A network diffusion model of disease progression in dementia

The thalamus as a putative biomarker in neurodegenerative disorders

OBJECTIVE

This review provides a brief account of the clinically relevant functional neuroanatomy of the thalamus, before considering the utility of various modalities utilized to image the thalamus and technical challenges therein, and going on to provide an overview of studies utilizing structural imaging techniques to map thalamic morphology in the spectrum of neurodegenerative disorders.

METHODS

A systematic search was conducted for peer-reviewed studies involving structural neuroimaging modalities investigating the morphology (shape and/or size) of the thalamus in the spectrum of neurodegenerative disorders.

RESULTS

While the precise role of the thalamus in the healthy brain remains unclear, there is a large body of knowledge accumulating which defines more precisely its functional connectivity within the connectome, and a burgeoning literature implicating its involvement in neurodegenerative disorders. It is proposed that correlation of clinical features with thalamic morphology (as a component of a quantifiable subcortical connectome) will provide a better understanding of neuropsychiatric dysfunction in various neurodegenerative disorders, potentially yielding clinically useful endophenotypes and disease biomarkers.

CONCLUSION

Thalamic biomarkers in the neurodegenerative disorders have great potential to provide clinically meaningful knowledge regarding not only disease onset and progression but may yield targets of and perhaps a way of gauging response to future disease-modifying modalities.

Propagation of pathology through brain networks in neurodegenerative diseases: from molecules to clinical phenotypes

The cellular mechanisms underlying the stereotypical progression of pathology in neurodegenerative diseases are incompletely understood, but increasing evidence indicates that misfolded protein aggregates can spread by a self-perpetuating neuron-to-neuron transmission. Novel neuroimaging techniques can help elucidating how these disorders spread across brain networks. Recent knowledge from structural and functional connectivity studies suggests that the relation between neurodegenerative diseases and distinct brain networks is likely to be a strict consequence of diffuse network dynamics. Diffusion tensor magnetic resonance imaging also showed that measurement of white matter tract involvement can be a valid surrogate to assess the in vivo spreading of pathological proteins in these conditions. This review will introduce briefly the main molecular and pathological substrates of the most frequent neurodegenerative diseases and provide a comprehensive overview of neuroimaging findings that support the "network-based neurodegeneration" hypothesis in these disorders. Characterizing network breakdown in neurodegenerative diseases will help anticipate and perhaps prevent the devastating impact of these conditions.

Templated misfolding of Tau by prion-like seeding along neuronal connections impairs neuronal network function and associated behavioral outcomes in Tau transgenic mice

Prion-like seeding and propagation of Tau-pathology have been demonstrated experimentally and may underlie the stereotyped progression of neurodegenerative Tauopathies. However, the involvement of templated misfolding of Tau in neuronal network dysfunction and behavioral outcomes remains to be explored in detail. Here we analyzed the repercussions of prion-like spreading of Tau-pathology via neuronal connections on neuronal network function in TauP301S transgenic mice. Spontaneous and GABA(A)R-antagonist-induced neuronal network activity were affected following templated Tau-misfolding using synthetic preformed Tau fibrils in cultured primary neurons. Electrophysiological analysis in organotypic hippocampal slices of Tau transgenic mice demonstrated impaired synaptic transmission and impaired long-term potentiation following Tau-seed induced Tau-aggregation. Intracerebral injection of Tau-seeds in TauP301S mice, caused prion-like spreading of Tau-pathology through functionally connected neuroanatomical pathways. Electrophysiological analysis revealed impaired synaptic plasticity in hippocampal CA1 region 6 months after Tau-seeding in entorhinal cortex (EC). Furthermore, templated Tau aggregation impaired cognitive function, measured in the object recognition test 6 months post-seeding. In contrast, Tau-seeding in basal ganglia and subsequent spreading through functionally connected neuronal networks involved in motor control, resulted in motoric deficits reflected in clasping and impaired inverted grid hanging, not significantly affected following Tau-seeding in EC. Immunostaining, biochemical and electron microscopic analysis in the different models suggested early pathological forms of Tau, including Tau-oligomers, rather than fully mature neurofibrillary tangles (NFTs) as culprits of neuronal dysfunction. We here demonstrate for the first time using in vitro, ex vivo and in vivo models, that prion-like spreading of Tau-misfolding by Tau seeds, along unique neuronal connections, causes neuronal network dysfunction and associated behavioral dysfunction. Our data highlight the potential relevance of this mechanism in the symptomatic progression in Tauopathies. We furthermore demonstrate that the initial site of Tau-seeding thereby determines the behavioral outcome, potentially underlying the observed heterogeneity in (familial) Tauopathies, including in TauP301 mutants.

A spectral graph regression model for learning brain connectivity of Alzheimer's disease

Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer's disease (AD) measured by amyloid-β deposits. The proposed GRM regards ¹¹C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1-regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.

