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.

Frontotemporal lobar degeneration targets brain regions linked to expression of recently evolved genes.

In frontotemporal lobar degeneration (FTLD), pathological protein aggregation in specific brain regions is associated with declines in human-specialized social-emotional and language functions. In most patients, disease protein aggregates contain either TDP-43 (FTLD-TDP) or tau (FTLD-tau). Here, we explored whether FTLD-associated regional degeneration patterns relate to regional gene expression of human accelerated regions (HARs), conserved sequences that have undergone positive selection during recent human evolution. To this end, we used structural neuroimaging from patients with FTLD and human brain regional transcriptomic data from controls to identify genes expressed in FTLD-targeted brain regions. We then integrated primate comparative genomic data to test our hypothesis that FTLD targets brain regions linked to expression levels of recently evolved genes. In addition, we asked whether genes whose expression correlates with FTLD atrophy are enriched for genes that undergo cryptic splicing when TDP-43 function is impaired. We found that FTLD-TDP and FTLD-tau subtypes target brain regions with overlapping and distinct gene expression correlates, highlighting many genes linked to neuromodulatory functions. FTLD atrophy-correlated genes were strongly enriched for HARs. Atrophy-correlated genes in FTLD-TDP showed greater overlap with TDP-43 cryptic splicing genes and genes with more numerous TDP-43 binding sites compared with atrophy-correlated genes in FTLD-tau. Cryptic splicing genes were enriched for HAR genes, and vice versa, but this effect was due to the confounding influence of gene length. Analyses performed at the individual-patient level revealed that the expression of HAR genes and cryptically spliced genes within putative regions of disease onset differed across FTLD-TDP subtypes.

Network Connectivity Alterations across the MAPT Mutation Clinical Spectrum.

OBJECTIVE: Microtubule-associated protein tau (MAPT) mutations cause frontotemporal lobar degeneration, and novel biomarkers are urgently needed for early disease detection. We used task-free functional magnetic resonance imaging (fMRI) mapping, a promising biomarker, to analyze network connectivity in symptomatic and presymptomatic MAPT mutation carriers. METHODS: We compared cross-sectional fMRI data between 17 symptomatic and 39 presymptomatic carriers and 81 controls with (1) seed-based analyses to examine connectivity within networks associated with the 4 most common MAPT-associated clinical syndromes (ie, salience, corticobasal syndrome, progressive supranuclear palsy syndrome, and default mode networks) and (2) whole-brain connectivity analyses. We applied K-means clustering to explore connectivity heterogeneity in presymptomatic carriers at baseline. Neuropsychological measures, plasma neurofilament light chain, and gray matter volume were compared at baseline and longitudinally between the presymptomatic subgroups defined by their baseline whole-brain connectivity profiles. RESULTS: Symptomatic and presymptomatic carriers had connectivity disruptions within MAPT-syndromic networks. Compared to controls, presymptomatic carriers showed regions of connectivity alterations with age. Two presymptomatic subgroups were identified by clustering analysis, exhibiting predominantly either whole-brain hypoconnectivity or hyperconnectivity at baseline. At baseline, these two presymptomatic subgroups did not differ in neuropsychological measures, although the hypoconnectivity subgroup had greater plasma neurofilament light chain levels than controls. Longitudinally, both subgroups showed visual memory decline (vs controls), yet the subgroup with baseline hypoconnectivity also had worsening verbal memory and neuropsychiatric symptoms, and extensive bilateral mesial temporal gray matter decline. INTERPRETATION: Network connectivity alterations arise as early as the presymptomatic phase. Future studies will determine whether presymptomatic carriers' baseline connectivity profiles predict symptomatic conversion. ANN NEUROL 2023;94:632-646.

Neuropsychiatric symptoms and imbalance of atrophy in behavioral variant frontotemporal dementia.

