Increasing access to transplantation through telemedicine and patient navigation.

BACKGROUND: Hematopoietic cell transplantation (HCT) is a promising treatment for hematological diseases, yet access barriers like cost and limited transplant centers persist. Telemedicine-based patient navigation (PN) has emerged as a solution. This study presents a cost-free PN telemedicine clinic (TC) in collaboration with the National Marrow Donor Program. AIM: to assess its feasibility and impac on HCT access determined by the cumulative incidence of transplantation. METHODS: In this single-center cohort study, patients of all ages and diagnoses referred for HCT participated. Two transplant physician-navigators established patient relationships via video calls, collecting medical history, offering HCT education and recommending pretransplant tests. The analysis involved descriptive statistics and intent-to-transplant survival assessment. RESULTS: One hundred and three patients were included of whom n = 78 were referred for allogeneic HCT (alloHCT), with a median age of 28 years. The median time from initial contact to the first consult was 5 days. The cumulative incidence of transplantation was 50% at 6 months and 61% at 12 months, with varying outcomes based on HCT type. Notably, 49 patients were not transplanted, primarily due to refractory disease, progression or relapse (57.1%). Autologous HCT candidates and physician referrals were correlated with higher transplant success compared to alloHCT candidates and patients who were not referred by a physician. CONCLUSION: Our pretransplant TC was feasible, facilitating access to HCT. Disease relapse posed a significant barrier. Enhancing timely physician referrals should be a focus for future efforts.

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.

Relation between task-related activity modulation and cortical inhibitory function in schizophrenia and healthy controls: a TMS-EEG study.

Schizophrenia has been associated with a reduced task-related modulation of cortical activity assessed through electroencephalography (EEG). However, to the best of our knowledge, no study so far has assessed the underpinnings of this decreased EEG modulation in schizophrenia. A possible substrate of these findings could be a decreased inhibitory function, a replicated finding in the field. In this pilot study, our aim was to explore the association between EEG modulation during a cognitive task and the inhibitory system function in vivo in a sample including healthy controls and patients with schizophrenia. We hypothesized that the replicated decreased task-related activity modulation during a cognitive task in schizophrenia would be related to a hypofunction of the inhibitory system. For this purpose, 27 healthy controls and 22 patients with schizophrenia (including 13 first episodes) performed a 3-condition auditory oddball task from which the spectral entropy modulation was calculated. In addition, cortical reactivity-as an index of the inhibitory function-was assessed by the administration of 75 monophasic transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex. Our results replicated the task-related cortical activity modulation deficit in schizophrenia patients. Moreover, schizophrenia patients showed higher cortical reactivity following transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex compared to healthy controls. Cortical reactivity was inversely associated with EEG modulation, supporting the idea that a hypofunction of the inhibitory system could hamper the task-related modulation of EEG activity.

Aproximación teórica a las incoherencias grafo-corporales en los procesos de recogida de muestras en pericia caligráfica / Theoretical approach to grapho-corporal inconsistencies in the processes of sample collection in handwriting expertise / Abordagem teórica das inconsistências grafo-corporais nos processos de coleta de amostras em perícias de caligrafia

La recogida de muestras indubitadas al momento de elaborar un cuerpo de escritura a una persona, es un proceso de suma importancia en la pericia caligráfica. Existen parámetros por los que se rigen los peritos calígrafos al confeccionar dicho cuerpo de escritura, que se centran de forma general en la posición del escribiente, la velocidad escritural, la espontaneidad, cantidad de muestras, entre otros. Sin embargo, el escribiente puede intentar alterarlo o manipularlo de diferentes formas, normalmente intentando dificultar la labor pericial. Así, existen diversos elementos que indican a un experto en grafística que se está intentando modificar la escritura o firma durante este proceso desde el punto de vista técnico. En este sentido, el objetivo de este estudio es resaltar la importancia de recopilar un cuerpo de escritura con todas las garantías para que pueda tratarse como un documento incuestionable, además de destacar la relevancia de que el perito tenga la capacidad de confirmar la sinceridad del escribiente, tanto mediante el análisis de su escritura (grafología) como a través del análisis de su comunicación no verbal en el momento de escribirlo. Se ha utilizado una metodología cualitativa, usando la técnica de información documental, mediante la recopilación de información para su estudio y análisis, así como de monografías, manuales de estudio y artículos científicos.

