during neuronal excitation. These changes in lactate levels can result in decreased brain pH, which has been implicated in patients with various neuropsychiatric disorders. We previously demonstrated that such alterations are commonly observed in five mouse models of schizophrenia, bipolar disorder, and autism, suggesting a shared endophenotype among these disorders rather than mere artifacts due to medications or agonal state. However, there is still limited research on this phenomenon in animal models, leaving its generality across other disease animal models uncertain. Moreover, the association between changes in brain lactate levels and specific behavioral abnormalities remains unclear. To address these gaps, the International Brain pH Project Consortium investigated brain pH and lactate levels in 109 strains/conditions of 2294 animals with genetic and other experimental manipulations relevant to neuropsychiatric disorders. Systematic analysis revealed that decreased brain pH and increased lactate levels were common features observed in multiple models of depression, epilepsy, Alzheimer's disease, and some additional schizophrenia models. While certain autism models also exhibited decreased pH and increased lactate levels, others showed the opposite pattern, potentially reflecting subpopulations within the autism spectrum. Furthermore, utilizing large-scale behavioral test battery, a multivariate cross-validated prediction analysis demonstrated that poor working memory performance was predominantly associated with increased brain lactate levels. Importantly, this association was confirmed in an independent cohort of animal models. Collectively, these findings suggest that altered brain pH and lactate levels, which could be attributed to dysregulated excitation/inhibition balance, may serve as transdiagnostic endophenotypes of debilitating neuropsychiatric disorders characterized by cognitive impairment, irrespective of their beneficial or detrimental nature.

Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification

Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.

Age-related modifications of muscle synergies during daily-living tasks: A scoping review

BACKGROUND

Aging is associated with changes in neuromuscular control that can lead to difficulties in performing daily living tasks. Muscle synergy analysis allows the assessment of neuromuscular control strategies and functional deficits. However, the age-related changes of muscle synergies during functional tasks are scattered throughout the literature. This review aimed to synthesize the existing literature on muscle synergies in elderly people during daily-living tasks and examine how they differ from those exhibited by young adults.

METHODS

The Medline, CINAHL and Web of Science databases were searched. Studies were included if they focused on muscle synergies in elderly people during walking, sit-to-stand or stair ascent, and if muscle synergies were obtained by a matrix factorization algorithm.

FINDINGS

Seventeen studies were included after the screening process. The muscle synergies of 295 elderly people and 182 young adults were reported, including 5 to 16 muscles per leg, or leg and trunk. Results suggest that: 1) elderly people and young adults retain similar muscle synergies' number, 2) elderly people have higher muscles weighting during walking, and 3) an increased inter and intra-subject temporal activation variability during specific tasks (i.e., walking and stair ascent, respectively) was reported in elderly people compared to young adults.

INTERPRETATION

This review gives a comprehensive understanding of age-related changes in neuromuscular control during daily living tasks. Our findings suggested that although the number of synergies remains similar, metrics such as spatial and temporal structures of synergies are more suitable to identify neuromuscular control deficits between young adults and elderly people.

Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits

BACKGROUND

Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD.

METHODS

Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors.

RESULTS

LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects.

CONCLUSIONS

We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

The effects of musical instrument training on fluid intelligence and executive functions in healthy older adults: A systematic review and meta-analysis

Intervention studiescombiningcognitive and motor demands have reported far-transfer cognitive benefits in healthy ageing. This systematic review and meta-analysis evaluated the effects of music and rhythm intervention on cognition in older adulthood. Inclusion criteria specified: 1) musical instrument training; 2) healthy, musically-naïve adults (≥60 years); 3) control group; 4) measure of executive function. Ovid, PubMed, Scopus and the Cochrane Library online databases were searched in August 2023. Data from thirteen studies were analysed (N = 502 participants). Study quality was assessed using the Cochrane Risk of Bias tool (RoB 2; Sterne et al., 2019). Random effects models revealed: a low effect on inhibition (d = 0.27,p = .0335); a low-moderate effect on switching (d = -0.39, p = .0021); a low-moderate effect on verbal category switching (d =0.39,p = .0166); and a moderate effect on processing speed (d = 0.47,p < .0001). No effect was found for selective visual attention, working memory, or verbal memory. With regards to overall bias, three studies were rated as "high", nine studies were rated as having "some concerns" and one was rated "low". The meta-analysis suggests that learning to play a musical instrument enhances attention inhibition, switching and processing speed in ageing.