Behavioral variant frontotemporal dementia is characterized by heterogeneous frontal, insular, and anterior temporal atrophy patterns that vary along left-right and dorso-ventral axes. Little is known about how these structural imbalances impact clinical symptomatology. The goal of this study was to assess the frequency of frontotemporal asymmetry (right- or left-lateralization) and dorsality (ventral or dorsal predominance of atrophy) and to investigate their clinical correlates. Neuropsychiatric symptoms and structural images were analyzed for 250 patients with behavioral variant frontotemporal dementia. Frontotemporal atrophy was most often symmetric while left-lateralized (9%) and right-lateralized (17%) atrophy were present in a minority of patients. Atrophy was more often ventral (32%) than dorsal (3%) predominant. Patients with right-lateralized atrophy were characterized by higher severity of abnormal eating behavior and hallucinations compared to those with left-lateralized atrophy. Subsequent analyses clarified that eating behavior was associated with right atrophy to a greater extent than a lack of left atrophy, and hallucinations were driven mainly by right atrophy. Dorsality analyses showed that anxiety, euphoria, and disinhibition correlated with ventral-predominant atrophy. Agitation, irritability, and depression showed greater severity with a lack of regional atrophy, including in dorsal regions. Aberrant motor behavior and apathy were not explained by asymmetry or dorsality. This study provides additional insight into how anatomical heterogeneity influences the clinical presentation of patients with behavioral variant frontotemporal dementia. Behavioral symptoms can be associated not only with the presence or absence of focal atrophy, but also with right/left or dorsal/ventral imbalance of gray matter volume.

Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology.

The outflow of the autonomic nervous system (ANS) is continuous and dynamic, but its functional organization is not well understood. Whether ANS patterns accompany emotions, or arise in basal physiology, remain unsettled questions in the field. Here, we searched for brief ANS patterns amidst continuous, multichannel physiological recordings in 45 healthy older adults. Participants completed an emotional reactivity task in which they viewed video clips that elicited a target emotion (awe, sadness, amusement, disgust, or nurturant love); each video clip was preceded by a pre-trial baseline period and followed by a post-trial recovery period. Participants also sat quietly for a separate 2-min resting period to assess basal physiology. Using principal components analysis and unsupervised clustering algorithms to reduce the second-by-second physiological data during the emotional reactivity task, we uncovered five ANS states. Each ANS state was characterized by a unique constellation of patterned physiological changes that differentiated among the trials of the emotional reactivity task. These ANS states emerged and dissipated over time, with each instance lasting several seconds on average. ANS states with similar structures were also detectable in the resting period but were intermittent and of smaller magnitude. Our results offer new insights into the functional organization of the ANS. By assembling short-lived, patterned changes, the ANS is equipped to generate a wide range of physiological states that accompany emotions and that contribute to the architecture of basal physiology.

A dynamic gradient architecture generates brain activity states.

The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.

Default Mode Network quantitative diffusion and resting-state functional magnetic resonance imaging correlates in sporadic Creutzfeldt-Jakob disease.

Grey matter involvement is a well-known feature in sporadic Creutzfeldt-Jakob disease (sCJD), yet precise anatomy-based quantification of reduced diffusivity is still not fully understood. Default Mode Network (DMN) areas have been recently demonstrated as selectively involved in sCJD, and functional connectivity has never been investigated in prion diseases. We analyzed the grey matter involvement using a quantitatively multi-parametric MRI approach. Specifically, grey matter mean diffusivity of 37 subjects with sCJD was compared with that of 30 age-matched healthy controls with a group-wise approach. Differences in mean diffusivity were also examined between the cortical (MM(V)1, MM(V)2C, and VV1) and subcortical (VV2 and MV2K) subgroups of sCJD for those with autopsy data available (n = 27, 73%). We also assessed resting-state functional connectivity of both ventral and dorsal components of DMN in a subset of subject with a rs-fMRI dataset available (n = 17). Decreased diffusivity was predominantly present in posterior cortical regions of the DMN, but also outside of the DMN in temporal areas and in a few limbic and frontal areas, in addition to extensive deep nuclei involvement. Both subcortical and cortical sCJD subgroups showed decreased diffusivity subcortically, whereas only the cortical type expressed significantly decreased diffusivity cortically, mainly in parietal, occipital, and medial-inferior temporal cortices bilaterally. Interestingly, we found abnormally increased connectivity in both dorsal and ventral components of the DMN in sCJD subjects compared with healthy controls. The significance and possible utility of functional imaging as a biomarker for tracking disease progression in prion disease needs to be explored further.

