Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

APOE, Immune Factors, Sex, and Diet Interact to Shape Brain Networks in Mouse Models of Aging

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed and modifiable risk factors influence susceptibility to AD are under intense investigation, yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors including APOE genotype, age, sex, diet, and immunity we leveraged mice expressing the human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. Employing graph analyses of brain connectomes derived from accelerated diffusion-weighted MRI, we assessed the global and local impact of risk factors in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

Significance Statement

Current interventions for Alzheimer's disease (AD) do not provide a cure, and are delivered years after neuropathological onset. Addressing the impact of risk factors on brain networks holds promises for early detection, prevention, and revealing putative therapeutic targets at preclinical stages. We utilized six mouse models to investigate the impact of factors, including APOE genotype, age, sex, immunity, and diet, on brain networks. Large structural connectomes were derived from high resolution compressed sensing diffusion MRI. A highly parallelized graph classification identified subnetworks associated with unique risk factors, revealing their network fingerprints, and a common network composed of 63 connections with shared vulnerability to all risk factors. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles

Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine-adenine-guanine repeat number. Moreover, the transcriptome-connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Beyond the factor indeterminacy problem using genome-wide association data

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

General dimensions of human brain morphometry inferred from genome-wide association data

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Mesial Prefrontal Cortex and Alcohol Misuse: Dissociating Cross-sectional and Longitudinal Relationships in UK Biobank

BACKGROUND

Alcohol misuse is a major global public health issue. The disorder is characterized by aberrant neural networks interacting with environment and genetics. Dissecting the neural substrates and functional networks that relate to longitudinal changes in alcohol use from those that relate to alcohol misuse cross-sectionally is important to elucidate therapeutic approaches.

METHODS

To assess how neuroimaging data, including T1, resting-state functional magnetic resonance imaging, and diffusion-weighted imaging, relate to alcohol misuse cross-sectionally and longitudinally in the UK Biobank, this study analyzed range of alcohol misuse in a population-based normative sample of 24,784 participants, ages 45 to 81 years old, in a cross-sectional analysis and a sample of 3070 participants in a longitudinal analysis 2 years later.

RESULTS

Cross-sectional analysis showed that alcohol use is associated with a reduction in dorsal anterior cingulate cortex and dorsomedial prefrontal cortex gray matter concentration and functional resting-state connectivity (nodal degree: t_24,422 = -12.99, p < 1 × 10^-17). Reduced dorsal anterior cingulate cortex/dorsomedial prefrontal cortex functional connections to the ventrolateral prefrontal cortex, amygdala, and striatum relate to greater alcohol use. In a longitudinal analysis, higher resting-state nodal degree (t₃₀₃₆ = -3.27, p = .0011) and T1 gray matter concentration in the ventromedial prefrontal cortex relate to reduced alcohol intake frequency 2 years later. Higher ventromedial prefrontal cortex and frontoparietal executive network functional connectivity is associated with lower subsequent drinking longitudinally.

CONCLUSIONS

Dorsal versus ventromedial prefrontal regions are differentially related to alcohol misuse cross-sectionally or longitudinally in a large UK Biobank normative dataset. Our study provides a comprehensive understanding of the neurobiological substrates of alcohol use as a state or prospectively, thereby providing potential targets for clinical treatment.

An accelerated degeneration of white matter microstructure and networks in the nondemented old-old

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

Extracellular microRNA and cognitive function in a prospective cohort of older men: The Veterans Affairs Normative Aging Study

BACKGROUND

Aging-related cognitive decline is an early symptom of Alzheimer's disease and other dementias, and on its own can have substantial consequences on an individual's ability to perform important everyday functions. Despite increasing interest in the potential roles of extracellular microRNAs (miRNAs) in central nervous system (CNS) pathologies, there has been little research on extracellular miRNAs in early stages of cognitive decline. We leverage the longitudinal Normative Aging Study (NAS) cohort to investigate associations between plasma miRNAs and cognitive function among cognitively normal men.

