A network diffusion model of disease progression in dementia

Connectivity Disruption, Atrophy, and Hypometabolism within Posterior Cingulate Networks in Alzheimer's Disease

The posterior cingulate cortex (PCC) is a critical brain network hub particularly sensitive to Alzheimer's disease (AD) and can be subdivided into ventral (vPCC) and dorsal (dPCC) regions. The aim of the present study was to highlight functional connectivity (FC) disruption, atrophy, and hypometabolism within the ventral and dorsal PCC networks in patients with amnestic mild cognitive impairment (aMCI) or AD. Forty-three healthy elders (HE) (68.7 ± 6 years), 34 aMCI (73.4 ± 6.8 years) and 24 AD (70.9 ± 9.1 years) patients underwent resting-state functional MRI, anatomical T1-weighted MRI and FDG-PET scans. We compared FC maps obtained from the vPCC and dPCC seeds in HE to identify the ventral and dorsal PCC networks. We then compared patients and HE on FC, gray matter volume and metabolism within each network. In HE, the ventral PCC network involved the hippocampus and posterior occipitotemporal and temporoparietal regions, whereas the dorsal PCC network included mainly frontal, middle temporal and temporoparietal areas. aMCI patients had impaired ventral network FC in the bilateral hippocampus, but dorsal network FC was preserved. In AD, the ventral network FC disruption had spread to the left parahippocampal and angular regions, while the dorsal network FC was also affected in the right middle temporal cortex. The ventral network was atrophied in the bilateral hippocampus in aMCI patients, and in the vPCC and angular regions as well in AD patients. The dorsal network was only atrophied in AD patients, in the dPCC, bilateral supramarginal and temporal regions. By contrast, hypometabolism was already present in both the vPCC and dPCC networks in aMCI patients, and further extended to include the whole networks in AD patients. The vPCC and dPCC connectivity networks were differentially sensitive to AD. Atrophy and FC disruption were only present in the vPCC network in aMCI patients, and extended to the dPCC network in AD patients, suggesting that the pathology spreads from the vPCC to the dPCC networks. By contrast, hypometabolism seemed to follow a different route, as it was present in both networks since the aMCI stage, possibly reflecting not only local disruption but also distant synaptic dysfunction.

Multiple beneficial effects of melanocortin MC4 receptor agonists in experimental neurodegenerative disorders: Therapeutic perspectives

Melanocortin peptides induce neuroprotection in acute and chronic experimental neurodegenerative conditions. Melanocortins likewise counteract systemic responses to brain injuries. Furthermore, they promote neurogenesis by activating critical signaling pathways. Melanocortin-induced long-lasting improvement in synaptic activity and neurological performance, including learning and memory, sensory-motor orientation and coordinated limb use, has been consistently observed in experimental models of acute and chronic neurodegeneration. Evidence indicates that the neuroprotective and neurogenic effects of melanocortins, as well as the protection against systemic responses to a brain injury, are mediated by brain melanocortin 4 (MC₄) receptors, through an involvement of the vagus nerve. Here we discuss the targets and mechanisms underlying the multiple beneficial effects recently observed in animal models of neurodegeneration. We comment on the potential clinical usefulness of melanocortin MC₄ receptor agonists as neuroprotective and neuroregenerative agents in ischemic stroke, subarachnoid hemorrhage, traumatic brain injury, spinal cord injury, and Alzheimer's disease.

The Many Faces of Elongator in Neurodevelopment and Disease

Development of the nervous system requires a variety of cellular activities, such as proliferation, migration, axonal outgrowth and guidance and synapse formation during the differentiation of neural precursors into mature neurons. Malfunction of these highly regulated and coordinated events results in various neurological diseases. The Elongator complex is a multi-subunit complex highly conserved in eukaryotes whose function has been implicated in the majority of cellular activities underlying neurodevelopment. These activities include cell motility, actin cytoskeleton organization, exocytosis, polarized secretion, intracellular trafficking and the maintenance of neural function. Several studies have associated mutations in Elongator subunits with the neurological disorders familial dysautonomia (FD), intellectual disability (ID), amyotrophic lateral sclerosis (ALS) and rolandic epilepsy (RE). Here, we review the various cellular activities assigned to this complex and discuss the implications for neural development and disease. Further research in this area has the potential to generate new diagnostic tools, better prevention strategies and more effective treatment options for a wide variety of neurological disorders.

Tau Prion Strains Dictate Patterns of Cell Pathology, Progression Rate, and Regional Vulnerability In Vivo

Tauopathies are neurodegenerative disorders that affect distinct brain regions, progress at different rates, and exhibit specific patterns of tau accumulation. The source of this diversity is unknown. We previously characterized two tau strains that stably maintain unique conformations in vitro and in vivo, but did not determine the relationship of each strain to parameters that discriminate between tauopathies such as regional vulnerability or rate of spread. We have now isolated and characterized 18 tau strains in cells based on detailed biochemical and biological criteria. Inoculation of PS19 transgenic tau (P301S) mice with these strains causes strain-specific intracellular pathology in distinct cell types and brain regions, and induces different rates of network propagation. In this system, strains alone are sufficient to account for diverse neuropathological presentations, similar to those that define human tauopathies. Further study of these strains can thus establish a structural logic that governs these biological effects.