Salience Network Atrophy Links Neuron Type-Specific Pathobiology to Loss of Empathy in Frontotemporal Dementia.

Each neurodegenerative syndrome reflects a stereotyped pattern of cellular, regional, and large-scale brain network degeneration. In behavioral variant of frontotemporal dementia (bvFTD), a disorder of social-emotional function, von Economo neurons (VENs), and fork cells are among the initial neuronal targets. These large layer 5 projection neurons are concentrated in the anterior cingulate and frontoinsular (FI) cortices, regions that anchor the salience network, a large-scale system linked to social-emotional function. Here, we studied patients with bvFTD, amyotrophic lateral sclerosis (ALS), or both, given that these syndromes share common pathobiological and genetic factors. Our goal was to determine how neuron type-specific TAR DNA-binding protein of 43 kDa (TDP-43) pathobiology relates to atrophy in specific brain structures and to loss of emotional empathy, a cardinal feature of bvFTD. We combined questionnaire-based empathy assessments, in vivo structural MR imaging, and quantitative histopathological data from 16 patients across the bvFTD/ALS spectrum. We show that TDP-43 pathobiology within right FI VENs and fork cells is associated with salience network atrophy spanning insular, medial frontal, and thalamic regions. Gray matter degeneration within these structures mediated loss of emotional empathy, suggesting a chain of influence linking the cellular, regional/network, and behavioral levels in producing signature bvFTD clinical features.

State and trait characteristics of anterior insula time-varying functional connectivity.

The human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both "state" and "trait" characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.

Evidence of corticofugal tau spreading in patients with frontotemporal dementia.

Common neurodegenerative diseases feature progressive accumulation of disease-specific protein aggregates in selectively vulnerable brain regions. Increasing experimental evidence suggests that misfolded disease proteins exhibit prion-like properties, including the ability to seed corruptive templating and self-propagation along axons. Direct evidence for transneuronal spread in patients, however, remains limited. To test predictions made by the transneuronal spread hypothesis in human tissues, we asked whether tau deposition within axons of the corticospinal and corticopontine pathways can be predicted based on clinical syndromes and cortical atrophy patterns seen in frontotemporal lobar degeneration (FTLD). Sixteen patients with Pick's disease, 21 with corticobasal degeneration, and 3 with FTLD-MAPT were included, spanning a range of clinical syndromes across the frontotemporal dementia (FTD) spectrum. Cortical involvement was measured using a neurodegeneration score, a tau score, and a composite score based on semiquantitative ratings and complemented by an MRI-based cortical atrophy W-map based on antemortem imaging. Midbrain cerebral peduncle and pontine base descending fibers were divided into three subregions, representing prefrontopontine, corticospinal, and parieto-temporo-occipital fiber pathways. Tau area fraction was calculated in each subregion and related to clinical syndrome and cortical measures. Within each clinical syndrome, there were predicted relationships between cortical atrophy patterns and axonal tau deposition in midbrain cerebral peduncle and pontine base. Between syndromes, contrasting and predictable patterns of brainstem axonal tau deposition emerged, with, for example, greater tau in prefrontopontine fibers in behavioral variant FTD and in corticospinal fibers in corticobasal syndrome. Finally, semiquantitative and quantitative cortical degeneration scores predicted brainstem axonal tau deposition based on anatomical principles. Taken together, these findings provide important human evidence in support of axonal tau spreading in patients with specific forms of tau-related neurodegeneration.

Neuropathological correlates of structural and functional imaging biomarkers in 4-repeat tauopathies.

