Towards a biologically annotated brain connectome.

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Null models in network neuroscience.

Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.

Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain.

Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately reconstructs the position of brain tissue samples, ii) delineates known functional territories, and iii) can model the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical axes differentiating the primary sensory cortex from the association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two nonhuman primate datasets and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well-known transcription factors (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.

Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease.

The brain is composed of disparate neural populations that communicate and interact with one another. Although fiber bundles, similarities in molecular architecture, and synchronized neural activity all reflect how brain regions potentially interact with one another, a comprehensive study of how all these interregional relationships jointly reflect brain structure and function remains missing. Here, we systematically integrate 7 multimodal, multiscale types of interregional similarity ("connectivity modes") derived from gene expression, neurotransmitter receptor density, cellular morphology, glucose metabolism, haemodynamic activity, and electrophysiology in humans. We first show that for all connectivity modes, feature similarity decreases with distance and increases when regions are structurally connected. Next, we show that connectivity modes exhibit unique and diverse connection patterns, hub profiles, spatial gradients, and modular organization. Throughout, we observe a consistent primacy of molecular connectivity modes-namely correlated gene expression and receptor similarity-that map onto multiple phenomena, including the rich club and patterns of abnormal cortical thickness across 13 neurological, psychiatric, and neurodevelopmental disorders. Finally, to construct a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and show that the fused network maps onto major organizational features of the cortex including structural connectivity, intrinsic functional networks, and cytoarchitectonic classes. Altogether, this work contributes to the integrative study of interregional relationships in the human cerebral cortex.

neuromaps: structural and functional interpretation of brain maps.

Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Comparing experimentally generated maps to these reference maps facilitates cross-disciplinary scientific discovery. Although recent data sharing initiatives increase the accessibility of brain maps, data are often shared in disparate coordinate systems, precluding systematic and accurate comparisons. Here we introduce neuromaps, a toolbox for accessing, transforming and analyzing structural and functional brain annotations. We implement functionalities for generating high-quality transformations between four standard coordinate systems. The toolbox includes curated reference maps and biological ontologies of the human brain, such as molecular, microstructural, electrophysiological, developmental and functional ontologies. Robust quantitative assessment of map-to-map similarity is enabled via a suite of spatial autocorrelation-preserving null models. neuromaps combines open-access data with transparent functionality for standardizing and comparing brain maps, providing a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex.

Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these 2 types of neural activity remains unknown. Here, we map electromagnetic networks (measured using magnetoencephalography (MEG)) to haemodynamic networks (measured using functional magnetic resonance imaging (fMRI)). We find that the relationship between the 2 modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic-haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the 2 modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by MEG functional connectivity at the beta (15 to 29 Hz) frequency band. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical microarchitecture and multimodal connectivity patterns.

Connectome reorganization associated with temporal lobe pathology and its surgical resection.

Network neuroscience offers a unique framework to understand the organizational principles of the human brain. Despite recent progress, our understanding of how the brain is modulated by focal lesions remains incomplete. Resection of the temporal lobe is the most effective treatment to control seizures in pharmaco-resistant temporal lobe epilepsy (TLE), making this syndrome a powerful model to study lesional effects on network organization in young and middle-aged adults. Here, we assessed the downstream consequences of a focal lesion and its surgical resection on the brain's structural connectome, and explored how this reorganization relates to clinical variables at the individual patient level. We included adults with pharmaco-resistant TLE (n = 37) who underwent anterior temporal lobectomy between two imaging time points, as well as age- and sex-matched healthy controls who underwent comparable imaging (n = 31). Core to our analysis was the projection of high-dimensional structural connectome data-derived from diffusion MRI tractography from each subject-into lower-dimensional gradients. We then compared connectome gradients in patients relative to controls before surgery, tracked surgically-induced connectome reconfiguration from pre- to postoperative time points, and examined associations to patient-specific clinical and imaging phenotypes. Before surgery, individuals with TLE presented with marked connectome changes in bilateral temporo-parietal regions, reflecting an increased segregation of the ipsilateral anterior temporal lobe from the rest of the brain. Surgery-induced connectome reorganization was localized to this temporo-parietal subnetwork, but primarily involved postoperative integration of contralateral regions with the rest of the brain. Using a partial least-squares analysis, we uncovered a latent clinical imaging signature underlying this pre- to postoperative connectome reorganization, showing that patients who displayed postoperative integration in bilateral fronto-occipital cortices also had greater preoperative ipsilateral hippocampal atrophy, lower seizure frequency and secondarily generalized seizures. Our results bridge the effects of focal brain lesions and their surgical resections with large-scale network reorganization and interindividual clinical variability, thus offering new avenues to examine the fundamental malleability of the human brain.

Adolescent development of multiscale structural wiring and functional interactions in the human connectome.

Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure-function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.

Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism.

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.

Mitochondrial function-associated genes underlie cortical atrophy in prodromal synucleinopathies.