The collection of indubitable samples when a corpus of handwriting is taken from a person is an extremely important process in handwriting expertise. There are parameters by which handwriting experts are governed when making such a writing corpus, which generally focus on the position of the scribe, writing speed, spontaneity, number of samples, among others. However, the scribe may try to alter or manipulate it in different ways, usually in an attempt to hinder the expert's work. Thus, there are several elements that indicate to a handwriting expert that an attempt is being made to modify the handwriting or signature during this process from a technical point of view. In this sense, the aim of this study is to highlight the importance of compiling a handwriting corpus with all the guarantees that it can be treated as an unquestionable document, as well as highlighting the importance of the expert having the ability to confirm the scribe's sincerity, both through the analysis of their handwriting (graphology) and through the analysis of their non-verbal communication at the time of writing. A qualitative methodology has been used, using the documentary information technique, by means of the collection of information for study and analysis, as well as monographs, study manuals and scientific articles.

A coleta de provas indubitáveis quando um corpo de caligrafia é retirado de uma pessoa é um processo extremamente importante na perícia de caligrafia. Existem parâmetros pelos quais os peritos em caligrafia são regidos ao fazer esse corpo de escrita, que geralmente se concentram na posição do escriba, na velocidade da escrita, na espontaneidade, no número de amostras, entre outros. No entanto, o escrevente pode tentar alterá-lo ou manipulá-lo de diversas formas, geralmente na tentativa de dificultar o trabalho do perito. Assim, existem vários elementos que indicam ao perito em caligrafia que, do ponto de vista técnico, está havendo uma tentativa de modificar a caligrafia ou a assinatura durante esse processo. Nesse sentido, o objetivo deste estudo é destacar a importância de se compilar um corpo de caligrafia com todas as garantias de que possa ser tratado como um documento inquestionável, bem como destacar a importância de o perito ter a capacidade de confirmar a sinceridade do escrevente, tanto por meio da análise de sua caligrafia (grafologia) quanto por meio da análise de sua comunicação não verbal no momento da escrita. Foi utilizada uma metodologia qualitativa, com a técnica de informação documental, por meio da coleta de informações para estudo e análise, bem como de monografias, manuais de estudo e artigos científicos.

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.

ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces.

BACKGROUND AND OBJECTIVE: Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user's brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms. METHODS: ITACA is designed to be easy-to-use, flexible and attractive. Specifically, ITACA includes three different gamified training scenarios with a choice of five brain activity metrics as real-time feedback. Among them, novel metrics based on functional connectivity and network theory stand out. It is complemented with five different computerized versions of widespread cognitive assessment tests. To validate the proposed framework, a computational efficiency analysis and an NF training protocol focused on frontal-medial theta modulation were conducted. RESULTS: Efficiency analysis proved that all implemented metrics allow an optimal feedback update rate for conducting NF sessions. Furthermore, conducted NF protocol yielded results that support the use of ITACA in NF research studies. CONCLUSIONS: ITACA implements a wide variety of features for designing, conducting and evaluating NF studies with the goal of helping researchers expand the current state-of-the-art in NF training.

MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research.

BACKGROUND AND OBJECTIVE: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations. METHODS: We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages. RESULTS: MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility. CONCLUSIONS: MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.

Distinctive effects of executive dysfunction and loss of learning/memory abilities on resting-state brain activity.

