Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Abstract

Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.

Older Adults Diagnosed With Schizophrenia in Long-term Care Facilities: Life Course Theory for Holistic Nursing Research and Practice

“…Alzheimer's disease, the most common cause of dementia or neurocognitive disorder, can cause functional disruptions of speech, orientation, and memory (Alzheimer’s Association [AA], 2021; Perpetuini et al, 2020). OADWS often exhibit similarly disorganized or incoherent speech, as well as difficulties in memory and orientation (APA, 2013).…”

Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer’s Disease

“…This theory postulates that, as stated by Goldberger et al 24 : 1) "the output of healthy systems (...) reveals a type of complex variability associated with long-range correlations", and 2) "this type of multiscale, nonlinear complexity breaks down with aging and disease, reducing the adaptive capabilities of the individual". Recent evidence seems to support this theory as, for instance, entropy measures of cerebral blood flow velocity decayed in altered states 27 , actigraphy data exhibited lower complexity in depressed subjects as compared to non-depressed controls 28 , and complexity of EEG signals from subjects with AD correlated well with performance in working memory tasks 29 . MSE analysis can have significant implications in biomedical research as several studies have demonstrated its application to detection of heartbeat irregularity 22,30 , postural control alterations 31 and postoperative neurocognitive dysfunction 32 , and diagnosis of depression 28 , Parkinson's disease 33 , schizophrenia 34 , and AD 35 .…”

Evidence of Neurovascular Un-Coupling in Mild Alzheimer’s Disease through Multimodal EEG-fNIRS and Multivariate Analysis of Resting-State Data

“…Because of the absence of electro-optical interference with these two scalp-located technologies, it is simple to integrate fNIRS with EEG. The simplicity of the hardware integration also encourages the development of unified analytical frameworks [19,47,48]. EEG is a well-established technique in neurological and neuroimaging settings suitable for capturing the temporal dynamics of brain electrical activity through measurements of voltages generated by macroscopic currents within neuronal ensembles [49].…”