Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Brain connectivity and transcriptomic similarity inform abnormal morphometric similarity patterns in first-episode, treatment-naïve major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is associated with disrupted brain structural integration. Morphometric similarity offers a means to capture the coordinated patterns of various structural features. However, it remains unknown whether MDD-related changes can be detected in cortical morphometric similarity through the Morphometric Inverse Divergence (MIND) network. Additionally, the role of brain connectivity in shaping these alterations, and their links to neuroreceptors and gene expression, have yet to be investigated.

METHODS

Using the T1-weighted MRI data from 71 patients with first-episode, treatment-naïve MDD and 69 healthy controls, we constructed the MIND network for all participants. We then performed between-group comparisons to investigate abnormalities in the network and spatial relationships between the observed patterns of MIND disruption and the patterns of neuroreceptors were estimated. Network-based spreading was utilized to explore the abnormalities constrained by brain connectivity based on structural, functional, and transcriptional connectome architecture and to further identify potential epicenters of MDD. In addition, partial least squares regression was conducted to examine the associations of gene expression profiles with MIND changes in MDD.

RESULTS

Patients with MDD showed significantly increased MIND in regions associated with sensation and cognition compared with healthy controls, with this altered pattern being influenced by a combination of transcriptional and structural connectivity, and potential epicenters of MDD were identified in the frontal, parietal, and paracentral cortices. Furthermore, the cortical map of case-control differences in MIND was spatially correlated with the cannabinoid CB1 receptor and the brain-wide expression of a weighted combination of genes. These genes were enriched for neurobiologically relevant pathways and preferentially expressed in different cell classes and cortical layers.

CONCLUSION

These results highlight the abnormal pattern of morphometric similarity observed in MDD, shedding light on the complex interplay between disrupted macroscale coordinated morphology and microscale molecular organization in MDD.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Molecular pathology, developmental changes and synaptic dysfunction in (pre-) symptomatic human C9ORF72-ALS/FTD cerebral organoids

A hexanucleotide repeat expansion (HRE) in C9ORF72 is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Human brain imaging and experimental studies indicate early changes in brain structure and connectivity in C9-ALS/FTD, even before symptom onset. Because these early disease phenotypes remain incompletely understood, we generated iPSC-derived cerebral organoid models from C9-ALS/FTD patients, presymptomatic C9ORF72-HRE (C9-HRE) carriers, and controls. Our work revealed the presence of all three C9-HRE-related molecular pathologies and developmental stage-dependent size phenotypes in cerebral organoids from C9-ALS/FTD patients. In addition, single-cell RNA sequencing identified changes in cell type abundance and distribution in C9-ALS/FTD organoids, including a reduction in the number of deep layer cortical neurons and the distribution of neural progenitors. Further, molecular and cellular analyses and patch-clamp electrophysiology detected various changes in synapse structure and function. Intriguingly, organoids from all presymptomatic C9-HRE carriers displayed C9-HRE molecular pathology, whereas the extent to which more downstream cellular defects, as found in C9-ALS/FTD models, were detected varied for the different presymptomatic C9-HRE cases. Together, these results unveil early changes in 3D human brain tissue organization and synaptic connectivity in C9-ALS/FTD that likely constitute initial pathologies crucial for understanding disease onset and the design of therapeutic strategies.

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

Spatial and Temporal Relationships Between Atrophy and Hypometabolism in Behavioral-Variant Frontotemporal Dementia

PURPOSE

Individuals with behavioral-variant frontotemporal dementia (bvFTD) show changes in brain structure as assessed by MRI and brain function assessed by 18FDG-PET hypometabolism. However, current understanding of the spatial and temporal interplay between these measures remains limited.

METHODS

Here, we examined longitudinal atrophy and hypometabolism relationships in 15 bvFTD subjects with 2 to 4 follow-up MRI and PET scans (56 visits total). Subject-specific slopes of atrophy and hypometabolism over time were extracted across brain regions and correlated with baseline measures both locally, via Pearson correlations, and nonlocally, via sparse canonical correlation analyses (SCCA).

