Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.

Stimuli from multiple sensory organs can be integrated into a coherent representation through multiple phases of multisensory processing; this phenomenon is called multisensory integration. Multisensory integration can interact with attention. Here, we propose a framework in which attention modulates multisensory processing in both endogenous (goal-driven) and exogenous (stimulus-driven) ways. Moreover, multisensory integration exerts not only bottom-up but also top-down control over attention. Specifically, we propose the following: (1) endogenous attentional selectivity acts on multiple levels of multisensory processing to determine the extent to which simultaneous stimuli from different modalities can be integrated; (2) integrated multisensory events exert top-down control on attentional capture via multisensory search templates that are stored in the brain; (3) integrated multisensory events can capture attention efficiently, even in quite complex circumstances, due to their increased salience compared to unimodal events and can thus improve search accuracy; and (4) within a multisensory object, endogenous attention can spread from one modality to another in an exogenous manner.

When humans perceive a sensation, their brains integrate inputs from sensory receptors and process them based on their expectations. The mechanisms of this predictive coding in the human somatosensory system are not fully understood. We fill a basic gap in our understanding of the predictive processing of somatosensation by examining the layer-specific activity in sensory input and predictive feedback in the human primary somatosensory cortex (S1). We acquired submillimeter functional magnetic resonance imaging data at 7T (n = 10) during a task of perceived, predictable, and unpredictable touching sequences. We demonstrate that the sensory input from thalamic projects preferentially activates the middle layer, while the superficial and deep layers in S1 are more engaged for cortico-cortical predictive feedback input. These findings are pivotal to understanding the mechanisms of tactile prediction processing in the human somatosensory cortex.

Audiovisual integration occurs frequently and has been shown to exhibit age-related differences via behavior experiments or time-frequency analyses. In the present study, we examined whether functional connectivity influences audiovisual integration during normal aging. Visual, auditory, and audiovisual stimuli were randomly presented peripherally; during this time, participants were asked to respond immediately to the target stimulus. Electroencephalography recordings captured visual, auditory, and audiovisual processing in 12 old (60–78 years) and 12 young (22–28 years) male adults. For non-target stimuli, we focused on alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) bands. We applied the Phase Lag Index to study the dynamics of functional connectivity. Then, the network topology parameters, which included the clustering coefficient, path length, small-worldness global efficiency, local efficiency and degree, were calculated for each condition. For the target stimulus, a race model was used to analyze the response time. Then, a Pearson correlation was used to test the relationship between each network topology parameters and response time. The results showed that old adults activated stronger connections during audiovisual processing in the beta band. The relationship between network topology parameters and the performance of audiovisual integration was detected only in old adults. Thus, we concluded that old adults who have a higher load during audiovisual integration need more cognitive resources. Furthermore, increased beta band functional connectivity influences the performance of audiovisual integration during normal aging.

Retinotopic mapping is a key property of organization in the human occipital cortex. The retinotopic organization of the central visual field of visual areas V1, V2, and V3 has been well established. We used fMRI to measure the retinotopic map of the peripheral visual field (eccentricity up to 60°). We estimated the sizes of the visual areas between 0° and 60° and obtained results consistent with anatomical studies. We also estimated the cortical distances and magnification factors for reconstruction of the retinotopic map using the peripheral wedge dipole model. By comparing the retinotopic map with the flattened surface, we analyzed the datasets used to reconstruct the map. We found that: (1) the percentage of the striate cortex devoted to peripheral vision in humans is significantly larger than that in the macaque, (2) the estimate of the scaling factor in linear magnification is larger than that found in previous studies focusing on central vision, and (3) the estimate of the peripheral factor in the dipolar model is too large to make the curve direction of the dipolar map in the periphery equivalent to that in the center. On the basis of our results, we revised the dipolar map to fit our conditions. The revised map in humans has a similar elliptical shape to that of macaques, and the central parts of the two species are the same. The different parts of the map are the peripheral regions, for which the peripheral wedge dipole model in humans is reversed compared to that of macaques.

By 2050 it is estimated that the number of worldwide Alzheimer’s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Although neuronal studies have shown that audiovisual integration is regulated by temporal factors, there is still little knowledge about the impact of temporal factors on audiovisual integration in older adults. To clarify how stimulus onset asynchrony (SOA) between auditory and visual stimuli modulates age-related audiovisual integration, 20 younger adults (21-24 years) and 20 older adults (61-80 years) were instructed to perform an auditory or visual stimuli discrimination experiment. The results showed that in younger adults, audiovisual integration was altered from an enhancement (AV, A ± 50 V) to a depression (A ± 150 V). In older adults, the alterative pattern was similar to that for younger adults with the expansion of SOA; however, older adults showed significantly delayed onset for the time-window-of-integration and peak latency in all conditions, which further demonstrated that audiovisual integration was delayed more severely with the expansion of SOA, especially in the peak latency for V-preceded-A conditions in older adults. Our study suggested that audiovisual facilitative integration occurs only within a certain SOA range (e.g., -50 to 50 ms) in both younger and older adults. Moreover, our results confirm that the response for older adults was slowed and provided empirical evidence that integration ability is much more sensitive to the temporal alignment of audiovisual stimuli in older adults.

Alzheimer’s disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer’s disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD.