Temporal dynamic changes of intrinsic brain activity in Alzheimer’s disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study

Abstract

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory impairment. Previous studies have largely focused on alterations of static brain activity occurring in patients with AD. Few studies to date have explored the characteristics of dynamic brain activity in cognitive impairment, and their predictive ability in AD patients.Methods: One hundred and eleven AD patients, 29 MCI patients, and 73 healthy controls (HC) were recruited. The dynamic amplitude of low-frequency fluctuation (dALFF) and the dynamic fraction amplitude of low-frequency fluctuation (dfALFF) were used to assess the temporal variability of local brain activity in patients with AD or mild cognitive impairment (MCI). Pearson's correlation coefficients were calculated between the metrics and subjects' behavioral scores. Results:The results of analysis of variance indicated that the AD, MCI, and HC groups showed significant variability of dALFF in the cerebellar posterior and middle temporal lobes. In AD patients, these brain regions had high dALFF variability. Significant dfALFF variability was found between the three groups in the left calcarine cortex and white matter. The AD group showed lower dfALFF than the MCI group in the left calcarine cortex.Conclusions: Compared to HC, AD patients were found to have increased dALFF variability in the cerebellar posterior and temporal lobes. This abnormal pattern may diminish the capacity of the cerebellum and temporal lobes to participate in the cerebrocerebellar circuits and default mode network (DMN), which regulate cognition and emotion in AD. The findings above indicate that the analysis of dALFF and dfALFF based on functional magnetic resonance imaging data may give a new insight into the neurophysiological mechanisms of AD.

A machine-learning approach for detection of local brain networks and marginally weak signals identifies novel AD/MCI differentiating connectomic neuroimaging biomarkers

“…Figure 5 shows the top selected marginally weak signals, along with their marginal and network-adjusted effects. Note that some regions identified containing mostly marginally weak signals have either not been reported to be associated with AD or MCI, or just been discovered recently, such as middle temporal gyrus 46 , olfactory bulbs 47 , lingual gyrus 48 and amygdala 69 . These novel findings demonstrate the power of our method in identifying novel neuroimaging biomarkers.…”

Subcortical and Cerebellar Neural Correlates of Prodromal Alzheimer’s Disease with Prolonged Sleep Latency

“…Moreover, changes of cerebro-cerebellar FC were closely correlated with cognitive subdomains in AD and aMCI [79], and aMCI subjects demonstrated lower anti-correlation between cerebro-cerebellar and DMN FC [80]. Further, when compared with HC and MCI patients, AD patients exhibited significantly decreased dynamic amplitude of low-frequency fluctuation variability in the cerebellum and temporal lobes [81]. With regard to sleep and the cerebellum, a meta-analysis on resting-state fMRI studies in patients with persistent insomnia disorder found diminished activation of cerebellum and superior frontal gyrus [82].…”

Abnormal dynamic brain activity and functional connectivity of primary motor cortex in blepharospasm

“…To exclude spurious fluctuations, the minimum window length should be larger than 1/f min, where f min is defined as the minimum frequency of the time series [34]. A window length of 50 TRs (100 s), with a step size of 5 TRs (10 s) as the optimal parameter, was selected to achieve a better balance between capturing rapidly shifting dynamic activity and achieving reliable estimates of the brain activity [18,22,23,35]. Consequently, a set of ALFF maps was computed and the corresponding coefficient of variation considered as the variance of the dALFF maps across all the windows was applied to measure temporal variability.…”

“…These features mean that the dynamic approach is likely to capture useful information missed in the static method [17]. Furthermore, neuroimaging studies have confirmed that dALFF and dFC can provide valuable information complementary to that obtained using sALFF and sFC, and new insights into the underlying neuropathological mechanisms in neurodegenerative diseases, psychiatric disorders, and cervical discogenic pain [18–23]. However, whether dALFF and dFC measurements can provide valuable information regarding the detection of functional abnormalities in patients with BSP and whether this information is helpful to improving our understanding of the pathophysiology of BSP remain unclear.…”