Neurodegenerative dementia syndromes are characterized by spreading of pathological protein deposition along syndrome-specific neural networks. Structural and functional MRI measures can assess the integrity of these networks and have been proposed as biomarkers of disease progression for clinical trials. The relationship between in vivo imaging measures and pathological features, at the single subject level, remains largely unknown. Patient-specific maps of atrophy and seed-based intrinsic connectivity disruption, as compared to normal controls, were obtained for 27 patients subsequently diagnosed with progressive supranuclear palsy (n = 16, seven males, age at death 68.9 ± 6.0 years, imaging-to-pathology interval = 670.2 ± 425.1 days) or corticobasal degeneration (n = 11, two males, age at death 66.7 ± 5.4 years, imaging-to-pathology interval = 696.2 ± 482.2 days). A linear mixed effect model with crossed random effects was used to test regional and single-subject level associations between post-mortem regional measures of neurodegeneration and tau inclusion burden, on the one hand, and regional volume loss and seed-based intrinsic connectivity reduction, on the other. A significant association was found between tau inclusion burden and in vivo volume loss, at the regional level and independent of neurodegeneration severity, in both progressive supranuclear palsy [n = 340 regions; beta 0.036; 95% confidence interval (CI): 0.001, 0.072; P = 0.046] and corticobasal degeneration (n = 215 regions; beta 0.044; 95% CI: 0.009, 0.079; P = 0.013). We also found a significant association between post-mortem neurodegeneration and in vivo volume loss in both progressive supranuclear palsy (n = 340 regions; beta 0.155; 95% CI: 0.061, 0.248; P = 0.001) and corticobasal degeneration (n = 215 regions; beta 0.277; 95% CI: 0.104, 0.450; P = 0.002). We found a significant association between regional neurodegeneration and intrinsic connectivity dysfunction in corticobasal degeneration (n = 215 regions; beta 0.074; 95% CI: 0.005, 0.143; P = 0.035), but no other associations between post-mortem measures of tauopathy and intrinsic connectivity dysfunction reached statistical significance. Our data suggest that in vivo structural imaging measures reflect independent contributions from neurodegeneration and tau burden in progressive supranuclear palsy and corticobasal degeneration. Seed-based measures of intrinsic connectivity dysfunction showed less reliable predictive value when used as in vivo biomarkers of tauopathy. The findings provide important guidance for the use of imaging biomarkers as indirect in vivo assays of microscopic pathology.

Network Architecture Underlying Basal Autonomic Outflow: Evidence from Frontotemporal Dementia.

The salience network is a distributed neural system that maintains homeostasis by regulating autonomic nervous system activity and social-emotional function. Here we examined how within-network connectivity relates to individual differences in human (including males and females) baseline parasympathetic and sympathetic nervous activity. We measured resting autonomic nervous system physiology in 24 healthy controls and 23 patients with behavioral variant frontotemporal dementia (bvFTD), a neurodegenerative disease characterized by baseline autonomic deficits. Participants also underwent structural and task-free fMRI. First, we used voxel-based morphometry to determine whether salience network atrophy was associated with lower baseline respiratory sinus arrhythmia (a parasympathetic measure) and skin conductance level (a sympathetic measure) in bvFTD. Next, we examined whether functional connectivity deficits in 21 autonomic-relevant, salience network node-pairs related to baseline autonomic dysfunction. Lower baseline respiratory sinus arrhythmia was associated with smaller volume in left ventral anterior insula (vAI), weaker connectivity between bilateral vAI and bilateral anterior cingulate cortex (ACC), and stronger connectivity between bilateral ACC and bilateral hypothalamus/amygdala. Lower baseline skin conductance level, in contrast, was associated with smaller volume in inferior temporal gyrus, dorsal mid-insula, and hypothalamus; weaker connectivity between bilateral ACC and right hypothalamus/amygdala; and stronger connectivity between bilateral dorsal anterior insula and periaqueductal gray. Our results suggest that baseline parasympathetic and sympathetic tone depends on the integrity of lateralized salience network hubs (left vAI for parasympathetic and right hypothalamus/amygdala for sympathetic) and highly calibrated ipsilateral and contralateral network connections. In bvFTD, deficits in this system may underlie resting parasympathetic and sympathetic disruption.SIGNIFICANCE STATEMENT The salience network maintains homeostasis and regulates autonomic nervous system activity. Whether within-network connectivity patterns underlie individual differences in resting parasympathetic and sympathetic nervous system activity, however, is not well understood. We measured baseline autonomic nervous system activity in healthy controls and patients with behavioral variant frontotemporal dementia, a neurodegenerative disease characterized by resting autonomic deficits, and probed how salience network dysfunction relates to diminished parasympathetic and sympathetic outflow. Our results indicate that baseline parasympathetic and sympathetic tone are the product of complex, opposing intranetwork nodal interactions and depend on the integrity of highly tuned, lateralized salience network hubs (i.e., left ventral anterior insula for parasympathetic activity and right hypothalamus/amygdala for sympathetic activity).