Disrupted Brain Structural Connectivity: Pathological Interactions Between Genetic APOE ε4 Status and Developed MCI Condition

The ε4 allele of the apolipoprotein E (APOE) gene and mild cognitive impairment (MCI) are both risk factors for Alzheimer's disease (AD). One factor is genetic, and the other is a developed condition during the aging process. The current study intended to discover the interactions of these two factors, which may be useful in the construction of a sensitive biomarker for early identification and intervention. Eight hundred eighty-five Chinese Han ethnic subjects (aged 55 and older) completed neuropsychological tests and APOE genotyping. One hundred ten of these participants underwent magnetic resonance imaging (MRI) for T1 structural and diffusion tensor imaging scans. Subjects were divided into four groups according to APOE ε4 carrying status and MCI condition: ε4+ MCI, ε4+ normal cognition (NC), ε4- MCI, and ε4- NC. In the studied Han population in Beijing, 16.9 % (ε2ε4 = 1.1 %, ε3ε4 = 14.8 %, and ε4ε4 = 0.9 %) carried at least one ε4 allele. Significant interactions between APOE ε4 and MCI were found in general cognitive function (p = 0.001) and white matter connectivity network (clustering coefficient, p = 0.004, and local efficiency, p = 0.011); the combination of ε4 positivity and MCI was accompanied by reductions in Mini-Mental Status Examination (MMSE) scores, global white matter network connectivity, and the right hippocampus (rHIP) nodal efficiency within that network (false discovery rate (FDR), p < 0.05). Our results suggest the presence of a genetic risk and MCI led to more severe pathological symptoms and could be informative in the implementation of clinical trials for early stages of AD.

Localizing Sources of Brain Disease Progression with Network Diffusion Model

Pinpointing the sources of dementia is crucial to the effective treatment of neurodegenerative diseases. In this paper, we propose a diffusion model with impulsive sources over the brain connectivity network to model the progression of brain atrophy. To reliably estimate the atrophy sources, we impose sparse regularization on the source distribution and solve the inverse problem with an efficient gradient descent method. We localize the possible origins of Alzheimer's disease (AD) based on a large set of repeated magnetic resonance imaging (MRI) scans in Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The distribution of the sources averaged over the sample population is evaluated. We find that the dementia sources have different concentrations in the brain lobes for AD patients and mild cognitive impairment (MCI) subjects, indicating possible switch of the dementia driving mechanism. Moreover, we demonstrate that we can effectively predict changes of brain atrophy patterns with the proposed model. Our work could help understand the dynamics and origin of dementia, as well as monitor the progression of the diseases in an early stage.

Small-World Brain Networks Revisited

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.

Network Analysis on Predicting Mean Diffusivity Change at Group Level in Temporal Lobe Epilepsy

The two most common types of temporal lobe epilepsy are medial temporal sclerosis (TLE-MTS) epilepsy and MRI-normal temporal lobe epilepsy (TLE-no). TLE-MTS is specified by its stereotyped focus and spread pattern of neuronal damage, with pronounced neuronal loss in the hippocampus. TLE-no exhibits normal-appearing hippocampus and more widespread neuronal loss. In both cases, neuronal loss spread appears to be constrained by the white matter connections. Both varieties of epilepsy reveal pathological abnormalities in increased mean diffusivity (MD). We model MD distribution as a simple consequence of the propagation of neuronal damage. By applying this model on the structural brain connectivity network of healthy subjects, we can predict at group level the MD gray matter change in the epilepsy cohorts relative to a control group. Diffusion tensor imaging images were acquired from 10 patients with TLE-MTS, 11 patients with TLE-no, and 35 healthy subjects. Statistical validation at the group level suggests high correlation with measured neuronal loss (R = 0.56 for the TLE-MTS group and R = 0.364 for the TLE-no group). The results of this exploratory work pave the way for potential future clinical application of the proposed model on individual patients, including predicting neuronal loss spread, identification of seizure onset zones, and helping in surgical planning.

Using Individualized Brain Network for Analyzing Structural Covariance of the Cerebral Cortex in Alzheimer's Patients

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are “small world.” There were significant difference between NC and AD group in characteristic path lengths (z = −2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.

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.

Opportunities and Challenges for Psychiatry in the Connectomic Era

Most major psychiatric disorders arise from disturbances of anatomically distributed neural systems rather than isolated dysfunction of circumscribed brain regions. The past decade has witnessed rapid advances in our capacity to measure, map, and model neural connectivity in diverse species and at different resolution scales, from the level of individual neurons and synapses to large-scale systems spanning the entire brain. In this review, we consider how these techniques, when grounded in the theory and methods of network science, can contribute to a biological understanding of mental illness. We focus in particular on attempts to accurately map brain network disturbances in clinical populations and to model the mechanistic causes of these changes. This work suggests that pathology within highly connected hub regions is a consistent finding across a broad array of phenotypically diverse disorders, and that disparate changes in brain network organization can sometimes be explained by a surprisingly small and simple set of mechanisms.