RESULTS

Notably, we identified a robust link between initial hypometabolism and subsequent cortical atrophy rate changes in bvFTD subjects. Network-level exploration unveiled alignment between baseline hypometabolism and ensuing atrophy rates in the dorsal attention, language, and default mode networks. SCCA identified 2 significant and highly localized components depicting the connection between baseline hypometabolism and atrophy slope over time. The first centered around bilateral orbitofrontal, frontopolar, and medial prefrontal lobes, whereas the second concentrated in the left temporal lobe and precuneus.

CONCLUSIONS

This study highlights 18FDG-PET as a dependable predictor of forthcoming atrophy in spatially adjacent brain regions for individuals with bvFTD.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

Progress in Primary Progressive Aphasia: A Review

We present a review of the definition, classification, and epidemiology of primary progressive aphasia (PPA); an update of the taxonomy of the clinical syndrome of PPA; and recent advances in the neuroanatomy, pathology, and genetics of PPA, as well as the search for biomarkers and treatment. PPA studies that have contributed to concepts of language organization and disease propagation in neurodegeneration are also reviewed. In addition, the issues of heterogeneity versus the relationships of the clinical phenotypes and their relationship to biological, pathological, and genetic advances are discussed, as is PPA's relationship to other conditions such as frontotemporal dementia, corticobasal degeneration, progressive supranuclear palsy, Pick disease, and amyotrophic lateral sclerosis. Arguments are presented in favor of considering these conditions as one entity versus many.

Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We study the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies by leveraging two large independent AD cohorts. We identify AD susceptibility genes and gene modules in a gene co-expression network with expression profiles specifically related to regional vulnerability to Aβ and tau pathologies in AD. In addition, we identify distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. These findings may explain the discordance between regional Aβ and tau pathologies. Finally, we propose an analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations.

Cortical thickness reductions associate with brain network architecture in major depressive disorder

BACKGROUND

Cortical thickness reductions in major depressive disorder are distributed across multiple regions. Research has indicated that cortical atrophy is influenced by connectome architecture on a range of neurological and psychiatric diseases. However, whether connectome architecture contributes to changes in cortical thickness in the same manner as it does in depression is unclear. This study aims to explain the distribution of cortical thickness reductions across the cortex in depression by brain connectome architecture.

METHODS

Here, we calculated a differential map of cortical thickness between 110 depression patients and 88 age-, gender-, and education level-matched healthy controls by using T1-weighted images and a structural network reconstructed through the diffusion tensor imaging of control group. We then used a neighborhood deformation model to explore how cortical thickness change in an area is influenced by areas structurally connected to it.

RESULTS

We found that cortical thickness in the frontoparietal and default networks decreased in depression, regional cortical thickness changes were related to reductions in their neighbors and were mainly limited by the frontoparietal and default networks, and the epicenter was in the prefrontal lobe.

CONCLUSION

Current findings suggest that connectome architecture contributes to the irregular topographic distribution of cortical thickness reductions in depression and cortical atrophy is restricted by and dependent on structural foundation.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition

The link between metabolic syndrome (MetS) and neurodegenerative as well cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, cortical morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.

Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET

Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.

Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration

There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical models and human in vivo and post mortem studies. Understanding the consequences of synaptic loss for network function is important to support translational models and guide future therapeutic strategies. To examine this relationship, we recruited 55 participants with syndromes associated with frontotemporal lobar degeneration and 24 healthy controls. We measured synaptic density with positron emission tomography using the radioligand [11C]UCB-J, which binds to the presynaptic vesicle glycoprotein SV2A, neurite dispersion with diffusion magnetic resonance imaging, and network function with task-free magnetic resonance imaging functional connectivity. Synaptic density and neurite dispersion in patients was associated with reduced connectivity beyond atrophy. Functional connectivity moderated the relationship between synaptic density and clinical severity. Our findings confirm the importance of synaptic loss in frontotemporal lobar degeneration syndromes, and the resulting effect on behaviour as a function of abnormal connectivity.

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.

Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks

The identification of a neurodegenerative disorder's distributed pattern of atrophy-or atrophy 'signature'-can lend insights into the cortical networks that degenerate in individuals with specific constellations of symptoms. In addition, this signature can be used as a biomarker to support early diagnoses and to potentially reveal pathological changes associated with said disorder. Here, we characterized the cortical atrophy signature of behavioural variant frontotemporal dementia (bvFTD). We used a data-driven approach to estimate cortical thickness using surface-based analyses in two independent, sporadic bvFTD samples (n = 30 and n = 71, total n = 101), using age- and gender-matched cognitively and behaviourally normal individuals. We found highly similar patterns of cortical atrophy across the two independent samples, supporting the reliability of our bvFTD signature. Next, we investigated whether our bvFTD signature targets specific large-scale cortical networks, as is the case for other neurodegenerative disorders. We specifically asked whether the bvFTD signature topographically overlaps with the salience network, as previous reports have suggested. We hypothesized that because phenotypic presentations of bvFTD are diverse, this would not be the case, and that the signature would cross canonical network boundaries. Consistent with our hypothesis, the bvFTD signature spanned rostral portions of multiple networks, including the default mode, limbic, frontoparietal control and salience networks. We then tested whether the signature comprised multiple anatomical subtypes, which themselves overlapped with specific networks. To explore this, we performed a hierarchical clustering analysis. This yielded three clusters, only one of which extensively overlapped with a canonical network (the limbic network). Taken together, these findings argue against the hypothesis that the salience network is preferentially affected in bvFTD, but rather suggest that-at least in patients who meet diagnostic criteria for the full-blown syndrome-neurodegeneration in bvFTD encompasses a distributed set of prefrontal, insular and anterior temporal nodes of multiple large-scale brain networks, in keeping with the phenotypic diversity of this disorder.

Brain MRI research in neurodegenerative dementia: time to deliver on promises

This scientific commentary refers to ‘Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks’ by Eldaief et al. (https://doi.org/10.1093/brain/awad167).

Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease

Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

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.

Spatial transcriptomic patterns underlying regional vulnerability to amyloid-β and tau pathologies and their relationships to cognitive dysfunction in Alzheimer's disease

Amyloid-β (Aβ) and tau proteins accumulate within distinct neuronal systems in Alzheimer's disease (AD). Although it is not clear why certain brain regions are more vulnerable to Aβ and tau pathologies than others, gene expression may play a role. We studied the association between brain-wide gene expression profiles and regional vulnerability to Aβ (gene-to-Aβ associations) and tau (gene-to-tau associations) pathologies leveraging two large independent cohorts (n = 715) of participants along the AD continuum. We identified several AD susceptibility genes and gene modules in a gene co-expression network with expression profiles related to regional vulnerability to Aβ and tau pathologies in AD. In particular, we found that the positive APOE -to-tau association was only seen in the AD cohort, whereas patients with AD and frontotemporal dementia shared similar positive MAPT -to-tau association. Some AD candidate genes showed sex-dependent negative gene-to-Aβ and gene-to-tau associations. In addition, we identified distinct biochemical pathways associated with the gene-to-Aβ and the gene-to-tau associations. Finally, we proposed a novel analytic framework, linking the identified gene-to-pathology associations to cognitive dysfunction in AD at the individual level, suggesting potential clinical implication of the gene-to-pathology associations. Taken together, our study identified distinct gene expression profiles and biochemical pathways that may explain the discordance between regional Aβ and tau pathologies, and filled the gap between gene-to-pathology associations and cognitive dysfunction in individual AD patients that may ultimately help identify novel personalized pathogenetic biomarkers and therapeutic targets.

One Sentence Summary

We identified replicable cognition-related associations between regional gene expression profiles and selectively regional vulnerability to amyloid-β and tau pathologies in AD.

