Auditory gamma-band entrainment enhances default mode network connectivity in dementia patients.

Dementia, and in particular Alzheimer's disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain's default mode network (DMN). The DMN operation is known to be impacted by dementia's progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity.

Aberrations in temporal dynamics of cognitive processing induced by Parkinson's disease and Levodopa.

The motor symptoms of Parkinson's disease (PD) have been shown to significantly improve by Levodopa. However, despite the widespread adoption of Levodopa as a standard pharmaceutical drug for the treatment of PD, cognitive impairments linked to PD do not show visible improvement with Levodopa treatment. Furthermore, the neuronal and network mechanisms behind the PD-induced cognitive impairments are not clearly understood. In this work, we aim to explain these cognitive impairments, as well as the ones exacerbated by Levodopa, through examining the differential dynamic patterns of the phase-amplitude coupling (PAC) during cognitive functions. EEG data recorded in an auditory oddball task performed by a cohort consisting of controls and a group of PD patients during both on and off periods of Levodopa treatment were analyzed to derive the temporal dynamics of the PAC across the brain. We observed distinguishing patterns in the PAC dynamics, as an indicator of information binding, which can explain the slower cognitive processing associated with PD in the form of a latency in the PAC peak time. Thus, considering the high-level connections between the hippocampus, the posterior and prefrontal cortices established through the dorsal and ventral striatum acting as a modulatory system, we posit that the primary issue with cognitive impairments of PD, as well as Levodopa's cognitive deficit side effects, can be attributed to the changes in temporal dynamics of dopamine release influencing the modulatory function of the striatum.

Brain electrophysiological recording during olfactory stimulation in mild cognitive impairment and Alzheimer disease patients: An EEG dataset.

The dataset presented in this article contains preprocessed cleaned electroencephalography (EEG) recording from 35 participants including 13 Alzheimer's disease (AD) patients, 7 amnestic mild cognitive impairment (aMCI) patients, and 15 healthy elderly. All participants performed the same olfactory task which consisted of 120 trials of 2 s olfactory stimulation and 8 s rest (no odorant). The olfactory stimulation consisted of rose and lemon odorants. Odor trials were presented randomly with a probability of 0.75 presenting lemon and 0.25 presenting rose. The impedance of the electrodes was kept under 15 KΩ during the experiment. The data was filtered from 0.5 to 40 Hz using a bandpass filter and epoched from 1 s pre-stimulus to 2 s post-stimulus. Artifacts related to eye blinks were removed by running independent component analysis (ICA) and the remaining noisy trials were identified by eye and removed from further analysis. Mini Mental State Examination (MMSE) test scores for all participants are also provided in the dataset. Olfactory dysfunction has been shown to be associated with neurodegenerative diseases such as dementia and Alzheimer's disease. Therefore, studying the response of the olfactory system may lead to identifying early biomarkers for related brain disorders.

Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning.

We report a label-free method for detection of the SARS-CoV-2 virus in nasopharyngeal swab samples without purification steps and multiplication of the target which simplifies and expedites the analysis process. The kit consists of a textile grid on which liquid crystals (LC) are deposited and the grid is placed in a crossed polarized microscopy. The swab samples are subsequently placed on the LCs. In the presence of a particular biomolecule, the direction of LCs changes locally based on the properties of the biomolecule and forms a particular pattern. As the swab samples are not perfectly purified, image processing and machine learning techniques are employed to detect the presence of specific molecules or quantify their concentrations in the medium. The method can differentiate negative and positive COVID-19 samples with an accuracy of 96% and also differentiate COVID-19 from influenza types A and B with an accuracy of 93%. The kit is portable, simple to manufacture, convenient to operate, cost effective, rapid and sensitive. The simplicity of the specimen processing, the speed of image acquisition, and fast diagnostic operations enable the deployment of the proposed technique for performing extensive on-spot screening of COVID-19 in public places.

Network synchronization deficits caused by dementia and Alzheimer's disease serve as topographical biomarkers: a pilot study.

Mild cognitive impairment (MCI) is known as an early stage of cognitive decline. Amnestic MCI (aMCI) is considered as the preliminary stage of dementia which may progress to Alzheimer's disease (AD). While some aMCI patients may stay in this condition for years, others might develop dementia associated with AD. Early detection of MCI allows for potential treatments to prevent or decelerate the process of developing dementia. Standard methods of diagnosing MCI and AD employ structural (imaging), behavioral (cognitive tests), and genetic or molecular (blood or CSF tests) techniques. Our study proposes network-level neural synchronization parameters as topographical markers for diagnosing aMCI and AD. We conducted a pilot study based on EEG data recorded during an olfactory task from a group of elderly participants consisting of healthy individuals and patients of aMCI and AD to assess the value of different indicators of network-level phase and amplitude synchronization in differentiating the three groups. Significant differences were observed in the percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups, and classifiers were developed to differentiate the three groups based on these parameters. The observed differences in these indicators of network-level functionality of the brain can help explain the underlying processes involved in aMCI and AD.