Two's company, three (or more) is a simplex : Algebraic-topological tools for understanding higher-order structure in neural data

The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit of interest in a brain is a dyad - two nodes (neurons or brain regions) connected by an edge. While rarely mentioned, this fundamental assumption inherently limits the types of neural structure and function that graphs can be used to model. Here, we describe a generalization of graphs that overcomes these limitations, thereby offering a broad range of new possibilities in terms of modeling and measuring neural phenomena. Specifically, we explore the use of simplicial complexes: a structure developed in the field of mathematics known as algebraic topology, of increasing applicability to real data due to a rapidly growing computational toolset. We review the underlying mathematical formalism as well as the budding literature applying simplicial complexes to neural data, from electrophysiological recordings in animal models to hemodynamic fluctuations in humans. Based on the exceptional flexibility of the tools and recent ground-breaking insights into neural function, we posit that this framework has the potential to eclipse graph theory in unraveling the fundamental mysteries of cognition.

Prions and Protein Assemblies that Convey Biological Information in Health and Disease

Prions derived from the prion protein (PrP) were first characterized as infectious agents that transmit pathology between individuals. However, the majority of cases of neurodegeneration caused by PrP prions occur sporadically. Proteins that self-assemble as cross-beta sheet amyloids are a defining pathological feature of infectious prion disorders and all major age-associated neurodegenerative diseases. In fact, multiple non-infectious proteins exhibit properties of template-driven self-assembly that are strikingly similar to PrP. Evidence suggests that like PrP, many proteins form aggregates that propagate between cells and convert cognate monomer into ordered assemblies. We now recognize that numerous proteins assemble into macromolecular complexes as part of normal physiology, some of which are self-amplifying. This review highlights similarities among infectious and non-infectious neurodegenerative diseases associated with prions, emphasizing the normal and pathogenic roles of higher-order protein assemblies. We propose that studies of the structural and cellular biology of pathological versus physiological aggregates will be mutually informative.

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points - quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer's disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant's brain connectivity into the future.

Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease

SEE SARAZIN ET AL DOI101093/BRAIN/AWW041 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: The advent of the positron emission tomography tracer (18)F-AV1451 provides the unique opportunity to visualize the regional distribution of tau pathology in the living human brain. In this study, we tested the hypothesis that tau pathology is closely linked to symptomatology and patterns of glucose hypometabolism in Alzheimer's disease, in contrast to the more diffuse distribution of amyloid-β pathology. We included 20 patients meeting criteria for probable Alzheimer's disease dementia or mild cognitive impairment due to Alzheimer's disease, presenting with a variety of clinical phenotypes, and 15 amyloid-β-negative cognitively normal individuals, who underwent (18)F-AV1451 (tau), (11)C-PiB (amyloid-β) and (18)F-FDG (glucose metabolism) positron emission tomography, apolipoprotein E (APOE) genotyping and neuropsychological testing. Voxel-wise contrasts against controls (at P < 0.05 family-wise error corrected) showed that (18)F-AV1451 and (18)F-FDG patterns in patients with posterior cortical atrophy ('visual variant of Alzheimer's disease', n = 7) specifically targeted the clinically affected posterior brain regions, while (11)C-PiB bound diffusely throughout the neocortex. Patients with an amnestic-predominant presentation (n = 5) showed highest (18)F-AV1451 retention in medial temporal and lateral temporoparietal regions. Patients with logopenic variant primary progressive aphasia ('language variant of Alzheimer's disease', n = 5) demonstrated asymmetric left greater than right hemisphere (18)F-AV1451 uptake in three of five patients. Across 30 FreeSurfer-defined regions of interest in 16 Alzheimer's disease patients with all three positron emission tomography scans available, there was a strong negative association between (18)F-AV1451 and (18)F-FDG uptake (Pearson's r = -0.49 ± 0.07, P < 0.001) and less pronounced positive associations between (11)C-PiB and (18)F-FDG (Pearson's r = 0.16 ± 0.09, P < 0.001) and (18)F-AV1451 and (11)C-PiB (Pearson's r = 0.18 ± 0.09, P < 0.001). Voxel-wise linear regressions thresholded at P < 0.05 (uncorrected) showed that, across all patients, younger age was associated with greater (18)F-AV1451 uptake in wide regions of the neocortex, while older age was associated with increased (18)F-AV1451 in the medial temporal lobe. APOE ϵ4 carriers showed greater temporal and parietal (18)F-AV1451 uptake than non-carriers. Finally, worse performance on domain-specific neuropsychological tests was associated with greater (18)F-AV1451 uptake in key regions implicated in memory (medial temporal lobes), visuospatial function (occipital, right temporoparietal cortex) and language (left > right temporoparietal cortex). In conclusion, tau imaging-contrary to amyloid-β imaging-shows a strong regional association with clinical and anatomical heterogeneity in Alzheimer's disease. Although preliminary, these results are consistent with and expand upon findings from post-mortem, animal and cerebrospinal fluid studies, and suggest that the pathological aggregation of tau is closely linked to patterns of neurodegeneration and clinical manifestations of Alzheimer's disease.