The Longitudinal Trajectory of Default Mode Network Connectivity in Healthy Older Adults Varies As a Function of Age and Is Associated with Changes in Episodic Memory and Processing Speed.

The default mode network (DMN) supports memory functioning and may be sensitive to preclinical Alzheimer's pathology. Little is known, however, about the longitudinal trajectory of this network's intrinsic functional connectivity (FC). In this study, we evaluated longitudinal FC in 111 cognitively normal older human adults (ages 49-87, 46 women/65 men), 92 of whom had at least three task-free fMRI scans (n = 353 total scans). Whole-brain FC and three DMN subnetworks were assessed: (1) within-DMN, (2) between anterior and posterior DMN, and (3) between medial temporal lobe network and posterior DMN. Linear mixed-effects models demonstrated significant baseline age × time interactions, indicating a nonlinear trajectory. There was a trend toward increasing FC between ages 50-66 and significantly accelerating declines after age 74. A similar interaction was observed for whole-brain FC. APOE status did not predict baseline connectivity or change in connectivity. After adjusting for network volume, changes in within-DMN connectivity were specifically associated with changes in episodic memory and processing speed but not working memory or executive functions. The relationship with processing speed was attenuated after covarying for white matter hyperintensities (WMH) and whole-brain FC, whereas within-DMN connectivity remained associated with memory above and beyond WMH and whole-brain FC. Whole-brain and DMN FC exhibit a nonlinear trajectory, with more rapid declines in older age and possibly increases in connectivity early in the aging process. Within-DMN connectivity is a marker of episodic memory performance even among cognitively healthy older adults.SIGNIFICANCE STATEMENT Default mode network and whole-brain connectivity, measured using task-free fMRI, changed nonlinearly as a function of age, with some suggestion of early increases in connectivity. For the first time, longitudinal changes in DMN connectivity were shown to correlate with changes in episodic memory, whereas volume changes in relevant brain regions did not. This relationship was not accounted for by white matter hyperintensities or mean whole-brain connectivity. Functional connectivity may be an early biomarker of changes in aging but should be used with caution given its nonmonotonic nature, which could complicate interpretation. Future studies investigating longitudinal network changes should consider whole-brain changes in connectivity.

Minimum spanning tree analysis of the human connectome.

One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion-weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null-model. The MST of individual subjects matched this reference MST for a mean 58%-88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so-called rich club nodes (a subset of high-degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical-subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.

Clinicopathological correlations in behavioural variant frontotemporal dementia.