Network-based analysis on the genes and their interactions reveals link between schizophrenia and Alzheimer's disease

Schizophrenia (SCZ) is a heterogeneous psychiatric disorder marked by impaired thinking, emotions, and behaviors. Studies have suggested a strong connection between SCZ and Alzheimer's disease (AD), however, controversies exist and the underlying mechanisms linking these two disorders remain largely unknown. Therefore, systematic studies of SCZ- and AD-related genes will provide valuable insights into the molecular features of these two diseases and their comorbidities. In this study, we obtained 331 SCZ-related genes, 650 AD-related genes, 65 shared genes between SCZ and AD. Enrichment analysis shown that these 65 shared genes were mainly involved in cognition, neural development, synaptic transmission, drug reactions, metabolic processes and immune related processes, suggesting a complex mechanism for the co-existence of SCZ and AD. In addition, we performed pathway enrichment analysis and found a total of 57 common pathways between SCZ and AD, which could be largely grouped into three modules: immune module, neurodevelopment module and cancer module. We eventually identified the potential disease-related genes whose interactions provide clues to the overlapping symptoms between SCZ and AD.

Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimer's Disease and Mild Cognitive Impairment

Recent research has implicated the cerebellum in Alzheimer's disease (AD), and cerebrocerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.

A review on brain age prediction models

Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

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.

Comparing neuromotor functions in 45- and 65-year-old adults with 18-year-old adolescents

Aim

This cross-sectional analysis investigates how neuromotor functions of two independent cohorts of approximately 45- and 65-year-old individuals are different from 18-year-old adolescents using the Zurich Neuromotor Assessment-2 (ZNA-2).

Methods

A total of 186 individuals of the Zurich Longitudinal Studies (ZLS) born in the 1950s (mean age 65.1 years, SD = 1.2 year, range of ages 59.0-67.5 years, n = 151, 82 males) and 1970s (mean age 43.6 years, SD = 1.3 year, range of ages 40.8-46.6 years, n = 35, 16 males) were tested with the ZNA-2 on 14 motor tasks combined in 5 motor components: fine motor, pure motor, balance, gross motor, and associated movements. Motor performance measures were converted into standard deviation scores (SDSs) using the normative data for 18-year-old individuals as reference.

Results

The motor performance of the 45-year-old individuals was remarkably similar to that of the 18-year-olds (SDS from -0.22 to 0.25) apart from associated movements (-0.49 SDS). The 65-year-olds showed lower performance than the 18-year-olds in all components of the ZNA-2, with the smallest difference observed for associated movements (-0.67 SDS) and the largest for gross motor skills (-2.29 SDS). Higher body mass index (BMI) was associated with better performance on gross motor skills for 45-year-olds but with worse performance for 65-year-olds. More educational years had positive effects on gross motor skills for both ages.

Interpretation

With the exception of associated movements, neuromotor functions as measured with the ZNA-2 are very similar in 45- and 18-year-olds. In contrast, at age 65 years, all neuromotor components show significantly lower function than the norm population at 18 years. Some evidence was found for the last-in-first-out hypothesis: the functions that developed later during adolescence, associated movements and gross motor skills, were the most vulnerable to age-related decline.