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

The application of a mathematical model linking structural and functional connectomes in severe brain injury

Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from injury by restoring normal functional connections via alternate structural pathways that circumvent impaired white matter connections. The so-called network diffusion model, which relates an individual's structural and functional connectomes by assuming that functional activation diffuses along structural pathways, is used here to capture this functional rerouting. We jointly examined functional and structural connectomes extracted from MRIs of 12 healthy and 16 brain-injured subjects. Connectome properties were quantified via graph theoretic measures and network diffusion model parameters. While a few graph metrics showed groupwise differences, they did not correlate with patients' level of consciousness as measured by the Coma Recovery Scale — Revised. There was, however, a strong and significant partial Pearson's correlation (accounting for age and years post-injury) between level of consciousness and network diffusion model propagation time (r = 0.76, p < 0.05, corrected), i.e. the time functional activation spends traversing the structural network. We concluded that functional rerouting via alternate (and less efficient) pathways leads to increases in network diffusion model propagation time. Simulations of injury and recovery in healthy connectomes confirmed these results. This work establishes the feasibility for using the network diffusion model to capture network-level mechanisms in recovery of consciousness after severe brain injury.

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A “functional rerouting” hypothesis in recovery from brain injury is tested.

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The connectome-based network diffusion model measures functional rerouting.

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Recovery in severe brain injury correlates with a network diffusion model parameter.

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Simulation in healthy connectomes independently validates the results in patients.

Meta-connectomics: human brain network and connectivity meta-analyses

Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as 'meta-connectomics'. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI 'lesions' to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Structural connectome disruption at baseline predicts 6-months post-stroke outcome

In this study, models based on quantitative imaging biomarkers of post-stroke structural connectome disruption were used to predict six-month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion-weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six-month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross-validation, bootstrapping and multiple comparisons corrections were implemented to minimize over-fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R(2)  = 0.56) and basic mobility outcomes (R(2)  = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R(2)  = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6-month post-stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease

In network analysis, the so-called "rich club" describes the core areas of the brain that are more densely interconnected among themselves than expected by chance, and has been identified as a fundamental aspect of the human brain connectome. This is the first in-depth diffusion imaging study to investigate the rich club along with other organizational changes in the brain's anatomical network in behavioral frontotemporal dementia (bvFTD), and a matched cohort with early-onset Alzheimer's disease (EOAD). Our study sheds light on how bvFTD and EOAD affect connectivity of white matter fiber pathways in the brain, revealing differences and commonalities in the connectome among the dementias. To analyze the breakdown in connectivity, we studied three groups: 20 bvFTD, 23 EOAD, and 37 healthy elderly controls. All participants were scanned with diffusion-weighted magnetic resonance imaging (MRI), and based on whole-brain probabilistic tractography and cortical parcellations, we analyzed the rich club of the brain's connectivity network. This revealed distinct patterns of disruption in both forms of dementia. In the connectome, we detected less disruption overall in EOAD than in bvFTD [false discovery rate (FDR) critical Pperm  = 5.7 × 10(-3) , 10,000 permutations], with more involvement of richly interconnected areas of the brain (chi-squared P = 1.4 × 10(-4) )-predominantly posterior cognitive alterations. In bvFTD, we found a greater spread of disruption including the rich club (FDR critical Pperm  = 6 × 10(-4) ), but especially more peripheral alterations (chi-squared P = 6.5 × 10(-3) ), particularly in medial frontal areas of the brain, in line with the known behavioral socioemotional deficits seen in these patients.

Amyloid pathology and axonal injury after brain trauma

OBJECTIVE

To image β-amyloid (Aβ) plaque burden in long-term survivors of traumatic brain injury (TBI), test whether traumatic axonal injury and Aβ are correlated, and compare the spatial distribution of Aβ to Alzheimer disease (AD).

METHODS

Patients 11 months to 17 years after moderate-severe TBI underwent (11)C-Pittsburgh compound B ((11)C-PiB)-PET, structural and diffusion MRI, and neuropsychological examination. Healthy aged controls and patients with AD underwent PET and structural MRI. Binding potential (BPND) images of (11)C-PiB, which index Aβ plaque density, were computed using an automatic reference region extraction procedure. Voxelwise and regional differences in BPND were assessed. In TBI, a measure of white matter integrity, fractional anisotropy, was estimated and correlated with (11)C-PiB BPND.

RESULTS

Twenty-eight participants (9 with TBI, 9 controls, 10 with AD) were assessed. Increased (11)C-PiB BPND was found in TBI vs controls in the posterior cingulate cortex and cerebellum. Binding in the posterior cingulate cortex increased with decreasing fractional anisotropy of associated white matter tracts and increased with time since injury. Compared to AD, binding after TBI was lower in neocortical regions but increased in the cerebellum.