Towards a new understanding of NCL pathogenesis

The Neuronal Ceroid Lipofuscinoses (NCLs, Batten disease) are a group of inherited neurodegenerative disorders that have been traditionally grouped together on the basis of certain shared clinical and pathological features. However, as the number of genes that appear to cause new forms of NCL continues to grow, it is timely to reassess our understanding of the pathogenesis of these disorders and what groups them together. The various NCL subtypes do indeed share features of a build-up of autofluorescent storage material, progressive neuron loss and activation of the innate immune system. The characterisation of animal models has highlighted the selective nature of neuron loss and its intimate relationship with glial activation, rather than the generalised build-up of storage material. More recent data provide evidence for the pathway-dependent nature of pathology, the contribution of glial dysfunction, and the involvement of new brain regions previously thought to be unaffected, and it is becoming apparent that pathology extends beyond the brain. These data have important implications, not just for therapy, but also for our understanding of these disorders. However, looking beneath these broadly similar pathological themes evidence emerges for marked differences in the nature and extent of these events in different forms of NCL. Indeed, given the widely different nature of the mutated gene products it is perhaps more surprising that these disorders resemble each other as much as they do. Such data raise the question whether we should rethink the collective grouping of these gene deficiencies together, or whether it would be better to consider them as separate entities. This article is part of a Special Issue entitled: Current Research on the Neuronal Ceroid Lipofuscinoses (Batten Disease).

On the central role of brain connectivity in neurodegenerative disease progression

Increased brain connectivity, in all its variants, is often considered an evolutionary advantage by mediating complex sensorimotor function and higher cognitive faculties. Interaction among components at all spatial scales, including genes, proteins, neurons, local neuronal circuits and macroscopic brain regions, are indispensable for such vital functions. However, a growing body of evidence suggests that, from the microscopic to the macroscopic levels, such connections might also be a conduit for in intra-brain disease spreading. For instance, cell-to-cell misfolded proteins (MP) transmission and neuronal toxicity are prominent connectivity-mediated factors in aging and neurodegeneration. This article offers an overview of connectivity dysfunctions associated with neurodegeneration, with a specific focus on how these may be central to both normal aging and the neuropathologic degenerative progression.

In vivo imaging of tau pathology using multi-parametric quantitative MRI

As the number of people diagnosed with Alzheimer's disease (AD) reaches epidemic proportions, there is an urgent need to develop effective treatment strategies to tackle the social and economic costs of this fatal condition. Dozens of candidate therapeutics are currently being tested in clinical trials, and compounds targeting the aberrant accumulation of tau proteins into neurofibrillary tangles (NFTs) are the focus of substantial current interest. Reliable, translatable biomarkers sensitive to both tau pathology and its modulation by treatment along with animal models that faithfully reflect aspects of the human disease are urgently required. Magnetic resonance imaging (MRI) is well established as a valuable tool for monitoring the structural brain changes that accompany AD progression. However the descent into dementia is not defined by macroscopic brain matter loss alone: non-invasive imaging measurements sensitive to protein accumulation, white matter integrity and cerebral haemodynamics probe distinct aspects of AD pathophysiology and may serve as superior biomarkers for assessing drug efficacy. Here we employ a multi-parametric array of five translatable MRI techniques to characterise the in vivo pathophysiological phenotype of the rTg4510 mouse model of tauopathy (structural imaging, diffusion tensor imaging (DTI), arterial spin labelling (ASL), chemical exchange saturation transfer (CEST) and glucose CEST). Tau-induced pathological changes included grey matter atrophy, increased radial diffusivity in the white matter, decreased amide proton transfer and hyperperfusion. We demonstrate that the above markers unambiguously discriminate between the transgenic group and age-matched controls and provide a comprehensive profile of the multifaceted neuropathological processes underlying the rTg4510 model. Furthermore, we show that ASL and DTI techniques offer heightened sensitivity to processes believed to precede detectable structural changes and, as such, provides a platform for the study of disease mechanisms and therapeutic intervention.

Tau-er of Power

Tau protein helps nerve cells in the brain maintain their function and structure. When tau turns toxic, replicates, and spreads, neurons misfire and die. If neuroscientists can pinpoint the reasons for toxicity, identify what our author calls “a staggering number of possible modified tau states,” and find a way to block tau’s movement from cell to cell, then progress can be made in fighting any number of neurological disorders linked to this protein, including frontotemporal dementia, chronic traumatic encephalopathy (CTE), and Alzheimer’s disease.

Connectomics: a new paradigm for understanding brain disease

In recent years, pathophysiological models of brain disorders have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disturbances of interconnected neural systems. This shift has paralleled rapid advances in connectomics, a field concerned with comprehensively mapping the neural elements and inter-connections that constitute the brain. Magnetic resonance imaging (MRI) has played a central role in these efforts, as it allows relatively cost-effective in vivo assessment of the macro-scale architecture of brain network connectivity. In this paper, we provide a brief introduction to some of the basic concepts in the field and review how recent developments in imaging connectomics are yielding new insights into brain disease, with a particular focus on Alzheimer's disease and schizophrenia. Specifically, we consider how research into circuit-level, connectome-wide and topological changes is stimulating the development of new aetiopathological theories and biomarkers with potential for clinical translation. The findings highlight the advantage of conceptualizing brain disease as a result of disturbances in an interconnected complex system, rather than discrete pathology in isolated sub-sets of brain regions.

The connectomics of brain disorders

Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

Directed network motifs in Alzheimer's disease and mild cognitive impairment

Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer’s disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer’s disease.