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

Single-subject cortical morphological brain networks across the adult lifespan

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Brain morphometry in older adults with and without dementia using extremely rapid structural scans

T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry. Here we compared the measurement properties of a widely adopted 1.0 mm resolution scan from the Alzheimer's Disease Neuroimaging Initiative (ADNI = 5'12'') with two variants of highly accelerated 1.0 mm scans (compressed-sensing, CSx6 = 1'12''; and wave-controlled aliasing in parallel imaging, WAVEx9 = 1'09'') in a test-retest study of 37 older adults aged 54 to 86 (including 19 individuals diagnosed with a neurodegenerative dementia). Rapid scans produced highly reliable morphometric measures that largely matched the quality of morphometrics derived from the ADNI scan. Regions of lower reliability and relative divergence between ADNI and rapid scan alternatives tended to occur in midline regions and regions with susceptibility-induced artifacts. Critically, the rapid scans yielded morphometric measures similar to the ADNI scan in regions of high atrophy. The results converge to suggest that, for many current uses, extremely rapid scans can replace longer scans. As a final test, we explored the possibility of a 0'49'' 1.2 mm CSx6 structural scan, which also showed promise. Rapid structural scans may benefit MRI studies by shortening the scan session and reducing cost, minimizing opportunity for movement, creating room for additional scan sequences, and allowing for the repetition of structural scans to increase precision of the estimates.

The retrogenesis of age-related decline in declarative and procedural memory

The retrogenesis hypothesis proposes that the order of breakdown of cognitive abilities in older adults is the reverse of the developmental order of children. Declarative and procedural memory systems, however, have been empirically understudied regarding this issue. The current study aimed to investigate whether retrogenesis occurs in the developmental and decline order of the declarative and procedural memory systems. Besides, we further investigated whether retrogenesis occurs in declarative memory, which was tested through the recognition of familiar and unfamiliar items. Both questions were investigated by looking at 28 Chinese younger adults and 27 cognitively healthy Chinese older adults. The recognition memory task and the Serial Reaction Time Task were administered on two consecutive days in order to measure their declarative and procedural memory, respectively. The results showed older adults performed significantly worse than younger adults for both tasks on both days, suggesting a decline in both declarative and procedural memory. Moreover, older adults exhibited relatively preserved declarative memory compared to procedural memory. This does not follow the expectations of the retrogenesis hypothesis. However, older adults demonstrated superior performance and a steeper rate of forgetting for recognizing familiar items than unfamiliar items. This reverses the developmental order of different patterns in the declarative memory system. Overall, we conclude that retrogenesis occurs in the declarative memory system, while does not in the decline order of the two memory systems; this understanding can better help inform our broader understanding of memory aging.

Functional topography of the neocortex predicts covariation in complex cognitive and basic motor abilities

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

Hippocampal anterior- posterior shift in childhood and adolescence

Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.

Social aging trajectories are sex-specific, sensitive to adolescent stress, and most robustly revealed during social tests with familiar stimuli

Social networks and support are integral to health and wellness across the lifespan, and social engagement may be particularly important during aging. However, social behavior and social cognition decline naturally during aging across species. Social behaviors are in part supported by the 'reward' circuitry, a network of brain regions that develops during adolescence. We published that male and female rats undergo adolescent social development during sex-specific periods, pre-early adolescence in females and early-mid adolescence males. Although males and females have highly dimorphic development, expression, and valuation of social behaviors, there is relatively little data indicating whether social aging is the same or different between the sexes. Thus, we sought to test two hypotheses: (1) natural social aging will be sex-speciifc, and (2) social isolation stress restricted to sex-specific adolescent critical periods for social development would impact social aging in sex-specific ways. To do this, we bred male and female rats in-house, and divided them randomly to receive either social isolation for one week during each sex's respective critical period, or no manipulation. We followed their social aging trajectory with a battery of five tests at 3, 7, and 11 months of age. We observed clear social aging signatures in all tests administered, but sex differences in natural social aging were most robustly observed when a familiar social stimulus was included in the test. We also observed that adolescent isolation did impact social behavior, in both age-independent and age-dependent ways, that were entirely sex-specific. Please note, this preprint will not be pushed further to publication (by me, AMK), as I am leaving academia. So, it's going to be written more conversationally.