Isolated rapid eye movement sleep behaviour disorder (iRBD) is a sleep disorder characterized by the loss of rapid eye movement sleep muscle atonia and the appearance of abnormal movements and vocalizations during rapid eye movement sleep. It is a strong marker of incipient synucleinopathy such as dementia with Lewy bodies and Parkinson's disease. Patients with iRBD already show brain changes that are reminiscent of manifest synucleinopathies including brain atrophy. However, the mechanisms underlying the development of this atrophy remain poorly understood. In this study, we performed cutting-edge imaging transcriptomics and comprehensive spatial mapping analyses in a multicentric cohort of 171 polysomnography-confirmed iRBD patients [67.7 ± 6.6 (49-87) years; 83% men] and 238 healthy controls [66.6 ± 7.9 (41-88) years; 77% men] with T1-weighted MRI to investigate the gene expression and connectivity patterns associated with changes in cortical thickness and surface area in iRBD. Partial least squares regression was performed to identify the gene expression patterns underlying cortical changes in iRBD. Gene set enrichment analysis and virtual histology were then done to assess the biological processes, cellular components, human disease gene terms, and cell types enriched in these gene expression patterns. We then used structural and functional neighbourhood analyses to assess whether the atrophy patterns in iRBD were constrained by the brain's structural and functional connectome. Moreover, we used comprehensive spatial mapping analyses to assess the specific neurotransmitter systems, functional networks, cytoarchitectonic classes, and cognitive brain systems associated with cortical changes in iRBD. All comparisons were tested against null models that preserved spatial autocorrelation between brain regions and compared to Alzheimer's disease to assess the specificity of findings to synucleinopathies. We found that genes involved in mitochondrial function and macroautophagy were the strongest contributors to the cortical thinning occurring in iRBD. Moreover, we demonstrated that cortical thinning was constrained by the brain's structural and functional connectome and that it mapped onto specific networks involved in motor and planning functions. In contrast with cortical thickness, changes in cortical surface area were related to distinct genes, namely genes involved in the inflammatory response, and to different spatial mapping patterns. The gene expression and connectivity patterns associated with iRBD were all distinct from those observed in Alzheimer's disease. In summary, this study demonstrates that the development of brain atrophy in synucleinopathies is constrained by specific genes and networks.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia.

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships.

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterization of the human brain. How these 2 modalities differ in their contribution to cognition and behavior is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that although for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, whereas for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.

Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks.

The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.

BrainStat: A toolbox for brain-wide statistics and multimodal feature associations.

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

Altered Spinal Cord Functional Connectivity Associated with Parkinson's Disease Progression.

BACKGROUND: Parkinson's disease (PD) has traditionally been viewed as an α-synucleinopathy brain pathology. Yet evidence based on postmortem human and animal experimental models indicates that the spinal cord may also be affected. OBJECTIVE: Functional magnetic resonance imaging (fMRI) seems to be a promising candidate to better characterize spinal cord functional organization in PD patients. METHODS: Resting-state spinal fMRI was performed in 70 PD patients and 24 age-matched healthy controls, the patients being divided into three groups based on their motor symptom severity: PDlow (n = 24), PDmed (n = 22), and PDadv (n = 24) groups. A combination of independent component analysis (ICA) and a seed-based approach was applied. RESULTS: When pooling all participants, the ICA revealed distinct ventral and dorsal components distributed along the rostro-caudal axis. This organization was highly reproducible within subgroups of patients and controls. PD severity, assessed by Unified Parkinson's Disease Rating Scale (UPDRS) scores, was associated with a decrease in spinal functional connectivity (FC). Notably, we observed a reduced intersegmental correlation in PD as compared to controls, the latter being negatively associated with patients' upper-limb UPDRS scores (P = 0.0085). This negative association between FC and upper-limb UPDRS scores was significant between adjacent C4-C5 (P = 0.015) and C5-C6 (P = 0.20) cervical segments, levels associated with upper-limb functions. CONCLUSIONS: The present study provides the first evidence of spinal cord FC changes in PD and opens new avenues for the effective diagnosis and therapeutic strategies in PD. This underscores how spinal cord fMRI can serve as a powerful tool to characterize, in vivo, spinal circuits for a variety of neurological diseases. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Differentially targeted seeding reveals unique pathological alpha-synuclein propagation patterns.