Simulating disease propagation across white matter connectome reveals anatomical substrate for neuropathology staging in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, characterized by progressive loss of motor function. While the pathogenesis of ALS remains largely unknown, recent histological examinations of Brettschneider and colleagues have proposed four time-sequential stages of neuropathology in ALS based on levels of phosphorylated 43kDa TAR DNA-binding protein (pTDP-43) aggregation. What governs dissemination of these aggregates between segregated regions of the brain is unknown. Here, we cross-reference stages of pTDP-43 pathology with in vivo diffusion weighted imaging data of 215 adult healthy control subjects, and reveal that regions involved in pTDP-43 pathology form a strongly interconnected component of the brain network (p=0.04) likely serving as an anatomical infrastructure facilitating pTDP-43 spread. Furthermore, brain regions of subsequent stages of neuropathology are shown to be more closely interconnected than regions of more distant stages (p=0.002). Computational simulation of disease spread from first-stage motor regions across the connections of the brain network reveals a pattern of pTDP-43 aggregation that reflects the stages of sequential involvement in neuropathology (p=0.02), a pattern in favor of the hypothesis of pTDP-43 pathology to spread across the brain along axonal pathways. Our findings thus provide computational evidence of disease spread in ALS to be directed and constrained by the topology of the anatomical brain network.

Cognitive network neuroscience

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

Spreading of pathology in neurodegenerative diseases: a focus on human studies

The progression of many neurodegenerative diseases is thought to be driven by the template-directed misfolding, seeded aggregation and cell-cell transmission of characteristic disease-related proteins, leading to the sequential dissemination of pathological protein aggregates. Recent evidence strongly suggests that the anatomical connections made by neurons - in addition to the intrinsic characteristics of neurons, such as morphology and gene expression profile - determine whether they are vulnerable to degeneration in these disorders. Notably, this common pathogenic principle opens up opportunities for pursuing novel targets for therapeutic interventions for these neurodegenerative disorders. We review recent evidence that supports the notion of neuron-neuron protein propagation, with a focus on neuropathological and positron emission tomography imaging studies in humans.

Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease

Alzheimer's disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brain's connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The model's rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimer's disease assessment.

[On the question of the organization of brain function: cortical associations, «disconnection» syndrome and higher brain functions]

The review considers the structural/functional brain organization, the disturbance of which is accompanied by the development of cognitive and behavioral disorders. The significance of the disruption of parallel circuits connecting frontal lobes with subcortical structures (the basal ganglia, thalamus, cerebellum) is highlighted. This disruption is clinically described as "disconnection" syndrome. The associations between the basal ganglia and the cortex of the large cerebral hemispheres responsible for motor, cognitive and emotional/behavioral functions do not restricted to these spheres and is characteristic not only of frontal brain areas. There are circuits connecting other brain compartments and the basal ganglia that provide perception, and are involved in decision making on the basis of input information of different modalities.The improvement of understanding of the pathophysiology and neurochemistry of these structures opens new possibilities for selective action on some or other circuit to achieve the best therapeutic result.

A common brain network links development, aging, and vulnerability to disease

Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

Modeling the relationship among gray matter atrophy, abnormalities in connecting white matter, and cognitive performance in early multiple sclerosis

BACKGROUND AND PURPOSE

Quantitative assessment of clinical and pathologic consequences of white matter abnormalities in multiple sclerosis is critical in understanding the pathways of disease. This study aimed to test whether gray matter atrophy was related to abnormalities in connecting white matter and to identify patterns of imaging biomarker abnormalities that were related to patient processing speed.

MATERIALS AND METHODS

Image data and Symbol Digit Modalities Test scores were collected from a cohort of patients with early multiple sclerosis. The Network Modification Tool was used to estimate connectivity irregularities by projecting white matter abnormalities onto connecting gray matter regions. Partial least-squares regression quantified the relationship between imaging biomarkers and processing speed as measured by the Symbol Digit Modalities Test.

RESULTS

Atrophy in deep gray matter structures of the thalami and putamen had moderate and significant correlations with abnormalities in connecting white matter (r = 0.39-0.41, P < .05 corrected). The 2 models of processing speed, 1 for each of the WM imaging biomarkers, had goodness-of-fit (R(2)) values of 0.42 and 0.30. A measure of the impact of white matter lesions on the connectivity of occipital and parietal areas had significant nonzero regression coefficients.

CONCLUSIONS

We concluded that deep gray matter regions may be susceptible to inflammation and/or demyelination in white matter, possibly having a higher sensitivity to remote degeneration, and that lesions affecting visual processing pathways were related to processing speed. The Network Modification Tool may be used to quantify the impact of early white matter abnormalities on both connecting gray matter structures and processing speed.

