Possible Biomarkers for Frontotemporal Dementia and to Differentiate from Alzheimer’s Disease and Amyotrophic Lateral Sclerosis

C9orf72is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) in Caucasian populations. However, the relationship betweenC9orf72repeats and Alzheimer’s disease (AD) was not clear. Additionally, there were few articles assessingC9orf72in other ethnicities with ALS. In this meta-analysis, we aimed to investigate the relationship betweenC9orf72repeat expansions (≥30 repeats) and intermediate repeat copies (20–29 repeats) and AD or ALS. The results suggested positive correlations betweenC9orf72repeat expansions and the risk of Alzheimer’s disease (OR = 6.36, 95% CI = 3.13–12.92, andp<0.00001), while intermediate repeat copies ofC9orf72gene were not associated with the risk of the disease.C9orf72repeat expansions were positively correlated with the risk of familial and sporadic ALS (OR = 293.25, 95% CI = 148.17–580.38, andp<0.00001; OR = 35.57, 95% CI = 19.61–64.51, andp<0.00001). There was a positive correlation between the gene variations and ALS risk among Caucasians and Asians (OR = 57.56, 95% CI = 36.73–90.22, andp<0.00001; OR = 6.35, 95% CI = 1.39–29.02, andp=0.02).

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Distinctions between the dementia in Amyotrophic Lateral Sclerosis with Frontotemporal Dementia and the dementia of Alzheimer's disease

Amyotrophic Lateral Sclerosis ◽

10.1080/17482960701381911 ◽

2007 ◽

Vol 8 (5) ◽

pp. 276-282 ◽

Cited By ~ 14

Author(s):

Jennifer Heidler‐Gary ◽

Argye E. Hillis

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyotrophic Lateral Sclerosis ◽

Frontotemporal Dementia ◽

Lateral Sclerosis

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TDP-43 and Tau Oligomers in Alzheimer's Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia

Neurobiology of Disease ◽

10.1016/j.nbd.2020.105130 ◽

2020 ◽

Vol 146 ◽

pp. 105130

Author(s):

Mauro Montalbano ◽

Salome McAllen ◽

Filippa Lo Cascio ◽

Urmi Sengupta ◽

Stephanie Garcia ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyotrophic Lateral Sclerosis ◽

Frontotemporal Dementia ◽

Tau Oligomers ◽

Lateral Sclerosis

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Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

10.1101/2021.12.23.473997 ◽

2021 ◽

Author(s):

Anita Monteverdi ◽

Fulvia Palesi ◽

Alfredo Costa ◽

Paolo Vitali ◽

Anna Pichiecchio ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyotrophic Lateral Sclerosis ◽

Functional Connectivity ◽

Neurodegenerative Diseases ◽

Frontotemporal Dementia ◽

Neural Mass Model ◽

Brain Dynamics ◽

The Impact ◽

Lateral Sclerosis

Brain pathologies are based on microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients excitation/inhibition profiles on neurodegenerative diseases including Alzheimer's disease, Frontotemporal Dementia and Amyotrophic Lateral Sclerosis. In this work we used a simulation platform, The Virtual Brain, to simulate brain dynamics in healthy controls and in Alzheimer's disease, Frontotemporal Dementia and Amyotrophic Lateral Sclerosis patients. The brain connectome and functional connectivity were extracted from 3T-MRI scans and The Virtual Brain nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. The integration of cerebro-cerebellar loops improved the correlation between experimental and simulated functional connectivity, and hence The Virtual Brain predictive power, in all pathological conditions. The Virtual Brain biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimer's disease and stronger NMDA (N-methyl-D-aspartate) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed an advanced analysis of patients' state. In backward regressions, The Virtual Brain parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of The Virtual Brain parameters and neuropsychological scores significantly improved discriminative power between clinical conditions. Eventually, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. In aggregate, The Virtual Brain simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.

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