CONCLUSIONS

Increased Aβ burden was observed in TBI. The distribution overlaps with, but is distinct from, that of AD. This suggests a mechanistic link between TBI and the development of neuropathologic features of dementia, which may relate to axonal damage produced by the injury.

Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural Network Pathology: Toward Clinical Applications

OBJECTIVES

The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition.

METHODS

We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls).

RESULTS

Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.

CONCLUSIONS

Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.

Neurological Diseases from a Systems Medicine Point of View

The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.

Advanced Neuroimaging Methods Towards Characterization of Early Stages of Alzheimer's Disease

In the past 5 years, imaging network properties in the brain of patients with Alzheimer's disease (AD) has revolutionized our understanding of this disorder. Postmortem data had already suggested that the damage spreads along functional neural networks, but postmortem studies do not provide information on the temporal evolution of the damage in the same patient, essential to determine spreading. These data can be provided by functional and structural neuroimaging, which allow for the visualization over time of the progressive damage inflicted by AD. Functional networks can be mapped by determining the synchrony across brain regions of the blood oxygenation level dependence (BOLD) signal on functional magnetic resonance imaging (MRI) during quiet wakefulness. Other less extensively used techniques are also useful. For instance, amyloid deposition can be imaged and its progression mapped to determine whether it follows brain networks, and, if so, which are affected earliest. Network patterns of neurobiological changes, including tau deposition, may prove critical to our understanding of the neurobiology of AD and therefore open the way for therapeutic interventions.

Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry

Increasing evidence supports transcellular propagation of toxic protein aggregates, or proteopathic seeds, as a mechanism for the initiation and progression of pathology in several neurodegenerative diseases, including Alzheimer's disease and the related tauopathies. The potentially critical role of tau seeds in disease progression strongly supports the need for a sensitive assay that readily detects seeding activity in biological samples. By combining the specificity of fluorescence resonance energy transfer (FRET), the sensitivity of flow cytometry, and the stability of a monoclonal cell line, an ultra-sensitive seeding assay has been engineered and is compatible with seed detection from recombinant or biological samples, including human and mouse brain homogenates. The assay employs monoclonal HEK 293T cells that stably express the aggregation-prone repeat domain (RD) of tau harboring the disease-associated P301S mutation fused to either CFP or YFP, which produce a FRET signal upon protein aggregation. The uptake of proteopathic tau seeds (but not other proteins) into the biosensor cells stimulates aggregation of RD-CFP and RD-YFP, and flow cytometry sensitively and quantitatively monitors this aggregation-induced FRET. The assay detects femtomolar concentrations (monomer equivalent) of recombinant tau seeds, has a dynamic range spanning three orders of magnitude, and is compatible with brain homogenates from tauopathy transgenic mice and human tauopathy subjects. With slight modifications, the assay can also detect seeding activity of other proteopathic seeds, such as α-synuclein, and is also compatible with primary neuronal cultures. The ease, sensitivity, and broad applicability of FRET flow cytometry makes it useful to study a wide range of protein aggregation disorders.

A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer

Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation. Few other linear algebra problems have a more mature set of numerical routines available and many computer vision libraries leverage such tools extensively. However, the ability to call the underlying solver only as a "black box" can often become restrictive. Many 'human in the loop' settings in vision frequently exploit supervision from an expert, to the extent that the user can be considered a subroutine in the overall system. In other cases, there is additional domain knowledge, side or even partial information that one may want to incorporate within the formulation. In general, regularizing a (generalized) eigenvalue problem with such side information remains difficult. Motivated by these needs, this paper presents an optimization scheme to solve generalized eigenvalue problems (GEP) involving a (nonsmooth) regularizer. We start from an alternative formulation of GEP where the feasibility set of the model involves the Stiefel manifold. The core of this paper presents an end to end stochastic optimization scheme for the resultant problem. We show how this general algorithm enables improved statistical analysis of brain imaging data where the regularizer is derived from other 'views' of the disease pathology, involving clinical measurements and other image-derived representations.

Charting Frontotemporal Dementia: From Genes to Networks

Frontotemporal dementia (FTD) is a genetically and clinically heterogeneous syndrome that is characterized by overlapping clinical symptoms involving behavior, personality, language and/or motor functions and degeneration of the frontal and temporal lobes. The term frontotemporal lobar degeneration (FTLD) is used to describe the proteinopathies associated with clinical FTD. Emerging evidence from network-based neuroimaging studies, such as resting state functional MRI and diffusion tensor MRI studies, have implicated specific large-scale brain networks in the pathogenesis of FTD syndromes, suggesting a new paradigm for explaining the distributed and heterogeneous spreading patterns of pathological proteins in FTLD. In this review, we overview recent research on the study of FTD syndromes as connectivity disorders in symptomatic patients as well as genotype-specific changes in asymptomatic FTD-related gene mutation carriers. Characterizing brain network breakdown in these subjects using neuroimaging may help anticipate the diagnosis and perhaps prevent the devastating impact of FTD.