Epidemic spreading model to characterize misfolded proteins propagation in aging and associated neurodegenerative disorders

Misfolded proteins (MP) are a key component in aging and associated neurodegenerative disorders. For example, misfolded Amyloid-ß (Aß) and tau proteins are two neuropathogenic hallmarks of Alzheimer's disease. Mechanisms underlying intra-brain MP propagation/deposition remain essentially uncharacterized. Here, is introduced an epidemic spreading model (ESM) for MP dynamics that considers propagation-like interactions between MP agents and the brain's clearance response across the structural connectome. The ESM reproduces advanced Aß deposition patterns in the human brain (explaining 46∼56% of the variance in regional Aß loads, in 733 subjects from the ADNI database). Furthermore, this model strongly supports a) the leading role of Aß clearance deficiency and early Aß onset age during Alzheimer's disease progression, b) that effective anatomical distance from Aß outbreak region explains regional Aß arrival time and Aß deposition likelihood, c) the multi-factorial impact of APOE e4 genotype, gender and educational level on lifetime intra-brain Aß propagation, and d) the modulatory impact of Aß propagation history on tau proteins concentrations, supporting the hypothesis of an interrelated pathway between Aß pathophysiology and tauopathy. To our knowledge, the ESM is the first computational model highlighting the direct link between structural brain networks, production/clearance of pathogenic proteins and associated intercellular transfer mechanisms, individual genetic/demographic properties and clinical states in health and disease. In sum, the proposed ESM constitutes a promising framework to clarify intra-brain region to region transference mechanisms associated with aging and neurodegenerative disorders.

Misfolded proteins (MP) mechanisms are a characteristic pathogenic feature of most prevalent human neurodegenerative diseases, such as Alzheimer's disease (AD). Characterizing the mechanisms underlying intra-brain MP propagation and deposition still constitutes a major challenge. Here, we hypothesize that these complex mechanisms can be accurately described by epidemic spreading-like interactions between infectious-like agents (MP) and the brain's MP clearance response, which are constrained by the brain's connectional architecture. Consequently, we have developed a stochastic epidemic spreading model (ESM) of MP propagation/deposition that allows for reconstructing individual lifetime histories of intra-brain MP propagation, and the subsequent analysis of factors that promote propagation/deposition (e.g., MP production and clearance). Using 733 individual PET Amyloid-ß (Aß) datasets, we show that ESM explains advanced Aß deposition patterns in healthy and diseased (AD) brains. More importantly, it offers new avenues for our understanding of the mechanisms underlying MP mediated disorders. For instance, the results strongly support the growing body of evidence suggesting the leading role of a reduced Aβ clearance on AD progression and the modulatory impact of Aß mechanisms on tau proteins concentrations, which could imply a turning point for associated therapeutic mitigation strategies.

Quantifying uncertainty in brain network measures using Bayesian connectomics

The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate. In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics.

Advances in MRI biomarkers for the diagnosis of Alzheimer's disease

With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.

Proteopathic tau seeding predicts tauopathy in vivo

Transcellular propagation of protein aggregates, or proteopathic seeds, may drive the progression of neurodegenerative diseases in a prion-like manner. In tauopathies such as Alzheimer's disease, this model predicts that tau seeds propagate pathology through the brain via cell-cell transfer in neural networks. The critical role of tau seeding activity is untested, however. It is unknown whether seeding anticipates and correlates with subsequent development of pathology as predicted for a causal agent. One major limitation has been the lack of a robust assay to measure proteopathic seeding activity in biological specimens. We engineered an ultrasensitive, specific, and facile FRET-based flow cytometry biosensor assay based on expression of tau or synuclein fusions to CFP and YFP, and confirmed its sensitivity and specificity to tau (∼ 300 fM) and synuclein (∼ 300 pM) fibrils. This assay readily discriminates Alzheimer's disease vs. Huntington's disease and aged control brains. We then carried out a detailed time-course study in P301S tauopathy mice, comparing seeding activity versus histological markers of tau pathology, including MC1, AT8, PG5, and Thioflavin S. We detected robust seeding activity at 1.5 mo, >1 mo before the earliest histopathological stain. Proteopathic tau seeding is thus an early and robust marker of tauopathy, suggesting a proximal role for tau seeds in neurodegeneration.

Frontal white matter tracts sustaining speech production in primary progressive aphasia

In primary progressive aphasia (PPA), speech and language difficulties are caused by neurodegeneration of specific brain networks. In the nonfluent/agrammatic variant (nfvPPA), motor speech and grammatical deficits are associated with atrophy in a left fronto-insular-striatal network previously implicated in speech production. In vivo dissection of the crossing white matter (WM) tracts within this "speech production network" is complex and has rarely been performed in health or in PPA. We hypothesized that damage to these tracts would be specific to nfvPPA and would correlate with differential aspects of the patients' fluency abilities. We prospectively studied 25 PPA and 21 healthy individuals who underwent extensive cognitive testing and 3 T MRI. Using residual bootstrap Q-ball probabilistic tractography on high angular resolution diffusion-weighted imaging (HARDI), we reconstructed pathways connecting posterior inferior frontal, inferior premotor, insula, supplementary motor area (SMA) complex, striatum, and standard ventral and dorsal language pathways. We extracted tract-specific diffusion tensor imaging (DTI) metrics to assess changes across PPA variants and perform brain-behavioral correlations. Significant WM changes in the left intrafrontal and frontostriatal pathways were found in nfvPPA, but not in the semantic or logopenic variants. Correlations between tract-specific DTI metrics with cognitive scores confirmed the specific involvement of this anterior-dorsal network in fluency and suggested a preferential role of a posterior premotor-SMA pathway in motor speech. This study shows that left WM pathways connecting the speech production network are selectively damaged in nfvPPA and suggests that different tracts within this system are involved in subcomponents of fluency. These findings emphasize the emerging role of diffusion imaging in the differential diagnosis of neurodegenerative diseases.

