Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS: To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS: Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION: This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses.

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.

Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum.

The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.

Detection of maltreatment of people with dementia in Spain: usefulness of the Caregiver Abuse Screen (CASE).

The objective of our study is to validate the Caregiver Abuse Screen (CASE) as an instrument for detecting the maltreatment of people with dementia in Spain. In total, 326 informal caregivers of people with different types of dementia were interviewed in several cities in northwest Spain. The caregivers were selected from outpatient neurology clinics and associations of relatives of people with Alzheimer's disease and other dementias. A comprehensive sociodemographic questionnaire was administered to all participants, and several standardized scales were used to assess burden, anxiety, depression, social support and resilience. The "Psychological Aggression" and "Physical Assault" dimensions of the Revised Conflicts Tactics Scale were used as risk factors of caregivers' maltreatment for the construct validation. To establish the probability of maltreatment, a latent class analysis was carried out according to the item responses obtained from the CASE. The internal consistency (Cronbach's alpha) of the CASE was 0.71. The construct validity was explored through factorial analysis, and we found that two dimensions of CASE-i.e., interpersonal abuse and neglect/dependency-explained 62.5% of the variability. According to the latent class probabilities, 20.4% of participants were categorized as possible abusers and 21.4% as non-abusers. The optimal maltreatment cutoff point was six points on the CASE. The validation of the CASE provides us a brief and easy instrument for detecting possible cases of maltreatment of Spanish people with dementia.

Prevalence of dementia and subtypes in Valladolid, northwestern Spain: the DEMINVALL study.

OBJECTIVE: To describe the prevalence of dementia and subtypes in a general elderly population in northwestern Spain and to analyze the influence of socio-demographic factors. METHODS: Cross-sectional, two-phase, door-to-door, population-based study. A total of 870 individuals from a rural region and 2,119 individuals from an urban region of Valladolid, Spain, were involved. The seven-minute screen neurocognitive battery was used in the screening phase. A control group was included. RESULTS: A total of 2,170 individuals aged 65 to 104 years (57% women) were assessed. There were 184 subjects diagnosed with dementia. The crude prevalence was 8.5% (95% CI: 7.3-9.7). Age- and sex-adjusted prevalence was 5.5 (95% CI: 4.5-6.5). Main subtypes of dementia were: Alzheimer's disease (AD) 77.7%, Lewy Body disease, 7.6% and vascular dementia (VD) 5.9%. Crude prevalences were 6.6% (AD), 0.6% (Lewy Body disease), and 0.5% (VD). Dementia was associated with age (OR 1.14 for 1-year increase in age), female sex (OR 1.79) and the absence of formal education (OR 2.53 compared to subjects with primary education or more). CONCLUSION: The prevalence of dementia in the study population was lower than the most recent estimates for Western Europe. There was a high proportion of AD among all dementia cases and very low prevalence of VD. Old age, female sex, and low education level were independent risk factors for dementia and AD.

Design, methods and demographic findings of the DEMINVALL survey: a population-based study of Dementia in Valladolid, Northwestern Spain.

BACKGROUND: This article describes the rationale and design of a population-based survey of dementia in Valladolid (northwestern Spain). The main aim of the study was to assess the epidemiology of dementia and its subtypes. Prevalence of anosognosia in dementia patients, nutritional status, diet characteristics, and determinants of non-diagnosed dementia in the community were studied. The main sociodemographic, educational, and general health status characteristics of the study population are described. METHODS: Cross-over and cohort, population-based study. A two-phase door-to-door study was performed. Both urban and rural environments were included. In phase 1 (February 2009 - February 2010) 28 trained physicians examined a population of 2,989 subjects (age: ≥ 65 years). The seven-minute screen neurocognitive battery was used. In phase 2 (May 2009 - May 2010) 4 neurologists, 1 geriatrician, and 3 neuropsychologists confirmed the diagnosis of dementia and subtype in patients screened positive by a structured neurological evaluation. Specific instruments to assess anosognosia, the nutritional status and diet characteristics were used. Of the initial sample, 2,170 subjects were evaluated (57% female, mean age 76.5 ± 7.8, 5.2% institutionalized), whose characteristics are described. 227 persons were excluded for various reasons. Among those eligible were 592 non-responders. The attrition bias of non-responders was lower in rural areas. 241 screened positive (11.1%). DISCUSSION: The survey will explore some clinical, social and health related life-style variables of dementia. The population size and the diversification of social and educational backgrounds will contribute to a better knowledge of dementia in our environment.

Complete bilateral ophthalmoplegia resistant to caloric stimulation in bilateral paramedian midbrain-thalamic infarction.

A 79-year-old woman who developed bilateral paramedian midbrain-thalamic infarction manifested complete bilateral ophthalmoplegia resistant to caloric stimulation, indicating impairment of the vestibulo-ocular reflex (VOR). Previous reports have mentioned this phenomenon but have not explicitly reported the results of caloric testing. Why a lesion apparently confined to the upper brainstem should produce impairment of the horizontal VOR remains unexplained.