Cascading network failure across the Alzheimer's disease spectrum

Complex biological systems are organized across various spatiotemporal scales with particular scientific disciplines dedicated to the study of each scale (e.g. genetics, molecular biology and cognitive neuroscience). When considering disease pathophysiology, one must contemplate the scale at which the disease process is being observed and how these processes impact other levels of organization. Historically Alzheimer’s disease has been viewed as a disease of abnormally aggregated proteins by pathologists and molecular biologists and a disease of clinical symptoms by neurologists and psychologists. Bridging the divide between these scales has been elusive, but the study of brain networks appears to be a pivotal inroad to accomplish this task. In this study, we were guided by an emerging systems-based conceptualization of Alzheimer’s disease and investigated changes in brain networks across the disease spectrum. The default mode network has distinct subsystems with unique functional-anatomic connectivity, cognitive associations, and responses to Alzheimer’s pathophysiology. These distinctions provide a window into the systems-level pathophysiology of Alzheimer’s disease. Using clinical phenotyping, metadata, and multimodal neuroimaging data from the Alzheimer’s Disease Neuroimaging Initiative, we characterized the pattern of default mode network subsystem connectivity changes across the entire disease spectrum (n = 128). The two main findings of this paper are (i) the posterior default mode network fails before measurable amyloid plaques and appears to initiate a connectivity cascade that continues throughout the disease spectrum; and (ii) high connectivity between the posterior default mode network and hubs of high connectivity (many located in the frontal lobe) is associated with amyloid accumulation. These findings support a system model best characterized by a cascading network failure—analogous to cascading failures seen in power grids triggered by local overloads proliferating to downstream nodes eventually leading to widespread power outages, or systems failures. The failure begins in the posterior default mode network, which then shifts processing burden to other systems containing prominent connectivity hubs. This model predicts a connectivity ‘overload’ that precedes structural and functional declines and recasts the interpretation of high connectivity from that of a positive compensatory phenomenon to that of a load-shifting process transiently serving a compensatory role. It is unknown whether this systems-level pathophysiology is the inciting event driving downstream molecular events related to synaptic activity embedded in these systems. Possible interpretations include that the molecular-level events drive the network failure, a pathological interaction between the network-level and the molecular-level, or other upstream factors are driving both.

Progression of Seed-Induced Aβ Deposition within the Limbic Connectome

An important early event in the pathogenesis of Alzheimer's disease (AD) is the aberrant polymerization and extracellular accumulation of amyloid-β peptide (Aβ). In young transgenic mice expressing the human Aβ-precursor protein (APP), deposits of Aβ can be induced by the inoculation of minute amounts of brain extract containing Aβ aggregates ("Aβ seeds"), indicative of a prion-like seeding phenomenon. Moreover, focal intracerebral injection of Aβ seeds can induce deposits not only in the immediate vicinity of the injection site, but, with time, also in distal regions of the brain. However, it remains uncertain whether the spatial progression of Aβ deposits occurs via nonsystematic diffusion from the injection site to proximal regions or via directed transit along neuroanatomical pathways. To address this question, we analyzed the spatiotemporal emergence of Aβ deposits in two different APP-transgenic mouse models that had been previously inoculated with Aβ seeds into the hippocampal formation. The results revealed a specific, neuroanatomically constrained pattern of induced Aβ deposits in structures corresponding to the limbic connectome, supporting the hypothesis that neuronal pathways act as conduits for the movement of proteopathic agents among brain regions, thereby facilitating the progression of disease.

Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models

Mesial temporal lobe epilepsy (TLE) is characterized by stereotyped origination and spread pattern of epileptogenic activity, which is reflected in stereotyped topographic distribution of neuronal atrophy on magnetic resonance imaging (MRI). Both epileptogenic activity and atrophy spread appear to follow white matter connections. We model the networked spread of activity and atrophy in TLE from first principles via two simple first order network diffusion models. Atrophy distribution is modeled as a simple consequence of the propagation of epileptogenic activity in one model, and as a progressive degenerative process in the other. We show that the network models closely reproduce the regional volumetric gray matter atrophy distribution of two epilepsy cohorts: 29 TLE subjects with medial temporal sclerosis (TLE-MTS), and 50 TLE subjects with normal appearance on MRI (TLE-no). Statistical validation at the group level suggests high correlation with measured atrophy (R = 0.586 for TLE-MTS, R = 0.283 for TLE-no). We conclude that atrophy spread model out-performs the hyperactivity spread model. These results pave the way for future clinical application of the proposed model on individual patients, including estimating future spread of atrophy, identification of seizure onset zones and surgical planning.