Presymptomatic and early pathological features of MAPT-associated frontotemporal lobar degeneration

Early pathological features of frontotemporal lobar degeneration (FTLD) due to MAPT pathogenic variants (FTLD-MAPT) are understudied, since early-stage tissue is rarely available. Here, we report unique pathological data from three presymptomatic/early-stage MAPT variant carriers (FTLD Clinical Dementia Rating [FTLD-CDR] = 0-1). We examined neuronal degeneration semi-quantitatively and digitally quantified tau burden in 18 grey matter (9 cortical, 9 subcortical) and 13 white matter (9 cortical, 4 subcortical) regions. We compared presymptomatic/early-stage pathology to an intermediate/end-stage cohort (FTLD-CDR = 2-3) with the same variants (2 L315R, 10 P301L, 6 G272V), and developed a clinicopathological staging model for P301L and G272V variants. The 68-year-old presymptomatic L315R carrier (FTLD-CDR = 0) had limited tau burden morphologically similar to L315R end-stage carriers in middle frontal, antero-inferior temporal, amygdala, (para-)hippocampus and striatum, along with age-related Alzheimer's disease neuropathological change. The 59-year-old prodromal P301L carrier (FTLD-CDR = 0.5) had highest tau burden in anterior cingulate, anterior temporal, middle/superior frontal, and fronto-insular cortex, and amygdala. The 45-year-old early-stage G272V carrier (FTLD-CDR = 1) had highest tau burden in superior frontal and anterior cingulate cortex, subiculum and CA1. The severity and distribution of tau burden showed some regional variability between variants at presymptomatic/early-stage, while neuronal degeneration, mild-to-moderate, was similarly distributed in frontotemporal regions. Early-stage tau burden and neuronal degeneration were both less severe than in intermediate-/end-stage cases. In a subset of regions (10 GM, 8 WM) used for clinicopathological staging, clinical severity correlated strongly with neuronal degeneration (rho = 0.72, p < 0.001), less strongly with GM tau burden (rho = 0.57, p = 0.006), and did not with WM tau burden (p = 0.9). Clinicopathological staging showed variant-specific patterns of early tau pathology and progression across stages. These unique data demonstrate that tau pathology and neuronal degeneration are present already at the presymptomatic/early-stage of FTLD-MAPT, though less severely compared to intermediate/end-stage disease. Moreover, early pathological patterns, especially of tau burden, differ partly between specific MAPT variants.

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.

Anatomical MRI staging of frontotemporal dementia variants

INTRODUCTION

The three clinical variants of frontotemporal dementia (behavioral variant [bvFTD], semantic dementia, and progressive non-fluent aphasia [PNFA]) are likely to develop over decades, from the preclinical stage to death.

METHODS

To describe the long-term chronological anatomical progression of FTD variants, we built lifespan brain charts of normal aging and FTD variants by combining 8022 quality-controlled MRIs from multiple large-scale data-bases, including 107 bvFTD, 44 semantic dementia, and 38 PNFA.

RESULTS

We report in this manuscript the anatomical MRI staging schemes of the three FTD variants by describing the sequential divergence of volumetric trajectories between normal aging and FTD variants. Subcortical atrophy precedes focal cortical atrophy in specific behavioral and/or language networks, with a "radiological" prodromal phase lasting 8-10 years (time elapsed between the first structural alteration and canonical cortical atrophy).

DISCUSSION

Amygdalar and striatal atrophy can be candidate biomarkers for future preclinical/prodromal FTD variants definitions.

HIGHLIGHTS

We describe the chronological MRI staging of the most affected structures in the three frontotemporal dementia (FTD) syndromic variants. In behavioral variant of FTD (bvFTD): bilateral amygdalar, striatal, and insular atrophy precedes fronto-temporal atrophy. In semantic dementia: bilateral amygdalar atrophy precedes left temporal and hippocampal atrophy. In progressive non-fluent aphasia (PNFA): left striatal, insular, and thalamic atrophy precedes opercular atrophy.

Altered Anterior Insular Metabolic Connectivity in Asymptomatic MAPT P301L Carriers

BACKGROUND

The insula is the predominant brain region impaired in behavior variant frontotemporal dementia (bvFTD). However, structural and functional changes in the sub-insula in the asymptomatic stage of bvFTD are unknown.

OBJECTIVE

To describe structural and functional changes in insula subregions in asymptomatic carriers of the P301L mutation of the microtubule-associated protein tau (MAPT) gene and patients with bvFTD.

