Mapping Neurodegenerative Disease Onset and Progression

Seeley, William W.

doi:10.1101/cshperspect.a023622

Cited by 81 publications

(78 citation statements)

References 97 publications

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“…These findings are consistent with the notion of selective vulnerability of neural networks to specific proteinopathies, with AD pathology having a tropism toward posterior temporoparietal brain regions, tau pathology toward frontostriatal networks, and TDP-C pathology toward the temporal pole. 19,47,48 When these pathologies demonstrate lateralization toward the language-dominant hemisphere, this may result in distinct variants of PPA (lvPPA, nfvPPA, or svPPA, respectively). The mechanisms underlying lateralization of pathology in PPA remain a mystery.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

Bergeron

Gorno‐Tempini

Rabinovici

et al. 2018

Annals of Neurology

156

111

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Objective: To estimate the prevalence of amyloid positivity, defined by positron emission tomography (PET)/cerebro-spinal fluid (CSF) biomarkers and/or neuropathological examination, in primary progressive aphasia (PPA) variants. Methods: We conducted a meta-analysis with individual participant data from 1,251 patients diagnosed with PPA (including logopenic [lvPPA, n = 443], nonfluent [nfvPPA, n = 333], semantic [svPPA, n = 401], and mixed/unclassifiable [n = 74] variants of PPA) from 36 centers, with a measure of amyloid-β pathology (CSF [n = 600], PET [n = 366], and/or autopsy [n = 378]) available. The estimated prevalence of amyloid positivity according to PPA variant, age, and apolipoprotein E (ApoE) ε4 status was determined using generalized estimating equation models. Results: Amyloid-β positivity was more prevalent in lvPPA (86%) than in nfvPPA (20%) or svPPA (16%; p < 0.001). Prevalence of amyloid-β positivity increased with age in nfvPPA (from 10% at age 50 years to 27% at age 80 years, p < 0.01) and svPPA (from 6% at age 50 years to 32% at age 80 years, p < 0.001), but not in lvPPA (p = 0.94). Across PPA variants, ApoE ε4 carriers were more often amyloid-β positive (58.0%) than noncarriers (35.0%, p < 0.001). Autopsy data revealed Alzheimer disease pathology as the most common pathologic diagnosis in lvPPA (76%), frontotemporal lobar degeneration–TDP-43 in svPPA (80%), and frontotemporal lobar degeneration–TDP-43/tau in nfvPPA (64%). Interpretation: This study shows that the current PPA classification system helps to predict underlying pathology across different cohorts and clinical settings, and suggests that age and ApoE genotype should be considered when interpreting amyloid-β biomarkers in PPA patients.

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Section: Discussionmentioning

confidence: 99%

Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

Bergeron

Gorno‐Tempini

Rabinovici

et al. 2018

Annals of Neurology

156

111

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“…The complex DLB connectivity signature fits well with the complexity of its underlying pathology, as well as with the heterogeneity of the clinical phenotype, suggesting that DLB connectivity changes can be best captured by clusters of large‐scale networks alterations. At difference, most neurodegenerative conditions are characterized by connectivity changes affecting one main large‐scale network (Pievani et al, ; Seeley, ; Seeley et al, ). Crucially, in DLB, a multinetwork derangement is evident since the early disease phases, as in our series (disease duration = 2.5 years), suggesting a fast, widespread breakdown of the brain functional architecture.…”

Section: Discussionmentioning

confidence: 99%

Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies

Sala

Caminiti

Iaccarino

et al. 2019

Human Brain Mapping

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Aberrations of large‐scale brain networks are found in the majority of neurodegenerative disorders. The brain connectivity alterations underlying dementia with Lewy bodies (DLB) remain, however, still elusive, with contrasting results possibly due to the pathological and clinical heterogeneity characterizing this disorder. Here, we provide a molecular assessment of brain network alterations, based on cerebral metabolic measurements as proxies of synaptic activity and density, in a large cohort of DLB patients (N = 72). We applied a seed‐based interregional correlation analysis approach (p < .01, false discovery rate corrected) to evaluate large‐scale resting‐state networks' integrity and their interactions. We found both local and long‐distance metabolic connectivity alterations, affecting the posterior cortical networks, that is, primary visual and the posterior default mode network, as well as the limbic and attention networks, suggesting a widespread derangement of the brain connectome. Notably, patients with the lowest visual and attention cognitive scores showed the most severe connectivity derangement in regions of the primary visual network. In addition, network‐level alterations were differentially associated with the core clinical manifestations, namely, hallucinations with more severe metabolic dysfunction of the attention and visual networks, and rapid eye movement sleep behavior disorder with alterations of connectivity of attention and subcortical networks. These multiple network‐level vulnerabilities may modulate the core clinical and cognitive features of DLB and suggest that DLB should be considered as a complex multinetwork disorder.

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“…At present, diagnostic biomarkers are not available, except in rare cases in which a causative genetic mutation can be shown to cause the disorder (Ghasemi and Brown 2016;Gijselinck et al 2016;Hinz and Geschwind 2016;TCWand Goate 2016). Hence, specific in vivo biomarkers, including biofluid Amyloidoses Creutzfeldt -Jakob disease (genetic, variant, sporadic, iatrogenic) and molecular imaging markers (some highlighted in Kolb and Andres 2016;Seeley 2016), are a major research priority. The most common neurodegenerative disorders are amyloidoses, tauopathies, a-synucleinopathies, and transactivation response DNA binding protein 43 (TDP-43) proteinopathies.…”

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confidence: 99%

Pathology of Neurodegenerative Diseases

Dugger

Dickson

2017

Cold Spring Harb Perspect Biol

1,151

702

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Neurodegenerative disorders are characterized by progressive loss of selectively vulnerable populations of neurons, which contrasts with select static neuronal loss because of metabolic or toxic disorders. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormality. The most common neurodegenerative disorders are amyloidoses, tauopathies, α-synucleinopathies, and TDP-43 proteinopathies. The protein abnormalities in these disorders have abnormal conformational properties. Growing experimental evidence suggests that abnormal protein conformers may spread from cell to cell along anatomically connected pathways, which may in part explain the specific anatomical patterns observed at autopsy. In this review, we detail the human pathology of select neurodegenerative disorders, focusing on their main protein aggregates.

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Mapping Neurodegenerative Disease Onset and Progression

Cited by 81 publications

References 97 publications

Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

Prevalence of amyloid‐β pathology in distinct variants of primary progressive aphasia

Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies

Pathology of Neurodegenerative Diseases

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