Transcellular spreading of huntingtin aggregates in the Drosophila brain

A key feature of many neurodegenerative diseases is the accumulation and subsequent aggregation of misfolded proteins. Recent studies have highlighted the transcellular propagation of protein aggregates in several major neurodegenerative diseases, although the precise mechanisms underlying this spreading and how it relates to disease pathology remain unclear. Here we use a polyglutamine-expanded form of human huntingtin (Htt) with a fluorescent tag to monitor the spreading of aggregates in the Drosophila brain in a model of Huntington's disease. Upon expression of this construct in a defined subset of neurons, we demonstrate that protein aggregates accumulate at synaptic terminals and progressively spread throughout the brain. These aggregates are internalized and accumulate within other neurons. We show that Htt aggregates cause non-cell-autonomous pathology, including loss of vulnerable neurons that can be prevented by inhibiting endocytosis in these neurons. Finally we show that the release of aggregates requires N-ethylmalemide-sensitive fusion protein 1, demonstrating that active release and uptake of Htt aggregates are important elements of spreading and disease progression.

Network structure of brain atrophy in de novo Parkinson's disease

We mapped the distribution of atrophy in Parkinson's disease (PD) using magnetic resonance imaging (MRI) and clinical data from 232 PD patients and 117 controls from the Parkinson's Progression Markers Initiative. Deformation-based morphometry and independent component analysis identified PD-specific atrophy in the midbrain, basal ganglia, basal forebrain, medial temporal lobe, and discrete cortical regions. The degree of atrophy reflected clinical measures of disease severity. The spatial pattern of atrophy demonstrated overlap with intrinsic networks present in healthy brain, as derived from functional MRI. Moreover, the degree of atrophy in each brain region reflected its functional and anatomical proximity to a presumed disease epicenter in the substantia nigra, compatible with a trans-neuronal spread of the disease. These results support a network-spread mechanism in PD. Finally, the atrophy pattern in PD was also seen in healthy aging, where it also correlated with the loss of striatal dopaminergic innervation.

Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease

Alzheimer's disease (AD) can present with distinct clinical variants. Identifying the earliest neurodegenerative changes associated with each variant has implications for early diagnosis, and for understanding the mechanisms that underlie regional vulnerability and disease progression in AD. We performed voxel-based morphometry to detect atrophy patterns in early clinical stages of four AD phenotypes: Posterior cortical atrophy (PCA, "visual variant," n=93), logopenic variant primary progressive aphasia (lvPPA, "language variant," n=74), and memory-predominant AD categorized as early age-of-onset (EOAD, <65 years, n=114) and late age-of-onset (LOAD, >65 years, n=114). Patients with each syndrome were stratified based on: (1) degree of functional impairment, as measured by the clinical dementia rating (CDR) scale, and (2) overall extent of brain atrophy, as measured by a neuroimaging approach that sums the number of brain voxels showing significantly lower gray matter volume than cognitively normal controls (n=80). Even at the earliest clinical stage (CDR=0.5 or bottom quartile of overall atrophy), patients with each syndrome showed both common and variant-specific atrophy. Common atrophy across variants was found in temporoparietal regions that comprise the posterior default mode network (DMN). Early syndrome-specific atrophy mirrored functional brain networks underlying functions that are uniquely affected in each variant: Language network in lvPPA, posterior cingulate cortex-hippocampal circuit in amnestic EOAD and LOAD, and visual networks in PCA. At more advanced stages, atrophy patterns largely converged across AD variants. These findings support a model in which neurodegeneration selectively targets both the DMN and syndrome-specific vulnerable networks at the earliest clinical stages of AD.

Imaging Neurodegeneration: Steps Toward Brain Network-Based Pathophysiology and Its Potential for Multi-modal Imaging Diagnostics

PURPOSE

Multi-modal brain imaging provides different in vivo windows into the human brain and thereby different ways to characterize brain disorders. Particularly, resting-state functional magnetic resonance imaging facilitates the study of macroscopic intrinsic brain networks, which are critical for development and spread of neurodegenerative processes in different neurodegenerative diseases. The aim of the current study is to present and highlight some paradigmatic findings in intrinsic network-based pathophysiology of neurodegenerative diseases and its potential for new network-based multimodal tools in imaging diagnostics.

METHODS

Qualitative review of selected multi-modal imaging studies in neurodegenerative diseases particularly in Alzheimer's disease (AD).

RESULTS

Functional connectivity of intrinsic brain networks is selectively and progressively impaired in AD, with changes likely starting before the onset of symptoms in fronto-parietal key networks such as default mode or attention networks. Patterns of distribution and development of both amyloid-β plaques and atrophy are linked with network connectivity changes, suggesting that start and spread of pathology interacts with network connectivity. Qualitatively similar findings have been observed in other neurodegenerative disorders, suggesting shared mechanisms of network-based pathophysiology across diseases.

CONCLUSION

Spread of neurodegeneration is intimately linked with the functional connectivity of intrinsic brain networks. These pathophysiological insights pave the way for new multi-modal network-based tools to detect and characterize neurodegeneration in individual patients.

The sleep-wake cycle and Alzheimer's disease: what do we know?