Dementia is a syndrome characterised by cognitive impairments, with a loss of learning/memory abilities at the earlier stages and executive dysfunction at the later stages. However, recent studies have suggested that impairments in both learning/memory abilities and executive functioning might co-exist. Cognitive impairments have been primarily evaluated using neuropsychological assessments, such as the Mini-Mental State Examination (MMSE). Recently, neuroimaging techniques such as magnetoencephalography (MEG), which assess changes in resting-state brain activity, have also been used as biomarkers for cognitive impairment. However, it is unclear whether these changes reflect dysfunction in executive function as well as learning and memory. In this study, parameters from the MEG for brain activity, MMSE for learning/memory, and Frontal Assessment Battery (FAB) for executive function were compared within 207 individuals. Three MEG parameters were used as representatives of resting-state brain activity: median frequency, individual alpha frequency, and Shannon's spectral entropy. Regression analysis showed that median frequency was predicted by both the MMSE and FAB scores, while individual alpha frequency and Shannon's spectral entropy were predicted by MMSE and FAB scores, respectively. Our results indicate that MEG spectral parameters reflect both learning/memory and executive functions, supporting the utility of MEG as a biomarker of cognitive impairment.

Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task.

Objective.Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information.Approach.A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography data from patients with schizophrenia and cognitively healthy controls.Main results.The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations.Significance.Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.

Effect of segment length, sampling frequency, and imaging modality on the estimation of measures of brain meta-state activation: an MEG/EEG study.

The main objective of this study was to examine the influence that recording length, sampling frequency, and imaging modality have on the estimation and characterization of spontaneous brain meta-states during rest. To this end, a recently developed method of meta-state extraction and characterization was applied to a subset of 16 healthy elderly subjects from two independent electroencephalographic and magnetoencephalographic (EEG/MEG) databases. The recordings were segmented into the first 5, 10, 15, 20, 25, 30, 60 and 90-s of artifact-free activity and meta-states were extracted. Temporal activation sequence (TAS) complexity, which characterizes the complexity of the metastateactivation sequences during rest, was calculated. Then, its stability as a function of segment length, sampling frequency, and imaging modality was assessed. The results showed that, in general, the minimum segment length needed to fully characterize resting-state meta-state activation complexity ranged from 15 to 25 seconds. Moreover, it was found that the sampling frequency has a slight influence on the complexity measure, and that results were similar across EEG and MEG. The findings indicate that the proposed methodology can be applied to both EEG and MEG recordings and displays stable behavior with relatively short segments. However, methodological choices, such as sampling frequency, should be carefully considered.

High Frequential Resolution Networks: Considerations on a New Functional Brain Connectivity Framework.

Connectivity analyses are widely used to assess the interaction brain networks. This type of analyses is usually conducted considering the well-known classical frequency bands: delta, theta, alpha, beta, and gamma. However, this parcellation of the frequency content can bias the analyses, since it does not consider the between-subject variability or the particular idiosyncrasies of the connectivity patterns that occur within a band. In this study, we addressed these limitations by introducing the High Frequential Resolution Networks (HFRNs). HFRNs were constructed, using a narrow-bandwidth FIR bank filter of 1 Hz bandwidth, for two different connectivity metrics (Amplitude Envelope Correlation, AEC, and Phase Lag index, PLI) and for 3 different databases of MEG and EEG recordings. Results showed a noticeable similarity between the frequential evolution of PLI, AEC, and the Power Spectral Density (PSD) from MEG and EEG signals. Nonetheless, some technical remarks should be considered: (i) results at the gamma band should exclude the frequency range around 50 Hz due to abnormal connectivity patterns, consequence of the previously applied 50 Hz notch-filter; (ii) HFRNs patterns barely vary with the connection distance; and (iii) a low sampling frequency can exert a remarkable influence on HFRNs. To conclude, we proposed a new framework to perform connectivity analyses that allow to further analyze the frequency-based distribution of brain networks.

Influence of PICALM and CLU risk variants on beta EEG activity in Alzheimer's disease patients.