METHODS

Six asymptomatic MAPT P301L mutation carriers and 12 MAPT negative control subjects of the same pedigree were enrolled, along with 30 patients with a clinical diagnosis of bvFTD and 30 matched controls. All subjects underwent hybrid positron emission tomography/magnetic resonance imaging. Atlas-based parcellation using a fine-grained Brainnetome Atlas was conducted to assess gray matter (GM) volume, metabolism, and metabolic connectivity in the sub-insula (region of interest).

RESULTS

There was no significant GM atrophy or hypometabolism in insula subregions in asymptomatic MAPT P301L carriers, although decreased metabolic connectivity between vIa-middle temporal gyrus, vIa-temporal poles, dIa-middle temporal gyrus and dIa-temporal poles; and increased connectivity between vIa-orbitofrontal, vIa-dorsal lateral superior frontal gyrus, and dIa-orbitofrontal and dIa-dorsal lateral superior frontal gyrus were observed. Patients with bvFTD had significant atrophy and hypometabolism in all insula subregions and decreased metabolic connectivity in the whole brain, including vIa/dIa-middle temporal and vIa/dIa-temporal poles. The standardized uptake value ratios of vIa and dIa were negatively associated with behavioral disinhibition scale scores.

CONCLUSION

Metabolic connectivity is altered in vIa and dIa subregions of the sub-insula in MAPT P301L mutation carriers before the occurrence of atrophy, hypometabolism, and clinical symptoms.

Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain

Neurotransmitter receptors modulate signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression measuring mRNA transcripts is often used as a proxy for receptor densities. In the present report, we comprehensively test the spatial correlation between gene expression and protein density for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography radioligand-based imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT_1A, CB₁, D₂, and MOR). These expression-density associations are related to gene differential stability and can vary between cortical and subcortical structures. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.

TDP-43 CSF Concentrations Increase Exponentially with Age in Metropolitan Mexico City Young Urbanites Highly Exposed to PM2.5 and Ultrafine Particles and Historically Showing Alzheimer and Parkinson's Hallmarks. Brain TDP-43 Pathology in MMC Residents Is Associated with High Cisternal CSF TDP-43 Concentrations

Environmental exposures to fine particulate matter (PM2.5) and ultrafine particle matter (UFPM) are associated with overlapping Alzheimer’s, Parkinson’s and TAR DNA-binding protein 43 (TDP-43) hallmark protein pathologies in young Metropolitan Mexico City (MMC) urbanites. We measured CSF concentrations of TDP-43 in 194 urban residents, including 92 MMC children aged 10.2 ± 4.7 y exposed to PM2.5 levels above the USEPA annual standard and to high UFPM and 26 low pollution controls (11.5 ± 4.4 y); 43 MMC adults (42.3 ± 15.9 y) and 14 low pollution adult controls (33.1 ± 12.0 y); and 19 amyotrophic lateral sclerosis (ALS) patients (52.4 ± 14.1 y). TDP-43 neuropathology and cisternal CSF data from 20 subjects—15 MMC (41.1 ± 18.9 y) and 5 low pollution controls (46 ± 16.01 y)—were included. CSF TDP-43 exponentially increased with age (p < 0.0001) and it was higher for MMC residents. TDP-43 cisternal CSF levels of 572 ± 208 pg/mL in 6/15 MMC autopsy cases forecasted TDP-43 in the olfactory bulb, medulla and pons, reticular formation and motor nuclei neurons. A 16 y old with TDP-43 cisternal levels of 1030 pg/mL exhibited TDP-43 pathology and all 15 MMC autopsy cases exhibited AD and PD hallmarks. Overlapping TDP-43, AD and PD pathologies start in childhood in urbanites with high exposures to PM2.5 and UFPM. Early, sustained exposures to PM air pollution represent a high risk for developing brains and MMC UFPM emissions sources ought to be clearly identified, regulated, monitored and controlled. Prevention of deadly neurologic diseases associated with air pollution ought to be a public health priority and preventive medicine is key.

Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

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.

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% men) 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.