METHODS

This study includes data from up to 530 NAS participants (median age: 71.0 years) collected from 1996 to 2013, with a total of 1,331 person-visits (equal to 2,471 years of follow up). Global cognitive function was assessed using the Mini-Mental State Examination (MMSE). Plasma miRNAs were profiled using small RNA sequencing. Associations of expression of 381 miRNAs with current cognitive function and rate of change in cognitive function were assessed using linear regression (N = 457) and linear mixed models (N = 530), respectively.

RESULTS

In adjusted models, levels of 2 plasma miRNAs were associated with higher MMSE scores (p < 0.05). Expression of 33 plasma miRNAs was associated with rate of change in MMSE scores over time (p < 0.05). Enriched KEGG pathways for miRNAs associated with concurrent MMSE and MMSE trajectory included Hippo signaling and extracellular matrix-receptor interactions. Gene targets of miRNAs associated with MMSE trajectory were additionally associated with prion diseases and fatty acid biosynthesis.

CONCLUSIONS

Circulating miRNAs were associated with both cross-sectional cognitive function and rate of change in cognitive function among cognitively normal men. Further research is needed to elucidate the potential functions of these miRNAs in the CNS and investigate relationships with other neurological outcomes.

Research hotspots and trends of multimodality MRI on vascular cognitive impairment in recent 12 years: A bibliometric analysis

BACKGROUND

Multimodality magnetic resonance imaging (MRI) is widely used to detect vascular cognitive impairment (VCI). However, a bibliometric analysis of this issue remains unknown. Therefore, this study aimed to explore the research hotspots and trends of multimodality MRI on VCI over the past 12 years based on the Web of Science core collection using CiteSpace Software (6.1R2).

METHODS

Literature related to multimodality MRI for VCI from 2010 to 2021 was identified and analyzed from the Web of Science core collection database. We analyzed the countries, institutions, authors, cited journals, references, keyword bursts, and clusters using CiteSpace.

RESULTS

In total, 587 peer-reviewed documents were retrieved, and the annual number of publications showed an exponential growth trend over the past 12 years. The most productive country was the USA, with 182 articles, followed by China with 134 papers. The top 3 active academic institutions were Capital Medical University, Radboud UNIV Nijmegen, and UNIV Toronto. The most productive journal was the Journal of Alzheimer's Disease (33 articles). The most co-cited journal was Neurology, with the highest citations (492) and the highest intermediary centrality (0.14). The top-ranked publishing author was De Leeuw FE (17 articles) with the highest intermediary centrality of 0.04. Ward Law JM was the most cited author (123 citations) and Salat Dh was the most centrally cited author (0.24). The research hotspots of multimodal MRI for VCI include Alzheimer disease, vascular cognitive impairment, white matter intensity, cerebrovascular disease, dementia, mild cognitive impairment, neurovascular coupling, acute ischemic stroke, depression, and cerebral ischemic stroke. The main frontiers in the keywords are fMRI, vascular coupling, and cerebral ischemic stroke, and current research trends include impact, decline, and classification.

CONCLUSIONS

The findings from this bibliometric study provide research hotspots and trends for multimodality MRI for VCI over the past 12 years, which may help researchers identify hotspots and explore cutting-edge trends in this field.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

The Severity of Sensorimotor Tracts Degeneration May Predict Motor Performance in Chronic Stroke Patients, While Brain Structural Network Dysfunction May Not