PICALM and CLU genes have been linked to alterations in brain biochemical processes that may have an impact on Alzheimer's disease (AD) development and neurophysiological dynamics. The aim of this study is to analyze the relationship between the electroencephalographic (EEG) activity and the PICALM and CLU alleles described as conferring risk or protective effects on AD patients and healthy controls. For this purpose, EEG activity was acquired from: 18 AD patients and 12 controls carrying risk alleles of both PICALM and CLU genes, and 35 AD patients and 12 controls carrying both protective alleles. Relative power (RP) in the conventional EEG frequency bands (delta, theta, alpha, beta, and gamma) was computed to quantify the brain activity at source level. In addition, spatial entropy (SE) was calculated in each band to characterize the regional distribution of the RP values throughout the brain. Statistically significant differences in global RP and SE at beta band (p-values < 0.05, Mann-Whitney U-test) were found between genotypes in the AD group. Furthermore, RP showed statistically significant differences in 58 cortical regions out of the 68 analyzed in AD. No statistically significant differences were found in the control group at any frequency band. Our results suggest that PICALM and CLU AD-inducing genotypes are involved in physiological processes related to disruption in beta power, which may be associated with physiological disturbances such as alterations in beta-amyloid and neurotransmitter metabolism.

The Menstrual Cycle Alters Resting-State Cortical Activity: A Magnetoencephalography Study.

Resting-state neural oscillations are used as biomarkers for functional diseases such as dementia, epilepsy, and stroke. However, accurate interpretation of clinical outcomes requires the identification and minimisation of potential confounding factors. While several studies have indicated that the menstrual cycle also alters brain activity, most of these studies were based on visual inspection rather than objective quantitative measures. In the present study, we aimed to clarify the effect of the menstrual cycle on spontaneous neural oscillations based on quantitative magnetoencephalography (MEG) parameters. Resting-state MEG activity was recorded from 25 healthy women with normal menstrual cycles. For each woman, resting-state brain activity was acquired twice using MEG: once during their menstrual period (MP) and once outside of this period (OP). Our results indicated that the median frequency and peak alpha frequency of the power spectrum were low, whereas Shannon spectral entropy was high, during the MP. Theta intensity within the right temporal cortex and right limbic system was significantly lower during the MP than during the OP. High gamma intensity in the left parietal cortex was also significantly lower during the MP than during the OP. Similar differences were also observed in the parietal and occipital regions between the proliferative (the late part of the follicular phase) and secretory phases (luteal phase). Our findings suggest that the menstrual cycle should be considered to ensure accurate interpretation of functional neuroimaging in clinical practice.

Exploring the Interactions Between Neurophysiology and Cognitive and Behavioral Changes Induced by a Non-pharmacological Treatment: A Network Approach.

Dementia due to Alzheimer's disease (AD) is a neurological syndrome which has an increasing impact on society, provoking behavioral, cognitive, and functional impairments. AD lacks an effective pharmacological intervention; thereby, non-pharmacological treatments (NPTs) play an important role, as they have been proven to ameliorate AD symptoms. Nevertheless, results associated with NPTs are patient-dependent, and new tools are needed to predict their outcome and to improve their effectiveness. In the present study, 19 patients with AD underwent an NPT for 83.1 ± 38.9 days (mean ± standard deviation). The NPT was a personalized intervention with physical, cognitive, and memory stimulation. The magnetoencephalographic activity was recorded at the beginning and at the end of the NPT to evaluate the neurophysiological state of each patient. Additionally, the cognitive (assessed by means of the Mini-Mental State Examination, MMSE) and behavioral (assessed in terms of the Dementia Behavior Disturbance Scale, DBD-13) status were collected before and after the NPT. We analyzed the interactions between cognitive, behavioral, and neurophysiological data by generating diverse association networks, able to intuitively characterize the relationships between variables of a different nature. Our results suggest that the NPT remarkably changed the structure of the association network, reinforcing the interactions between the DBD-13 and the neurophysiological parameters. We also found that the changes in cognition and behavior are related to the changes in spectral-based neurophysiological parameters. Furthermore, our results support the idea that MEG-derived parameters can predict NPT outcome; specifically, a lesser degree of AD neurophysiological alterations (i.e., neural oscillatory slowing, decreased variety of spectral components, and increased neural signal regularity) predicts a better NPT prognosis. This study provides deeper insights into the relationships between neurophysiology and both, cognitive and behavioral status, proving the potential of network-based methodology as a tool to further understand the complex interactions elicited by NPTs.