Sleep-wake disturbances are a highly prevalent and often disabling feature of Alzheimer's disease (AD). A cardinal feature of AD includes the formation of amyloid plaques, associated with the extracellular accumulation of the amyloid-β (Aβ) peptide. Evidence from animal and human studies suggests that Aβ pathology may disrupt the sleep-wake cycle, in that as Aβ accumulates, more sleep-wake fragmentation develops. Furthermore, recent research in animal and human studies suggests that the sleep-wake cycle itself may influence Alzheimer's disease onset and progression. Chronic sleep deprivation increases amyloid plaque deposition, and sleep extension results in fewer plaques in experimental models. In this review geared towards the practicing clinician, we discuss possible mechanisms underlying the reciprocal relationship between the sleep-wake cycle and AD pathology and behavior, and present current approaches to therapy for sleep disorders in AD.

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.

Altered whole-brain white matter networks in preclinical Alzheimer's disease

Surrogates of whole-brain white matter (WM) networks reconstructed using diffusion tensor imaging (DTI) are novel markers of structural brain connectivity. Global connectivity of networks has been found impaired in clinical Alzheimer's disease (AD) compared to cognitively healthy aging. We hypothesized that network alterations are detectable already in preclinical AD and investigated major global WM network properties. Other structural markers of neurodegeneration typically affected in prodromal AD but seeming largely unimpaired in preclinical AD were also examined. 12 cognitively healthy elderly with preclinical AD as classified by florbetapir-PET (mean age 73.4 ± 4.9) and 31 age-matched controls without cerebral amyloidosis (mean age 73.1 ± 6.7) from the ADNI were included. WM networks were reconstructed from DTI using tractography and graph theory. Indices of network capacity and the established imaging markers of neurodegeneration hippocampal volume, and cerebral glucose utilization as measured by fludeoxyglucose-PET were compared between the two groups. Additionally, we measured surrogates of global WM integrity (fractional anisotropy, mean diffusivity, volume). We found an increase of shortest path length and a decrease of global efficiency in preclinical AD. These results remained largely unchanged when controlling for WM integrity. In contrast, neither markers of neurodegeneration nor WM integrity were altered in preclinical AD subjects. Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity.

White matter tract signatures of impaired social cognition in frontotemporal lobar degeneration

Impairments of social cognition are often leading features in frontotemporal lobar degeneration (FTLD) and likely to reflect large-scale brain network disintegration. However, the neuroanatomical basis of impaired social cognition in FTLD and the role of white matter connections have not been defined. Here we assessed social cognition in a cohort of patients representing two core syndromes of FTLD, behavioural variant frontotemporal dementia (bvFTD; n = 29) and semantic variant primary progressive aphasia (svPPA; n = 15), relative to healthy older individuals (n = 37) using two components of the Awareness of Social Inference Test, canonical emotion identification and sarcasm identification. Diffusion tensor imaging (DTI) was used to derive white matter tract correlates of social cognition performance and compared with the distribution of grey matter atrophy on voxel-based morphometry. The bvFTD and svPPA groups showed comparably severe deficits for identification of canonical emotions and sarcasm, and these deficits were correlated with distributed and overlapping white matter tract alterations particularly affecting frontotemporal connections in the right cerebral hemisphere. The most robust DTI associations were identified in white matter tracts linking cognitive and evaluative processing with emotional responses: anterior thalamic radiation, fornix (emotion identification) and uncinate fasciculus (sarcasm identification). DTI associations of impaired social cognition were more consistent than corresponding grey matter associations. These findings delineate a brain network substrate for the social impairment that characterises FTLD syndromes. The findings further suggest that DTI can generate sensitive and functionally relevant indexes of white matter damage in FTLD, with potential to transcend conventional syndrome boundaries.

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Social cognition deficits define frontotemporal dementias but are poorly understood.

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We studied brain network correlates of sarcasm processing in these dementias with DTI.

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Sarcasm deficits were particularly linked to right frontotemporal tract changes.

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DTI generates functionally relevant metrics of white matter damage in these dementias.

Clinical effect of white matter network disruption related to amyloid and small vessel disease

BACKGROUND

We tested our hypothesis that the white matter network might mediate the effect of amyloid and small vessel disease (SVD) on cortical thickness and/or cognition.

METHODS

We prospectively recruited 232 patients with cognitive impairment. Amyloid was assessed using Pittsburgh compound B-PET. SVD was quantified as white matter hyperintensity volume and lacune number. The regional white matter network connectivity was measured as regional nodal efficiency by applying graph theoretical analysis to diffusion tensor imaging data. We measured cortical thickness and performed neuropsychological tests.

RESULTS

SVD burden was associated with decreased nodal efficiency in the bilateral frontal, lateral temporal, lateral parietal, and occipital regions. Path analyses showed that the frontal nodal efficiency mediated the effect of SVD on the frontal atrophy and frontal-executive dysfunction. The temporoparietal nodal efficiency mediated the effect of SVD on the temporoparietal atrophy and memory dysfunction. However, Pittsburgh compound B retention ratio affected cortical atrophy and cognitive impairment without being mediated by nodal efficiency.

CONCLUSIONS

We suggest that a disrupted white matter network mediates the effect of SVD, but not amyloid, on specific patterns of cortical atrophy and/or cognitive impairment. Therefore, our findings provide insight to better understand how amyloid and SVD burden can give rise to brain atrophy or cognitive impairment in specific patterns.