Structural connectivity based whole brain modelling in epilepsy

Epilepsy is a neurological condition characterised by the recurrence of seizures. During seizures multiple brain areas can behave abnormally. Rather than considering each abnormal area in isolation, one can consider them as an interconnected functional 'network'. Recently, there has been a shift in emphasis to consider epilepsy as a disorder involving more widespread functional brain networks than perhaps was previously thought. The basis for these functional networks is proposed to be the static structural brain network established through the connectivity of the white matter. Additionally, it has also been argued that time varying aspects of epilepsy are of crucial importance and as such computational models of these dynamical properties have recently advanced. We describe how dynamic computer models can be combined with static human in vivo connectivity obtained through diffusion weighted magnetic resonance imaging. We predict that in future the use of these two methods in concert will lead to predictions for optimal surgery and brain stimulation sites for epilepsy and other neurological disorders.

The hubs of the human connectome are generally implicated in the anatomy of brain disorders

Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.

Profiles of white matter tract pathology in frontotemporal dementia

Despite considerable interest in improving clinical and neurobiological characterisation of frontotemporal dementia and in defining the role of brain network disintegration in its pathogenesis, information about white matter pathway alterations in frontotemporal dementia remains limited. Here we investigated white matter tract damage using an unbiased, template-based diffusion tensor imaging (DTI) protocol in a cohort of 27 patients with the behavioral variant of frontotemporal dementia (bvFTD) representing both major genetic and sporadic forms, in relation both to healthy individuals and to patients with Alzheimer's disease. Widespread white matter tract pathology was identified in the bvFTD group compared with both healthy controls and Alzheimer's disease group, with prominent involvement of uncinate fasciculus, cingulum bundle and corpus callosum. Relatively discrete and distinctive white matter profiles were associated with genetic subgroups of bvFTD associated with MAPT and C9ORF72 mutations. Comparing diffusivity metrics, optimal overall separation of the bvFTD group from the healthy control group was signalled using radial diffusivity, whereas optimal overall separation of the bvFTD group from the Alzheimer's disease group was signalled using fractional anisotropy. Comparing white matter changes with regional grey matter atrophy (delineated using voxel based morphometry) in the bvFTD cohort revealed co-localisation between modalities particularly in the anterior temporal lobe, however white matter changes extended widely beyond the zones of grey matter atrophy. Our findings demonstrate a distributed signature of white matter alterations that is likely to be core to the pathophysiology of bvFTD and further suggest that this signature is modulated by underlying molecular pathologies.

Network hubs in the human brain

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.

Transneuronal propagation of mutant huntingtin contributes to non-cell autonomous pathology in neurons

In Huntington's disease (HD), whether transneuronal spreading of mutant huntingtin (mHTT) occurs and its contribution to non-cell autonomous damage in brain networks is largely unknown. We found mHTT spreading in three different neural network models: human neurons integrated in the neural network of organotypic brain slices of HD mouse model, an ex vivo corticostriatal slice model and the corticostriatal pathway in vivo. Transneuronal propagation of mHTT was blocked by two different botulinum neurotoxins, each known for specifically inactivating a single critical component of the synaptic vesicle fusion machinery. Moreover, healthy human neurons in HD mouse model brain slices displayed non-cell autonomous changes in morphological integrity that were more pronounced when these neurons bore mHTT aggregates. Altogether, our findings suggest that transneuronal propagation of mHTT might be an important and underestimated contributor to the pathophysiology of HD.

Reduced glucose uptake and Aβ in brain regions with hyperintensities in connected white matter

Interstitial concentration of amyloid beta (Aß) is positively related to synaptic activity in animal experiments. In humans, Aß deposition in Alzheimer's disease overlaps with cortical regions highly active earlier in life. White matter lesions (WML) disrupt connections between gray matter (GM) regions which in turn changes their activation patterns. Here, we tested if WML are related to Aß accumulation (measured with PiB-PET) and glucose uptake (measured with FDG-PET) in connected GM. WML masks from 72 cognitively normal (age 61.7 ± 9.6 years, 71% women) individuals were obtained from T2-FLAIR. MRI and PET images were normalized into common space, segmented and parcellated into gray matter (GM) regions. The effects of WML on connected GM regions were assessed using the Change in Connectivity (ChaCo) score. Defined for each GM region, ChaCo is the percentage of WM tracts connecting to that region that pass through the WML mask. The regional relationship between ChaCo, glucose uptake and Aß was explored via linear regression. Subcortical regions of the bilateral caudate, putamen, calcarine, insula, thalamus and anterior cingulum had WM connections with the most lesions, followed by frontal, occipital, temporal, parietal and cerebellar regions. Regional analysis revealed that GM with more lesions in connecting WM and thus impaired connectivity had lower FDG-PET (r = 0.20, p<0.05 corrected) and lower PiB uptake (r = 0.28, p<0.05 corrected). Regional regression also revealed that both ChaCo (β = 0.045) and FDG-PET (β = 0.089) were significant predictors of PiB. In conclusion, brain regions with more lesions in connecting WM had lower glucose metabolism and lower Aß deposition.