The association between carotid blood flow and resting-state brain activity in patients with cerebrovascular diseases.

Cerebral hypoperfusion impairs brain activity and leads to cognitive impairment. Left and right common carotid arteries (CCA) are the major source of cerebral blood supply. It remains unclear whether blood flow in both CCA contributes equally to brain activity. Here, CCA blood flow was evaluated using ultrasonography in 23 patients with cerebrovascular diseases. Resting-state brain activity and cognitive status were also assessed using magnetoencephalography and a cognitive subscale of the Functional Independence Measure, respectively, to explore the relationships between blood flow, functional brain activity, and cognitive status. Our findings indicated that there was an association between blood flow and resting-state brain activity, and between resting-state brain activity and cognitive status. However, blood flow was not significantly associated with cognitive status directly. Furthermore, blood velocity in the right CCA correlated with resting-state brain activity, but not with the resistance index. In contrast, the resistance index in the left CCA correlated with resting-state brain activity, but not with blood velocity. Our findings suggest that hypoperfusion is important in the right CCA, whereas cerebral microcirculation is important in the left CCA for brain activity. Hence, this asymmetry should be considered when designing appropriate therapeutic strategies.

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.

A new method to build multiplex networks using canonical correlation analysis for the characterization of the Alzheimer's disease continuum.

Objective. The aim of this study was to solve one of the current limitations for the characterization of the brain network in the Alzheimer's disease (AD) continuum. Nowadays, frequency-dependent approaches have reached contradictory results depending on the frequency band under study, tangling the possible clinical interpretations.Approach. To overcome this issue, we proposed a new method to build multiplex networks based on canonical correlation analysis (CCA). Our method determines two basis vectors using the source and electrode-level frequency-specific network parameters for a reference group, and then project the results for the rest of the groups into these hyperplanes to make them comparable. It was applied to: (i) synthetic signals generated with a Kuramoto-based model; and (ii) a resting-state electroencephalography (EEG) database formed by recordings from 51 cognitively healthy controls, 51 mild cognitive impairment subjects, 51 mild AD patients, 50 moderate AD patients, and 50 severe AD patients.Main results. Our results using synthetic signals showed that the interpretation of the proposed CCA-based multiplex parameters (multiplex strength, multiplex characteristic path length and multiplex clustering coefficient) can be analogous to their frequency-specific counterparts, as they displayed similar behaviors in terms of average connectivity, integration, and segregation. Findings using real EEG recordings revealed that dementia due to AD is characterized by a significant increase in average connectivity, and by a loss of integration and segregation.Significance. We can conclude that CCA can be used to build multiplex networks based from frequency-specific results, summarizing all the available information and avoiding the limitations of possible frequency-specific conflicts. Additionally, our method supposes a novel approach for the construction and analysis of multiplex networks during AD continuum.

Predicting the outcome of non-pharmacological treatment for patients with dementia-related mild cognitive impairment.

Dementia is a progressive cognitive syndrome, with few effective pharmacological treatments that can slow its progress. Hence, non-pharmacological treatments (NPTs) play an important role in improving patient symptoms and quality of life. Designing the optimal personalised NPT strategy relies on objectively and quantitatively predicting the treatment outcome. Magnetoencephalography (MEG) findings can reflect the cognitive status of patients with dementia, and thus potentially predict NPT outcome. In the present study, 16 participants with cognitive impairment underwent NPT for several months. Their cognitive performance was evaluated based on the Mini-Mental State Examination and the Alzheimer's Disease Assessment Scale - Cognitive at the beginning and end of the NPT period, while resting-state brain activity was evaluated using MEG during the NPT period. Our results showed that the spectral properties of MEG signals predicted the changes in cognitive performance scores. High frequency oscillatory intensity at the right superior frontal gyrus medial segment, opercular part of the inferior frontal gyrus, triangular part of the inferior frontal gyrus, post central gyrus, and angular gyrus predicted the changes in cognitive performance scores. Thus, resting-state brain activity may be a powerful tool in designing personalised NPT.