Periodic Reporting for period 4 - NeuroTRACK (Tracking and predicting neurodegeneration spreading across the brain connectome)
Periodo di rendicontazione: 2021-10-01 al 2023-09-30
• What is the problem/issue being addressed?
Clinical progression in neurodegenerative diseases involves the systematic spreading of misfolded proteins along neuronal pathways. In the CNS, protein aggregates would have the ability to trigger misfolding of adjacent homologue proteins in newly-affected regions, and this would propagate in a “prion-like” fashion. So far, however, we have lacked a useful framework for conceptualizing and predicting this networked disease evolution in humans. The recent development and application of graph theoretical tools into brain connectivity research suggested that patterns of disease propagation are mediated by the extremely complex, yet highly structured topology of the underlying neural architecture, the so-called connectome. It has been suggested that anatomical connections are a conduit for the physical spread of disease through the brain. The prevailing view is that the different vulnerability of brain regions to neurodegenerative changes would correlate with the connectional proximity to the origination site (i.e. primarily affected regions) and strength of neuronal connections between the affected areas.
• Why is it important for society?
Frontotemporal lobar degeneration (FTLD) encompasses a clinically and pathologically heterogeneous group of rapidly-progressive, non-Alzheimer neurodegenerative diseases for which there are currently no effective therapies. FTLD is less common than Alzheimer’s disease (AD). However, this disease group is of incommensurate importance as a cause of young onset dementia and/or motor deficits, with all the global societal economic costs that this implies. Understanding in vivo models of disease spreading in FTLD patients is relevant to the biology of these conditions, will allow the development of a new set of tools to track and predict disease progression for use both in clinical practice and in future therapeutic trials, and ultimately may prove vital in the search for effective treatments.
• What are the overall objectives?
The overarching goal of this research is to decipher the mechanisms of network-based neurodegeneration, in order to understand how the complex architecture of brain networks shapes the evolving pathology of neurodegenerative diseases, and to predict the time-course of a subject’s neuroanatomical changes and, therefore, their clinical and cognitive state in the future. To this end, we will apply graph theory and connectomics on multimodal, longitudinal 3T magnetic resonance imaging (MRI) data acquired in patients with FTLD, moving from early into the later stages of disease. An additional hope for this project is that connectomics can potentially contribute at multiple levels: determining the presence or absence of the disease (diagnosis), establishing imaging-based staging systems, defining risk prognosis, and prediction and monitoring of response to an intervention. It is also likely that results from this project will facilitate investigations in other diseases, such as AD and Parkinson’s disease.
Clinical progression in neurodegenerative diseases involves the systematic spreading of misfolded proteins along neuronal pathways. In the CNS, protein aggregates would have the ability to trigger misfolding of adjacent homologue proteins in newly-affected regions, and this would propagate in a “prion-like” fashion. So far, however, we have lacked a useful framework for conceptualizing and predicting this networked disease evolution in humans. The recent development and application of graph theoretical tools into brain connectivity research suggested that patterns of disease propagation are mediated by the extremely complex, yet highly structured topology of the underlying neural architecture, the so-called connectome. It has been suggested that anatomical connections are a conduit for the physical spread of disease through the brain. The prevailing view is that the different vulnerability of brain regions to neurodegenerative changes would correlate with the connectional proximity to the origination site (i.e. primarily affected regions) and strength of neuronal connections between the affected areas.
• Why is it important for society?
Frontotemporal lobar degeneration (FTLD) encompasses a clinically and pathologically heterogeneous group of rapidly-progressive, non-Alzheimer neurodegenerative diseases for which there are currently no effective therapies. FTLD is less common than Alzheimer’s disease (AD). However, this disease group is of incommensurate importance as a cause of young onset dementia and/or motor deficits, with all the global societal economic costs that this implies. Understanding in vivo models of disease spreading in FTLD patients is relevant to the biology of these conditions, will allow the development of a new set of tools to track and predict disease progression for use both in clinical practice and in future therapeutic trials, and ultimately may prove vital in the search for effective treatments.
• What are the overall objectives?
The overarching goal of this research is to decipher the mechanisms of network-based neurodegeneration, in order to understand how the complex architecture of brain networks shapes the evolving pathology of neurodegenerative diseases, and to predict the time-course of a subject’s neuroanatomical changes and, therefore, their clinical and cognitive state in the future. To this end, we will apply graph theory and connectomics on multimodal, longitudinal 3T magnetic resonance imaging (MRI) data acquired in patients with FTLD, moving from early into the later stages of disease. An additional hope for this project is that connectomics can potentially contribute at multiple levels: determining the presence or absence of the disease (diagnosis), establishing imaging-based staging systems, defining risk prognosis, and prediction and monitoring of response to an intervention. It is also likely that results from this project will facilitate investigations in other diseases, such as AD and Parkinson’s disease.
NeuroTRACK study focuses on the longitudinal analysis of connectome architecture changes and clinical evolution in patients with various clinical syndromes of Frontotemporal Lobar Degeneration (FTLD). The study cohort includes patients with syndromes such as behavioral variant Frontotemporal Dementia (bvFTD), semantic variant Primary Progressive Aphasia (PPA), nonfluent variant PPA, Progressive Supranuclear Palsy (PSP), Corticobasal Syndrome (CBS), and Amyotrophic Lateral Sclerosis (ALS). The study involves a multicenter collaborative platform in Northern Italy.