Accurately predicting the underlying neuropathological diagnosis in patients with behavioural variant frontotemporal dementia (bvFTD) poses a daunting challenge for clinicians but will be critical for the success of disease-modifying therapies. We sought to improve pathological prediction by exploring clinicopathological correlations in a large bvFTD cohort. Among 438 patients in whom bvFTD was either the top or an alternative possible clinical diagnosis, 117 had available autopsy data, including 98 with a primary pathological diagnosis of frontotemporal lobar degeneration (FTLD), 15 with Alzheimer's disease, and four with amyotrophic lateral sclerosis who lacked neurodegenerative disease-related pathology outside of the motor system. Patients with FTLD were distributed between FTLD-tau (34 patients: 10 corticobasal degeneration, nine progressive supranuclear palsy, eight Pick's disease, three frontotemporal dementia with parkinsonism associated with chromosome 17, three unclassifiable tauopathy, and one argyrophilic grain disease); FTLD-TDP (55 patients: nine type A including one with motor neuron disease, 27 type B including 21 with motor neuron disease, eight type C with right temporal lobe presentations, and 11 unclassifiable including eight with motor neuron disease), FTLD-FUS (eight patients), and one patient with FTLD-ubiquitin proteasome system positive inclusions (FTLD-UPS) that stained negatively for tau, TDP-43, and FUS. Alzheimer's disease was uncommon (6%) among patients whose only top diagnosis during follow-up was bvFTD. Seventy-nine per cent of FTLD-tau, 86% of FTLD-TDP, and 88% of FTLD-FUS met at least 'possible' bvFTD diagnostic criteria at first presentation. The frequency of the six core bvFTD diagnostic features was similar in FTLD-tau and FTLD-TDP, suggesting that these features alone cannot be used to separate patients by major molecular class. Voxel-based morphometry revealed that nearly all pathological subgroups and even individual patients share atrophy in anterior cingulate, frontoinsula, striatum, and amygdala, indicating that degeneration of these regions is intimately linked to the behavioural syndrome produced by these diverse aetiologies. In addition to these unifying features, symptom profiles also differed among pathological subtypes, suggesting distinct anatomical vulnerabilities and informing a clinician's prediction of pathological diagnosis. Data-driven classification into one of the 10 most common pathological diagnoses was most accurate (up to 60.2%) when using a combination of known predictive factors (genetic mutations, motor features, or striking atrophy patterns) and the results of a discriminant function analysis that incorporated clinical, neuroimaging, and neuropsychological data.

Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia.

Neurodegeneration has been hypothesized to follow predetermined large-scale networks through the trans-synaptic spread of toxic proteins from a syndrome-specific epicentre. To date, no longitudinal neuroimaging study has tested this hypothesis in vivo in frontotemporal dementia spectrum disorders. The aim of this study was to demonstrate that longitudinal progression of atrophy in non-fluent/agrammatic variant primary progressive aphasia spreads over time from a syndrome-specific epicentre to additional regions, based on their connectivity to the epicentre in healthy control subjects. The syndrome-specific epicentre of the non-fluent/agrammatic variant of primary progressive aphasia was derived in a group of 10 mildly affected patients (clinical dementia rating equal to 0) using voxel-based morphometry. From this region, the inferior frontal gyrus (pars opercularis), we derived functional and structural connectivity maps in healthy controls (n = 30) using functional magnetic resonance imaging at rest and diffusion-weighted imaging tractography. Graph theory analysis was applied to derive functional network features. Atrophy progression was calculated using voxel-based morphometry longitudinal analysis on 34 non-fluent/agrammatic patients. Correlation analyses were performed to compare volume changes in patients with connectivity measures of the healthy functional and structural speech/language network. The default mode network was used as a control network. From the epicentre, the healthy functional connectivity network included the left supplementary motor area and the prefrontal, inferior parietal and temporal regions, which were connected through the aslant, superior longitudinal and arcuate fasciculi. Longitudinal grey and white matter changes were found in the left language-related regions and in the right inferior frontal gyrus. Functional connectivity strength in the healthy speech/language network, but not in the default network, correlated with longitudinal grey matter changes in the non-fluent/agrammatic variant of primary progressive aphasia. Graph theoretical analysis of the speech/language network showed that regions with shorter functional paths to the epicentre exhibited greater longitudinal atrophy. The network contained three modules, including a left inferior frontal gyrus/supplementary motor area, which was most strongly connected with the epicentre. The aslant tract was the white matter pathway connecting these two regions and showed the most significant correlation between fractional anisotropy and white matter longitudinal atrophy changes. This study showed that the pattern of longitudinal atrophy progression in the non-fluent/agrammatic variant of primary progressive aphasia relates to the strength of connectivity in pre-determined functional and structural large-scale speech production networks. These findings support the hypothesis that the spread of neurodegeneration occurs by following specific anatomical and functional neuronal network architectures.

Connected brains and minds--The UMCD repository for brain connectivity matrices.