Imaging markers of structural and functional brain changes that precede cognitive symptoms in risk for Alzheimer's disease

Neuroimaging has rapidly advanced investigations into dysfunction both within and emanating from the hippocampus in early Alzheimer's disease . Focusing on prodromal subjects, we will discuss structural changes to hippocampal subregions, alterations to functional activity both within the hippocampus and elsewhere in the cortex, as well as changes to structural white matter connectivity and changes to functionally correlated patterns during memory performance. We present ample evidence that asymptomatic subjects demonstrate substantial identifiable brain changes before the onset of cognitive decline, but suggest there is significant work yet to be accomplished before applying these findings to individual patients.

Prion-like properties of Tau protein: the importance of extracellular Tau as a therapeutic target

Work over the past 4 years indicates that multiple proteins associated with neurodegenerative diseases, especially Tau and α-synuclein, can propagate aggregates between cells in a prion-like manner. This means that once an aggregate is formed it can escape the cell of origin, contact a connected cell, enter the cell, and induce further aggregation via templated conformational change. The prion model predicts a key role for extracellular protein aggregates in mediating progression of disease. This suggests new therapeutic approaches based on blocking neuronal uptake of protein aggregates and promoting their clearance. This will likely include therapeutic antibodies or small molecules, both of which can be developed and optimized in vitro prior to preclinical studies.

Distinct tau prion strains propagate in cells and mice and define different tauopathies

Prion-like propagation of tau aggregation might underlie the stereotyped progression of neurodegenerative tauopathies. True prions stably maintain unique conformations ("strains") in vivo that link structure to patterns of pathology. We now find that tau meets this criterion. Stably expressed tau repeat domain indefinitely propagates distinct amyloid conformations in a clonal fashion in culture. Reintroduction of tau from these lines into naive cells reestablishes identical clones. We produced two strains in vitro that induce distinct pathologies in vivo as determined by successive inoculations into three generations of transgenic mice. Immunopurified tau from these mice recreates the original strains in culture. We used the cell system to isolate tau strains from 29 patients with 5 different tauopathies, finding that different diseases are associated with different sets of strains. Tau thus demonstrates essential characteristics of a prion. This might explain the phenotypic diversity of tauopathies and could enable more effective diagnosis and therapy.

The Network Modification (NeMo) Tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity

Accurate prediction of brain dysfunction caused by disease or injury requires the quantification of resultant neural connectivity changes compared with the normal state. There are many methods with which to assess anatomical changes in structural or diffusion magnetic resonance imaging, but most overlook the topology of white matter (WM) connections that make up the healthy brain network. Here, a new neuroimaging software pipeline called the Network Modification (NeMo) Tool is presented that associates alterations in WM integrity with expected changes in neural connectivity between gray matter regions. The NeMo Tool uses a large reference set of healthy tractograms to assess implied network changes arising from a particular pattern of WM alteration on a region- and network-wise level. In this way, WM integrity changes can be extrapolated to the cortices and deep brain nuclei, enabling assessment of functional and cognitive alterations. Unlike current techniques that assess network dysfunction, the NeMo tool does not require tractography in pathological brains for which the algorithms may be unreliable or diffusion data are unavailable. The versatility of the NeMo Tool is demonstrated by applying it to data from patients with Alzheimer's disease, fronto-temporal dementia, normal pressure hydrocephalus, and mild traumatic brain injury. This tool fills a gap in the quantitative neuroimaging field by enabling an investigation of morphological and functional implications of changes in structural WM integrity.

Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer's disease

There is striking overlap between the spatial distribution of amyloid-β pathology in patients with Alzheimer's disease and the spatial distribution of high intrinsic functional connectivity in healthy persons. This overlap suggests a mechanistic link between amyloid-β and intrinsic connectivity, and indeed there is evidence in patients for the detrimental effects of amyloid-β plaque accumulation on intrinsic connectivity in areas of high connectivity in heteromodal hubs, and particularly in the default mode network. However, the observed spatial extent of amyloid-β exceeds these tightly circumscribed areas, suggesting that previous studies may have underestimated the negative impact of amyloid-β on intrinsic connectivity. We hypothesized that the known positive baseline correlation between patterns of amyloid-β and intrinsic connectivity may mask the larger extent of the negative effects of amyloid-β on connectivity. Crucially, a test of this hypothesis requires the within-patient comparison of intrinsic connectivity and amyloid-β distributions. Here we compared spatial patterns of amyloid-β-plaques (measured by Pittsburgh compound B positron emission tomography) and intrinsic functional connectivity (measured by resting-state functional magnetic resonance imaging) in patients with prodromal Alzheimer's disease via spatial correlations in intrinsic networks covering fronto-parietal heteromodal cortices. At the global network level, we found that amyloid-β and intrinsic connectivity patterns were positively correlated in the default mode and several fronto-parietal attention networks, confirming that amyloid-β aggregates in areas of high intrinsic connectivity on a within-network basis. Further, we saw an internetwork gradient of the magnitude of correlation that depended on network plaque-load. After accounting for this globally positive correlation, local amyloid-β-plaque concentration in regions of high connectivity co-varied negatively with intrinsic connectivity, indicating that amyloid-β pathology adversely reduces connectivity anywhere in an affected network as a function of local amyloid-β-plaque concentration. The local negative association between amyloid-β and intrinsic connectivity was much more pronounced than conventional group comparisons of intrinsic connectivity would suggest. Our findings indicate that the negative impact of amyloid-β on intrinsic connectivity in heteromodal networks is underestimated by conventional analyses. Moreover, our results provide first within-patient evidence for correspondent patterns of amyloid-β and intrinsic connectivity, with the distribution of amyloid-β pathology following functional connectivity gradients within and across intrinsic networks.