The research explores age-related vulnerability in the human brain connectome, highlighting changes in brain function and structure contributing to age-related cognitive decline. The study identifies alterations in fronto-temporo-parietal hubs crucial for healthy cognition and investigates the impact of functional connectivity on age-related brain changes.
Within the FTLD spectrum, the study reveals shared and distinct connectome alterations in different syndromes. For example, bvFTD exhibits extensive disruption in frontotemporal and parietal networks, while ALS without cognitive impairment shows focal damage in sensorimotor-basal ganglia areas. The study also explores connectome changes across ALS clinical stages, indicating progressive decreases in structural connectivity in sensorimotor regions.
Experimental measures are employed to assess cognition, behavior, and motor functions in FTLD, providing Italian reference values for verbal fluency. The study investigates social cognition and emotion processing using MRI tools, revealing widespread functional connectivity changes in FTLD patients. Additionally, the research explores awareness impairment in FTD and other dementias through systematic MRI reviews.
The study investigates disruptions in brain networks in FTLD, emphasizing imbalances in local and global connectivity crucial for cognitive function. Mathematical frameworks support the prion-like spreading hypothesis, suggesting that disconnections initiate from specific neurodegenerative epicenters and propagate through interconnected neural networks. The study employs Stepwise Functional Connectivity (SFC) to examine different FTD variants, identifying atrophy in specific brain regions. NeuroTRACK explores the relationship between network vulnerability and longitudinal atrophy progression in FTD, using the Network Diffusion Model (NDM). NDM predicts pathology spread through brain regions, and correlations between predicted atrophy and empirically observed atrophy support the influence of healthy structural architecture on disease progression.
NeuroTRACK also explores genetic mutations in FTLD, highlighting distinct patterns in C9orf72 and GRN mutations. Phenotypic heterogeneity is observed in a family with the rare MAPT Q336H mutation, and TARDBP mutation-related motor neuron disease exhibits distinctive cortical atrophy and white matter tract damage.
Finally, the study assesses the diagnostic and prognostic value of serum biomarkers, identifying NfL as a strong diagnostic and prognostic marker, with UCHL1 providing independent prognostic information. GFAP reflects extramotor involvement.
The research explores age-related vulnerability in the human brain connectome, highlighting changes in brain function and structure contributing to age-related cognitive decline. The study identifies alterations in fronto-temporo-parietal hubs crucial for healthy cognition and investigates the impact of functional connectivity on age-related brain changes.
Within the FTLD spectrum, the study reveals shared and distinct connectome alterations in different syndromes. For example, bvFTD exhibits extensive disruption in frontotemporal and parietal networks, while ALS without cognitive impairment shows focal damage in sensorimotor-basal ganglia areas. The study also explores connectome changes across ALS clinical stages, indicating progressive decreases in structural connectivity in sensorimotor regions.
Experimental measures are employed to assess cognition, behavior, and motor functions in FTLD, providing Italian reference values for verbal fluency. The study investigates social cognition and emotion processing using MRI tools, revealing widespread functional connectivity changes in FTLD patients. Additionally, the research explores awareness impairment in FTD and other dementias through systematic MRI reviews.
The study investigates disruptions in brain networks in FTLD, emphasizing imbalances in local and global connectivity crucial for cognitive function. Mathematical frameworks support the prion-like spreading hypothesis, suggesting that disconnections initiate from specific neurodegenerative epicenters and propagate through interconnected neural networks. The study employs Stepwise Functional Connectivity (SFC) to examine different FTD variants, identifying atrophy in specific brain regions. NeuroTRACK explores the relationship between network vulnerability and longitudinal atrophy progression in FTD, using the Network Diffusion Model (NDM). NDM predicts pathology spread through brain regions, and correlations between predicted atrophy and empirically observed atrophy support the influence of healthy structural architecture on disease progression.
NeuroTRACK also explores genetic mutations in FTLD, highlighting distinct patterns in C9orf72 and GRN mutations. Phenotypic heterogeneity is observed in a family with the rare MAPT Q336H mutation, and TARDBP mutation-related motor neuron disease exhibits distinctive cortical atrophy and white matter tract damage.
Finally, the study assesses the diagnostic and prognostic value of serum biomarkers, identifying NfL as a strong diagnostic and prognostic marker, with UCHL1 providing independent prognostic information. GFAP reflects extramotor involvement.
This ERC project is entailing a multimodal, longitudinal approach, including serial 3T MRI studies to assess structural and functional connectome properties in patients, genetic studies to recognize relevant genetic variations and identify mutated presymptomatic subjects, and clinical, cognitive and behavioural quantitative metrics, which yield fundamental details about the evolution of the disease. If a new set of robust imaging tools to characterise network disorganisation can be identified, we should be able to explain the phenotypic diversity in FTLD and to track and predict disease progression for use both in future therapeutic trials, and in monitoring personalised treatment. The ground-breaking nature of the experimental approaches we are performing will pave the way to making inroads into fundamental questions about the progression of neurodegenerative diseases across brain networks; for developing tools for understanding the biological underpinnings of CNS proteinopathies; to monitoring accurately changes in patients during life; and to identifying individualized, early interventions that have the potential to modify disease progression.