We describe the USC Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an interactive web-based platform for brain connectivity matrix sharing and analysis. The site enables users to download connectivity matrices shared by other users, upload matrices from their own published studies, or select a specific matrix and perform a real-time graph theory-based analysis and visualization of network properties. The data shared on the site span a broad spectrum of functional and structural brain connectivity information from humans across the entire age range (fetal to age 89), representing an array of different neuropsychiatric and neurodegenerative disease populations (autism spectrum disorder, ADHD, and APOE-4 carriers). An analysis combining 7 different datasets shared on the site illustrates the diversity of the data and the potential for yielding deeper insight by assessing new connectivity matrices with respect to population-wide network properties represented in the UMCD.

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or "topics," provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions.

Brain network local interconnectivity loss in aging APOE-4 allele carriers.

Old age and possession of the APOE-4 allele are the two main risk factors for developing later onset Alzheimer's Disease (AD). Carriers of the APOE-4 allele have known differences in intrinsic functional brain network activity across the life span. These individuals also demonstrate specific regional differences in gray and white matter gross structure. However, the relationship of these variations to whole brain structural network connectivity remains unclear. We performed diffusion tensor imaging (DTI), T1 structural imaging, and cognitive testing on aging APOE-4 noncarriers (n = 30; mean age = 63.8±8.3) and APOE-4 carriers (n = 25; mean age = 60.8 ±9.7). Fiber tractography was used to derive whole brain structural graphs, and graph theory was applied to assess structural network properties. Network communication efficiency was determined for each network by quantifying local interconnectivity, global integration, and the balance between these, the small worldness. Relative to noncarriers, APOE-4 carriers demonstrated an accelerated age-related loss of mean local interconnectivity (r = -0.64, P ≤ 0.01) and regional local interconnectivity decreases in the precuneus (r = -0.64), medial orbitofrontal cortex (r = -0.5), and lateral parietal cortex (r = -0.54). APOE-4 carriers also showed significant age-related loss in mean cortical thickness (r = -0.52, P < 0.05). Cognitively, APOE-4 carriers had significant negative correlations of age and performance on two episodic memory tasks (P < 0.05). This genotype-specific pattern of structural connectivity change with age thus appears related to changes in gross cortical structure and cognition, potentially affecting the rate and/or spatial distribution of AD-related pathology.

Dysfunctional Cortical Gradient Topography in Treatment-Resistant Major Depressive Disorder.

BACKGROUND: Treatment-resistant depression (TRD) refers to patients with major depressive disorder who do not remit after 2 or more antidepressant trials. TRD is common and highly debilitating, but its neurobiological basis remains poorly understood. Recent neuroimaging studies have revealed cortical connectivity gradients that dissociate primary sensorimotor areas from higher-order associative cortices. This fundamental topography determines cortical information flow and is affected by psychiatric disorders. We examined how TRD impacts gradient-based hierarchical cortical organization. METHODS: In this secondary study, we analyzed resting-state functional magnetic resonance imaging data from a mindfulness-based intervention enrolling 56 patients with TRD and 28 healthy control subjects. Using gradient extraction tools, baseline measures of cortical gradient dispersion within and between functional brain networks were derived, compared across groups, and associated with graph theoretical measures of network topology. In patients, correlation analyses were used to associate measures of cortical gradient dispersion with clinical measures of anxiety, depression, and mindfulness at baseline and following the intervention. RESULTS: Cortical gradient dispersion was reduced within major intrinsic brain networks in patients with TRD. Reduced cortical gradient dispersion correlated with increased network degree assessed through graph theory-based measures of network topology. Lower dispersion among default mode, control, and limbic network nodes related to baseline levels of trait anxiety, depression, and mindfulness. Patients' baseline limbic network dispersion predicted trait anxiety scores 24 weeks after the intervention. CONCLUSIONS: Our findings provide preliminary support for widespread alterations in cortical gradient architecture in TRD, implicating a significant role for transmodal and limbic networks in mediating depression, anxiety, and lower mindfulness in patients with TRD.