Neural stem cells genetically-modified to express neprilysin reduce pathology in Alzheimer transgenic models

INTRODUCTION

Short-term neural stem cell (NSC) transplantation improves cognition in Alzheimer's disease (AD) transgenic mice by enhancing endogenous synaptic connectivity. However, this approach has no effect on the underlying beta-amyloid (Aβ) and neurofibrillary tangle pathology. Long term efficacy of cell based approaches may therefore require combinatorial approaches.

METHODS

To begin to examine this question we genetically-modified NSCs to stably express and secrete the Aβ-degrading enzyme, neprilysin (sNEP). Next, we studied the effects of sNEP expression in vitro by quantifying Aβ-degrading activity, NSC multipotency markers, and Aβ-induced toxicity. To determine whether sNEP-expressing NSCs can also modulate AD-pathogenesis in vivo, control-modified and sNEP-NSCs were transplanted unilaterally into the hippocampus of two independent and well characterized transgenic models of AD: 3xTg-AD and Thy1-APP mice. After three months, stem cell engraftment, neprilysin expression, and AD pathology were examined.

RESULTS

Our findings reveal that stem cell-mediated delivery of NEP provides marked and significant reductions in Aβ pathology and increases synaptic density in both 3xTg-AD and Thy1-APP transgenic mice. Remarkably, Aβ plaque loads are reduced not only in the hippocampus and subiculum adjacent to engrafted NSCs, but also within the amygdala and medial septum, areas that receive afferent projections from the engrafted region.

CONCLUSIONS

Taken together, our data suggest that genetically-modified NSCs could provide a powerful combinatorial approach to not only enhance synaptic plasticity but to also target and modify underlying Alzheimer's disease pathology.

Distribution of tract deficits in schizophrenia

BACKGROUND

Gray and white matter brain changes have been found in schizophrenia but the anatomical organizing process underlying these changes remains unknown. We aimed to identify gray and white matter volumetric changes in a group of patients with schizophrenia and to quantify the distribution of white matter tract changes using a novel approach which applied three complementary analyses to diffusion imaging data.

METHODS

21 patients with schizophrenia and 21 matched control subjects underwent brain magnetic resonance imaging. Gray and white matter volume differences were investigated using Voxel-based Morphometry (VBM). White matter diffusion changes were located using Tract Based Spatial Statistics (TBSS) and quantified within a standard atlas. Tracts where significant regional differences were located were examined using fiber tractography.

RESULTS

No significant differences in gray or white matter volumetry were found between the two groups. Using TBSS the schizophrenia group showed significantly lower fractional anisotropy (FA) compared to the controls in regions (false discovery rate <0.05) including the genu, body and splenium of the corpus callosum and the left anterior limb of the internal capsule (ALIC). Using fiber tractography, FA was significantly lower in schizophrenia in the corpus callosum genu (p = 0.003).

CONCLUSIONS

In schizophrenia, white matter diffusion deficits are prominent in medial frontal regions. These changes are consistent with the results of previous studies which have detected white matter changes in these areas. The pathology of schizophrenia may preferentially affect the prefrontal-thalamic white matter circuits traversing these regions.

Network dysfunction in Alzheimer's disease and frontotemporal dementia: implications for psychiatry

Structural and functional connectivity methods are changing how researchers conceptualize and explore neuropsychiatric disease. Here, we summarize emerging evidence of large-scale network dysfunction in Alzheimer's disease and behavioral variant frontotemporal dementia, focusing on the divergent impact these disorders have on the default mode network and the salience network. We update a working model for understanding the functions of these networks within a broader anatomical context and highlight the relevance of this model for understanding psychiatric illness. Finally, we look ahead to persistent challenges in the application of network-based imaging methods to patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and other neuropsychiatric conditions. Recent advances and persistent needs are discussed, with an eye toward anticipating the hurdles that must be overcome for a network-based framework to clarify the biology of psychiatric illness and aid in the drug discovery process.

Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.