Reduced memory precision in older age is associated with functional and structural differences in the angular gyrus

Decreased fidelity of mnemonic representations plays a critical role in age-related episodic memory deficits, yet the brain mechanisms underlying such reductions remain unclear. Using functional and structural neuroimaging, we examined how changes in two key nodes of the posterior-medial network, the hippocampus and the angular gyrus (AG), might underpin loss of memory precision in older age. Healthy young and older adults completed a memory task that involved reconstructing object features on a continuous scale. Investigation of blood-oxygen-level-dependent (BOLD) activity during retrieval revealed an age-related reduction in activity reflecting successful recovery of object features in the hippocampus, whereas trial-wise modulation of BOLD signal by graded memory precision was diminished in the AG. Gray matter volume of the AG further predicted individual differences in memory precision in older age, beyond likelihood of successful retrieval. These findings provide converging evidence for a role of functional and structural integrity of the AG in constraining the fidelity of episodic remembering in older age, yielding new insights into parietal contributions to age-related episodic memory decline.

Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain

Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

The characteristics of glucose metabolism and functional connectivity in posterior default network during nondemented aging: relationship with executive function performance

BACKGROUND

Understanding the characteristics of intrinsic connectivity networks (ICNs) in terms of both glucose metabolism and functional connectivity (FC) is important for revealing cognitive aging and neurodegeneration, but the relationships between these two aspects during aging has not been well established in older adults.

OBJECTIVE

This study is to assess the relationship between age-related glucose metabolism and FC in key ICNs, and their direct or indirect effects on cognitive deficits in older adults.

METHODS

We estimated the individual-level standard uptake value ratio (SUVr) and FC of eleven ICNs in 59 cognitively unimpaired older adults, then analyzed the associations of SUVr and FC of each ICN and their relationships with cognitive performance.

RESULTS

The results showed both the SUVr and FC in the posterior default mode network (pDMN) had a significant decline with age, and the association between them was also significant. Moreover, both decline of metabolism and FC in the pDMN were significantly correlated with executive function decline. Finally, mediation analysis revealed the glucose metabolism mediated the FC decline with age and FC mediated the executive function deficits.

CONCLUSIONS

Our findings indicated that covariance between glucose metabolism and FC in the pDMN is one of the main routes that contributes to age-related executive function decline.

Supervised Phenotype Discovery From Multimodal Brain Imaging

Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects' demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.

Investigating associations of delay discounting with brain structure, working memory, and episodic memory

INTRODUCTION

Delay discounting (DD), the preference for smaller and sooner rewards over larger and later ones, is an important behavioural phenomenon for daily functioning of increasing interest within psychopathology. The neurobiological mechanisms behind DD are not well understood and the literature on structural correlates of DD shows inconsistencies.

METHODS

Here we leveraged a large openly available dataset (n = 1196) to investigate associations with memory performance and gray and white matter correlates of DD using linked independent component analysis.

RESULTS

Greater DD was related to smaller anterior temporal gray matter volume. Associations of DD with total cortical volume, subcortical volumes, markers of white matter microscopic organization, working memory, and episodic memory scores were not significant after controlling for education and income.

CONCLUSION

Effects of size comparable to the one we identified would be unlikely to be replicated with sample sizes common in many previous studies in this domain, which may explain the incongruities in the literature. The paucity and small size of the effects detected in our data underscore the importance of using large samples together with methods that accommodate their statistical structure and appropriate control for confounders, as well as the need to devise paradigms with improved task parameter reliability in studies relating brain structure and cognitive abilities with DD.

Functional Topography of the Neocortex Predicts Covariation in Complex Cognitive and Basic Motor Abilities

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g., hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

The Overview of Cognitive Aging Models

To understand the cause of the age-related decline in cognitive function and its underlying mechanism, the cognitive aging model can provide us with important insights. In this section, we will introduce behavioral and neural models about age-related cognitive changes. Among behavioral models, several aging theories were discussed from the perspectives of educational, biological, and sociological factors, which could explain parts of the aging process. With the development of imaging technology, many studies have discussed the neural mechanism of aging and successively proposed neural models to explain the aging phenomenon. Behavioral models and neural mechanism models supplement each other, gradually unveiling the mystery of cognitive aging.

