Functional biomarkers for neurodegenerative disorders based on the network paradigm

Abstract

This commentary provides a brief introduction to the various uses that functional neuroimaging biomarkers can play in detecting, diagnosing, assessing treatment response and investigating neurodegenerative disorders. It then goes on to explain why the emphasis of much recent work has shifted to network-based biomarkers, as opposed to those that examine individual brain regions. A number of examples are referenced that illustrate the points made.

Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA

“…Multicenter and longitudinal studies comparing different connectivity methods showed that graph theory provides reliable, replicable and robust measures of network abnormalities in patients with frontotemporal dementia (FTD) (Sedeño et al, 2017) as well as the best unit-wise reliability (Guo et al, 2012). This evidence suggests that graph theory is uniquely suited to studying network dynamics in focal neurodegenerative diseases (Horwitz & Rowe, 2011). …”

Patterns of gray matter atrophy in atypical parkinsonism syndromes: a VBM meta‐analysis

“…Atypical Parkinsonism syndromes (APS) constitute a subset known to resemble Idiopathic Parkinson's Disease (IPD) on a clinical basis: multi-system atrophy (MSA), progressive supranuclear palsy, (PSP) and corticobasal degeneration (CBD) (Stamelou et al 2013). Distinguishing these disorders from IPD, and from each other, is important given differences in prognosis and potential therapies, as well as the growing elderly population (Horwitz and Rowe 2011). Historically, this was based on physical exam findings of clinical Parkinsonism (e.g., rigidity, bradykinesia, tremors) with atypical features (e.g., autonomic symptoms, vertical gaze palsy).…”

Successful Object Encoding Induces Increased Directed Connectivity in Presymptomatic Early-Onset Alzheimer’s Disease

“…This approach provides information about the interaction of different brain regions in terms of the underlying brain networks. Recently has been discussed that the connectivity approach is highly sensitive to the neurological disease presence, is able to distinguish among alternate diagnostics, gives insights about the disease phenomenology, and also is sensitive to progression and therapy effect [21]. Although the connectivity can be studied at electrode level, there are techniques like the inverse solution methods, that enable the study of the connectivity directly between brain regions [22–24].…”