Biomarker modeling of Alzheimer's disease

Alzheimer's disease (AD) is a slowly progressing disorder in which pathophysiological abnormalities, detectable in vivo by biomarkers, precede overt clinical symptoms by many years to decades. Five AD biomarkers are sufficiently validated to have been incorporated into clinical diagnostic criteria and commonly used in therapeutic trials. Current AD biomarkers fall into two categories: biomarkers of amyloid-β plaques and of tau-related neurodegeneration. Three of the five are imaging measures and two are cerebrospinal fluid analytes. AD biomarkers do not evolve in an identical manner but rather in a sequential but temporally overlapping manner. Models of the temporal evolution of AD biomarkers can take the form of plots of biomarker severity (degree of abnormality) versus time. In this Review, we discuss several time-dependent models of AD that take into consideration varying age of onset (early versus late) and the influence of aging and co-occurring brain pathologies that commonly arise in the elderly.

Network dysfunction after traumatic brain injury

Diffuse axonal injury after traumatic brain injury (TBI) produces neurological impairment by disconnecting brain networks. This structural damage can be mapped using diffusion MRI, and its functional effects can be investigated in large-scale intrinsic connectivity networks (ICNs). Here, we review evidence that TBI substantially disrupts ICN function, and that this disruption predicts cognitive impairment. We focus on two ICNs--the salience network and the default mode network. The activity of these ICNs is normally tightly coupled, which is important for attentional control. Damage to the structural connectivity of these networks produces predictable abnormalities of network function and cognitive control. For example, the brain normally shows a 'small-world architecture' that is optimized for information processing, but TBI shifts network function away from this organization. The effects of TBI on network function are likely to be complex, and we discuss how advanced approaches to modelling brain dynamics can provide insights into the network dysfunction. We highlight how structural network damage caused by axonal injury might interact with neuroinflammation and neurodegeneration in the pathogenesis of Alzheimer disease and chronic traumatic encephalopathy, which are late complications of TBI. Finally, we discuss how network-level diagnostics could inform diagnosis, prognosis and treatment development following TBI.

Application of quantitative DTI metrics in sporadic CJD

Diffusion Weighted Imaging is extremely important for the diagnosis of probable sporadic Jakob-Creutzfeldt disease, the most common human prion disease. Although visual assessment of DWI MRI is critical diagnostically, a more objective, quantifiable approach might more precisely identify the precise pattern of brain involvement. Furthermore, a quantitative, systematic tracking of MRI changes occurring over time might provide insights regarding the underlying histopathological mechanisms of human prion disease and provide information useful for clinical trials. The purposes of this study were: 1) to describe quantitatively the average cross-sectional pattern of reduced mean diffusivity, fractional anisotropy, atrophy and T1 relaxation in the gray matter (GM) in sporadic Jakob-Creutzfeldt disease, 2) to study changes in mean diffusivity and atrophy over time and 3) to explore their relationship with clinical scales. Twenty-six sporadic Jakob-Creutzfeldt disease and nine control subjects had MRIs on the same scanner; seven sCJD subjects had a second scan after approximately two months. Cortical and subcortical gray matter regions were parcellated with Freesurfer. Average cortical thickness (or subcortical volume), T1-relaxiation and mean diffusivity from co-registered diffusion maps were calculated in each region for each subject. Quantitatively on cross-sectional analysis, certain brain regions were preferentially affected by reduced mean diffusivity (parietal, temporal lobes, posterior cingulate, thalamus and deep nuclei), but with relative sparing of the frontal and occipital lobes. Serial imaging, surprisingly showed that mean diffusivity did not have a linear or unidirectional reduction over time, but tended to decrease initially and then reverse and increase towards normalization. Furthermore, there was a strong correlation between worsening of patient clinical function (based on modified Barthel score) and increasing mean diffusivity.

Frontotemporal dementia and primary progressive aphasia, a review

Frontotemporal dementias are neurodegenerative diseases in which symptoms of frontal and/or temporal lobe disease are the first signs of the illness, and as the diseases progress, they resemble a focal left hemisphere process such as stroke or traumatic brain injury, even more than a neurodegenerative disease. Over time, some patients develop a more generalized dementia. Four clinical subtypes characterize the predominant presentations of this illness: behavioral or frontal variant FTD, progressive nonfluent aphasia, semantic dementia, and logopenic primary progressive aphasia. These clinical variants correlate with regional patterns of atrophy on brain imaging studies such as MRI and PET scanning, as well as with biochemical and molecular genetic variants of the disorder. The treatment is as yet only symptomatic, but advances in molecular genetics promise new therapies.

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain's long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways.

Self-propagation of pathogenic protein aggregates in neurodegenerative diseases

For several decades scientists have speculated that the key to understanding age-related neurodegenerative disorders may be found in the unusual biology of the prion diseases. Recently, owing largely to the advent of new disease models, this hypothesis has gained experimental momentum. In a remarkable variety of diseases, specific proteins have been found to misfold and aggregate into seeds that structurally corrupt like proteins, causing them to aggregate and form pathogenic assemblies ranging from small oligomers to large masses of amyloid. Proteinaceous seeds can therefore serve as self-propagating agents for the instigation and progression of disease. Alzheimer's disease and other cerebral proteopathies seem to arise from the de novo misfolding and sustained corruption of endogenous proteins, whereas prion diseases can also be infectious in origin. However, the outcome in all cases is the functional compromise of the nervous system, because the aggregated proteins gain a toxic function and/or lose their normal function. As a unifying pathogenic principle, the prion paradigm suggests broadly relevant therapeutic directions for a large class of currently intractable diseases.