The gene expression patterns as surrogate indices of pH in the brain

Hydrogen ion (H⁺) is one of the most potent intrinsic neuromodulators in the brain in terms of concentration. Changes in H⁺ concentration, expressed as pH, are thought to be associated with various biological processes, such as gene expression, in the brain. Accumulating evidence suggests that decreased brain pH is a common feature of several neuropsychiatric disorders, including schizophrenia, bipolar disorder, autism spectrum disorder, and Alzheimer's disease. However, it remains unclear whether gene expression patterns can be used as surrogates for pH changes in the brain. In this study, we performed meta-analyses using publicly available gene expression datasets to profile the expression patterns of pH-associated genes, whose expression levels were correlated with brain pH, in human patients and mouse models of major central nervous system (CNS) diseases, as well as in mouse cell-type datasets. Comprehensive analysis of 281 human datasets from 11 CNS disorders revealed that gene expression associated with decreased pH was over-represented in disorders including schizophrenia, bipolar disorder, autism spectrum disorders, Alzheimer's disease, Huntington's disease, Parkinson's disease, and brain tumors. Expression patterns of pH-associated genes in mouse models of neurodegenerative disease showed a common time course trend toward lower pH over time. Furthermore, cell type analysis identified astrocytes as the cell type with the most acidity-related gene expression, consistent with previous experimental measurements showing a lower intracellular pH in astrocytes than in neurons. These results suggest that the expression pattern of pH-associated genes may be a surrogate for the state- and trait-related changes in pH in brain cells. Altered expression of pH-associated genes may serve as a novel molecular mechanism for a more complete understanding of the transdiagnostic pathophysiology of neuropsychiatric and neurodegenerative disorders.

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects

Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.

Brain network architecture constrains age-related cortical thinning

Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including "hotspots" of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.

Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis

Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.

Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications

Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

Equalization of Brain State Occupancy Accompanies Cognitive Impairment in Cerebral Small Vessel Disease

BACKGROUND

Cognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment.

METHODS

We obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level-dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function.

RESULTS

Data were available for 979 participants (ages 45-74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (-) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN- = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN-. Time spent in DMN-related brain states was associated with executive function.

CONCLUSIONS

Associations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.

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

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

Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r² of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

Hypothalamic microstructure and function are related to body mass, but not mental or cognitive abilities across the adult lifespan

Physical, mental, and cognitive resources are essential for healthy aging. Aging impacts on the structural integrity of various brain regions, including the hippocampus. Even though recent rodent studies hint towards a critical role of the hypothalamus, there is limited evidence on functional consequences of age-related changes of this region in humans. Given its central role in metabolic regulation and affective processing and its connections to the hippocampus, it is plausible that hypothalamic integrity and connectivity are associated with functional age-related decline. We used data of n = 369 participants (18-88 years) from the Cambridge Centre for Ageing and Neuroscience repository to determine functional impacts of potential changes in hypothalamic microstructure across the lifespan. First, we identified age-related changes in microstructure as a function of physical, mental, and cognitive health and compared those findings to changes in hippocampal microstructure. Second, we investigated the relationship of hypothalamic microstructure and resting-state functional connectivity and related those changes to age as well as physical health. Our results showed that hypothalamic microstructure is not affected by depressive symptoms (mental health), cognitive performance (cognitive health), and comparatively stable across the lifespan, but affected by body mass (physical health). Furthermore, body mass changes connectivity to limbic regions including the hippocampus, amygdala, and nucleus accumbens, suggesting functional alterations in the metabolic and reward systems. Our results demonstrate that hypothalamic structure and function are affected by body mass, focused on neural density and dispersion, but not inflammation. Still, observed effect sizes were small, encouraging detailed investigations of individual hypothalamic subunits.