Alz Res Therapy: Changes in brain function networks may predict cognitive decline in advance

Due to the lack of effective treatments, recent research has focused on the early diagnosis of Alzheimer's disease (AD)
.

Subjective cognitive decline (SCD), which is considered a risk state of AD, has received more and more attention

Diagnostic neuroimaging technology has been developed to identify non-invasive biomarkers in the early stages of AD

Since the destruction of functional connectivity (FC) occurs at the earliest stage of AD, FC has been considered as a potential neurobiomarker for early identification of functional changes related to the pathophysiology of AD
.

However, previous research mainly focused on static FC (sFC), which should be stable in a static state, although FC is highly variable during imaging

Dynamic FC (DFC) contains information about the dynamic functional organization of the brain and has attracted more and more attention in the past few years
.

In addition, DFC is closely related to cognition and may be a biomarker of dementia

Although the important role of DFC in dementia has been gradually recognized, the spontaneous activity of dynamic areas has not been well explored
.

Some studies have shown that low-frequency resting-state functional magnetic resonance imaging (fMRI) activities, such as the quantification of low-frequency fluctuation amplitude (ALFF) or fractional ALFF (fALFF), are very suitable for measuring cognitive ability, but whether the dynamic mode of ALFF or fALFF is consistent with Cognitive decline is still unclear

In this way, Lingyan Liang of Guangxi University of Traditional Chinese Medicine and others investigated the repetitive dynamic fALFF (dfALFF) and DFC patterns (i.

e.

Therefore, these recurring and concurrent patterns found in dynamic area activity and FC may become neuroimaging biomarkers for the diagnosis of SCD and the conversion from SCD to MCI

They found some recurring states of dfALFF and DFC, and further determined the co-occurrence patterns of these two types of dynamic brain states (ie dfALFF and DFC states)
.

It is important that the frequency of the DFC state dominated by the default mode network (DMN) is significantly different between HCs and SCD patients, while the co-occurrence frequency of the DFC state dominated by DMN and the dfALFF state dominated by DMN is between SCD and MCI patients.

There are also significant differences between

These two dynamic characteristics are obviously positively correlated with the score of Mini-Mental State Examination
.

This study shows that the new neural features of fMRI-based cognitive decline come from the repeated and concurrent patterns of dfALFF and DFC , providing strong evidence to support SCD as a transitional stage between normal aging and MCI
.

This discovery may use dFC and dfALFF as objective neuroimaging biomarkers to distinguish SCD patients from HCs, which may help early diagnosis and intervention of Alzheimer's disease
.

The new neural features of fMRI-based cognitive decline come from the repeated and concurrent patterns of dfALFF and DFC

Original source:
Liang L, Yuan Y, Wei Y, et al.
Recurrent and concurrent patterns of regional BOLD dynamics and functional connectivity dynamics in cognitive decline.
Alz Res Therapy.
2021;13(1):28.
doi:10.
1186/s13195- 020-00764-6

Recurrent and concurrent patterns of regional BOLD dynamics and functional connectivity dynamics in cognitive decline.

 

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