Parkinson's disease is a progressive neurodegenerative disorder characterized by the intracellular accumulation of insoluble alpha-synuclein aggregates into Lewy bodies and neurites. Increasing evidence indicates that Parkinson's disease progression results from the spread of pathologic alpha-synuclein through neuronal networks. However, the exact mechanisms underlying the propagation of abnormal proteins in the brain are only partially understood. The objective of this study was first to describe the long-term spatiotemporal distributions of Lewy-related pathology in mice injected with alpha-synuclein preformed fibrils and then to recreate these patterns using a computational model that simulates in silico the spread of pathologic alpha-synuclein. In this study, 87 2-3-month-old non-transgenic mice were injected with alpha-synuclein preformed fibrils to generate a comprehensive post-mortem dataset representing the long-term spatiotemporal distributions of hyperphosphorylated alpha-synuclein, an established marker of Lewy pathology, across the 426 regions of the Allen Mouse Brain Atlas. The mice were injected into either the caudoputamen, nucleus accumbens or hippocampus, and followed over 24 months with pathologic alpha-synuclein quantified at seven intermediate time points. The pathologic patterns observed at each time point in this high-resolution dataset were then compared to those generated using a Susceptible-Infected-Removed (SIR) computational model, an agent-based model that simulates the spread of pathologic alpha-synuclein for every brain region taking simultaneously into account the effect of regional brain connectivity and Snca gene expression. Our histopathological findings showed that differentially targeted seeding of pathological alpha-synuclein resulted in unique propagation patterns over 24 months and that most brain regions were permissive to pathology. We found that the SIR model recreated the observed distributions of pathology over 24 months for each injection site. Null models showed that both Snca gene expression and connectivity had a significant influence on model fit. In sum, our study demonstrates that the combination of normal alpha-synuclein concentration and brain connectomics contributes to making brain regions more vulnerable to the pathological process, providing support for a prion-like spread of pathologic alpha-synuclein. We propose that this rich dataset and the related computational model will help test new hypotheses regarding mechanisms that may alter the spread of pathologic alpha-synuclein in the brain.

Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression.

Isolated REM sleep behaviour disorder (iRBD) is a synucleinopathy characterized by abnormal behaviours and vocalizations during REM sleep. Most iRBD patients develop dementia with Lewy bodies, Parkinson's disease or multiple system atrophy over time. Patients with iRBD exhibit brain atrophy patterns that are reminiscent of those observed in overt synucleinopathies. However, the mechanisms linking brain atrophy to the underlying alpha-synuclein pathophysiology are poorly understood. Our objective was to investigate how the prion-like and regional vulnerability hypotheses of alpha-synuclein might explain brain atrophy in iRBD. Using a multicentric cohort of 182 polysomnography-confirmed iRBD patients who underwent T1-weighted MRI, we performed vertex-based cortical surface and deformation-based morphometry analyses to quantify brain atrophy in patients (67.8 years, 84% male) and 261 healthy controls (66.2 years, 75%) and investigated the morphological correlates of motor and cognitive functioning in iRBD. Next, we applied the agent-based Susceptible-Infected-Removed model (i.e. a computational model that simulates in silico the spread of pathologic alpha-synuclein based on structural connectivity and gene expression) and tested if it recreated atrophy in iRBD by statistically comparing simulated regional brain atrophy to the atrophy observed in patients. The impact of SNCA and GBA gene expression and brain connectivity was then evaluated by comparing the model fit to the one obtained in null models where either gene expression or connectivity was randomized. The results showed that iRBD patients present with cortical thinning and tissue deformation, which correlated with motor and cognitive functioning. Next, we found that the computational model recreated cortical thinning (r = 0.51, P = 0.0007) and tissue deformation (r = 0.52, P = 0.0005) in patients, and that the connectome's architecture along with SNCA and GBA gene expression contributed to shaping atrophy in iRBD. We further demonstrated that the full agent-based model performed better than network measures or gene expression alone in recreating the atrophy pattern in iRBD. In summary, atrophy in iRBD is extensive, correlates with motor and cognitive function and can be recreated using the dynamics of agent-based modelling, structural connectivity and gene expression. These findings support the concepts that both prion-like spread and regional susceptibility account for the atrophy observed in prodromal synucleinopathies. Therefore, the agent-based Susceptible-Infected-Removed model may be a useful tool for testing hypotheses underlying neurodegenerative diseases and new therapies aimed at slowing or stopping the spread of alpha-synuclein pathology.

Age differences in the functional architecture of the human brain.

The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, from global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience-dependent changes, or both, is uncertain. We employed a multimethod strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n = 181) and older (n = 120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multiscale, multimethod approaches to characterize the architecture of functional brain aging.

Age- and Episodic Memory-related Differences in Task-based Functional Connectivity in Women and Men.

Aging is associated with episodic memory decline and changes in functional brain connectivity. Understanding whether and how biological sex influences age- and memory performance-related functional connectivity has important theoretical implications for the cognitive neuroscience of memory and aging. Here, we scanned 161 healthy adults between 19 and 76 years of age in an event-related fMRI study of face-location spatial context memory. Adults were scanned while performing easy and difficult versions of the task at both encoding and retrieval. We used multivariate whole-brain partial least squares connectivity to test the hypothesis that there are sex differences in age- and episodic memory performance-related functional connectivity. We examined how individual differences in age and retrieval accuracy correlated with task-related connectivity. We then repeated this analysis after disaggregating the data by self-reported sex. We found that increased encoding and retrieval-related connectivity within the dorsal attention network (DAN), and between DAN and frontoparietal network and visual networks, were positively correlated to retrieval accuracy and negatively correlated with age in both sexes. We also observed sex differences in age- and performance-related functional connectivity: (a) Greater between-networks integration was apparent at both levels of task difficulty in women only, and (b) increased DAN-default mode network connectivity with age was observed in men and was correlated with poorer memory performance. Therefore, the neural correlates of age-related episodic memory decline differ in women and men and have important theoretical and clinical implications for the cognitive neuroscience of memory, aging, and dementia prevention.

A Riemannian approach to predicting brain function from the structural connectome.

Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.