Although the relationship between corticospinal tract (CST) fiber degeneration and motor outcome after stroke has been established, the relationship of sensorimotor cortical areas with CST fibers has not been clarified. Also limited research has been conducted on how abnormalities in brain structural networks are related to motor recovery. To address these gaps in knowledge, we conducted a diffusion tensor imaging (DTI) study with 12 chronic stroke patients (CSPs) and 12 age-matched healthy controls (HCs). We compared fractional anisotropy (FA) and mean diffusivity (MD) in 60 CST segments using the probabilistic sensorimotor area tract template (SMATT). Least Absolute Shrinkage and Selection Operator (LASSO) regressions were used to select independent predictors of Fugl-Meyer upper extremity (FM-UE) scores among FA and MD values of SMATT regions. The Graph Theoretical Network Analysis Toolbox was used to assess the structural network of each subject's brain. Global and nodal metrics were calculated, compared between the groups, and correlated with FM-UE scores. Mann–Whitney U-tests revealed reduced FA values in CSPs, compared to HCs, in many ipsilesional SMATT regions and in two contralesional regions. Mean FA value of the left (L.) primary motor cortex (M1)/supplementary motor area (SMA) region was predictive of FM-UE score (P = 0.004). Mean MD values for the L. M1/ventral premotor cortex (PMv) region (P = 0.001) and L. PMv/SMA region (P = 0.001) were found to be significant predictors of FM-UE scores. Network efficiency was the only global metric found to be reduced in CSPs (P = 0.006 vs. HCs). Nodal efficiency of the L. hippocampus, L. parahippocampal gyrus, L. fusiform gyrus (P = 0.001), and nodal local efficiency of the L. supramarginal gyrus (P < 0.001) were reduced in CSPs relative to HCs. No graph metric was associated with FM-UE scores. In conclusion, the integrity of CSTs connected to M1, SMA, and PMv were shown to be independent predictors of motor performance in CSPs, while stroke-induced topological changes in the brain's structural connectome may not be. A sensorimotor cortex-specific tract template can refine CST degeneration data and the relationship of CST degeneration with motor performance.

Overview of (f)MRI Studies of Cognitive Aging for Non-Experts: Looking through the Lens of Neuroimaging

This special issue concerning Brain Functional and Structural Connectivity and Cognition aims to expand our understanding of brain connectivity. Herein, I review related topics including the principle and concepts of functional MRI, brain activation, and functional/structural connectivity in aging for uninitiated readers. Visuospatial attention, one of the well-studied functions in aging, is discussed from the perspective of neuroimaging.

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

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

A strong dependency between changes in fluid and crystallized abilities in human cognitive aging

Theories of adult cognitive development classically distinguish between fluid abilities, which require effortful processing at the time of assessment, and crystallized abilities, which require the retrieval and application of knowledge. On average, fluid abilities decline throughout adulthood, whereas crystallized abilities show gains into old age. These diverging age trends, along with marked individual differences in rates of change, have led to the proposition that individuals might compensate for fluid declines with crystallized gains. Here, using data from two large longitudinal studies, we show that rates of change are strongly correlated across fluid and crystallized abilities. Hence, individuals showing greater losses in fluid abilities tend to show smaller gains, or even losses, in crystallized abilities. This observed commonality between fluid and crystallized changes places constraints on theories of compensation and directs attention toward domain-general drivers of adult cognitive decline and maintenance.

The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI

A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.

White Matter Alterations Between Brain Network Hubs Underlie Processing Speed Impairment in Patients With Schizophrenia

Processing speed (PS) impairment is one of the most severe and common cognitive deficits in schizophrenia. Previous studies have reported correlations between PS and white matter diffusion properties, including fractional anisotropy (FA), in several fiber bundles in schizophrenia, suggesting that white matter alterations could underpin decreased PS. In schizophrenia, white matter alterations are most prevalent within inter-hub connections of the rich club. However, the spatial and topological characteristics of this association between PS and FA have not been investigated in patients. In this context, we tested whether structural connections comprising the rich club network would underlie PS impairment in 298 patients with schizophrenia or schizoaffective disorder and 190 healthy controls from the Australian Schizophrenia Research Bank. PS, measured using the digit symbol coding task, was largely (Cohen’s d = 1.33) and significantly (P < .001) reduced in the patient group when compared with healthy controls. Significant associations between PS and FA were widespread in the patient group, involving all cerebral lobes. FA was not associated with other cognitive measures of phonological fluency and verbal working memory in patients, suggesting specificity to PS. A topological analysis revealed that despite being spatially widespread, associations between PS and FA were over-represented among connections forming the rich club network. These findings highlight the need to consider brain network topology when investigating high-order cognitive functions that may be spatially distributed among several brain regions. They also reinforce the evidence that brain hubs and their interconnections may be particularly vulnerable parts of the brain in schizophrenia.