Spatiotemporal Signatures of Surprise Captured by Magnetoencephalography.

Surprise and social influence are linked through several neuropsychological mechanisms. By garnering attention, causing arousal, and motivating engagement, surprise provides a context for effective or durable social influence. Attention to a surprising event motivates the formation of an explanation or updating of models, while high arousal experiences due to surprise promote memory formation. They both encourage engagement with the surprising event through efforts aimed at understanding the situation. By affecting the behavior of the individual or a social group via setting an attractive engagement context, surprise plays an important role in shaping personal and social change. Surprise is an outcome of the brain's function in constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from the brain's predictions shaped by recent trends, distinct neural signals are generated to report this surprise. As a quantitative approach to modeling the generation of brain surprise, input stimuli containing surprising elements are employed in experiments such as oddball tasks during which brain activity is recorded. Although surprise has been well characterized in many studies, an information-theoretical model to describe and predict the surprise level of an external stimulus in the recorded MEG data has not been reported to date, and setting forth such a model is the main objective of this paper. Through mining trial-by-trial MEG data in an oddball task according to theoretical definitions of surprise, the proposed surprise decoding model employs the entire epoch of the brain response to a stimulus to measure surprise and assesses which collection of temporal/spatial components in the recorded data can provide optimal power for describing the brain's surprise. We considered three different theoretical formulations for surprise assuming the brain acts as an ideal observer that calculates transition probabilities to estimate the generative distribution of the input. We found that middle temporal components and the right and left fronto-central regions offer the strongest power for decoding surprise. Our findings provide a practical and rigorous method for measuring the brain's surprise, which can be employed in conjunction with behavioral data to evaluate the interactive and social effects of surprising events.

Behavior of olfactory-related frontal lobe oscillations in Alzheimer's disease and MCI: A pilot study.

Slow-gamma (35-45 Hz) phase synchronization and the coupling between slow-gamma and low-frequency theta oscillations (4-8 Hz) are closely related to memory retrieval and cognitive functions. In this pilot study, we assess the Phase Amplitude Coupling (PAC) between theta and slow-gamma oscillatory bands and the quality of synchronization in slow-gamma oscillations using Phase Locking Value (PLV) on EEG data from healthy individuals and patients diagnosed with amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD) during an oddball olfactory task. Our study indicates noticeable differences between the PLV and PAC values corresponding to olfactory stimulation in the three groups of participants. These differences can help explain the underlying processes involved in these cognitive disorders and the differences between aMCI and AD patients in performing cognitive tasks. Our study also proposes a diagnosis method for aMCI through comparing the brain's response characteristics during olfactory stimulation and rest. Early diagnosis of aMCI can potentially lead to its timely treatment and prevention from progression to AD.

Non-invasive auditory brain stimulation for gamma-band entrainment in dementia patients: An EEG dataset.

Gamma entrainment has been shown to enhance beta amyloid (Aß) uptake in mouse models of Alzheimer's disease (AD) as well as improve cognitive symptoms of dementia in both humans and mice. Similar improvements have been reported for both invasive and non-invasive brain stimulation in the gamma oscillatory band, with 40 Hz auditory and visual sensory stimulants employed in non-invasive approaches. Non-invasive stimulation techniques possess the clear advantage of not requiring surgical procedures and can hence be applicable to a wider set of patients. The dataset introduced here was acquired with the aim of examining the network-level mechanisms governing the production of the brain's oscillatory activity during non-invasive auditory gamma-band stimulation, and thereby helping to explain the reported therapeutic effects of entrainment in AD patients. Thirteen elderly participants with memory complaints whose conditions were diagnosed as normal aging (non-AD) or mild AD based on the standard criteria for the diagnosis of AD including the mini-mental state exam (MMSE) took part in data collection in which EEG signals were recorded during auditory stimulation of the brain. The data collection session consisted of an initial one-minute rest followed by an alternating set of six stimulation trials interleaved with five rest trials. During each stimulation trial, an auditory stimulant in the form of a 40 Hz chirp was presented to the participant. The collected data from all participants were preprocessed following the full pipeline of Makoto with the use of EEGLAB and posted as a dataset named: Auditory Gamma Entrainment at OpenNeuro repository. The data record for each participant includes the EEG signal represented in standard BIDS format for one-minute rest followed by the auditory task data. A copy of the source EEG data is also provided in .txt format. The dataset can be used to study the characteristics of brain oscillations during entrainment, as well as for studies on auditory perception, analysis of resting state potentials in dementia patients, comparison of auditory evoked potentials with resting state potentials, ERP, ERSP, and SSAVP analysis of auditory response in dementia patients, time series analysis of the stimulation and rest trials, and brain connectivity analysis in dementia patients.

Using distance on the Riemannian manifold to compare representations in brain and in models.

Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product and correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI data and considering a wide range of metrics, we show that the Riemannian distance is least susceptible to sampling bias, results in larger intra-subject reliability, and affords searchlight mapping with high sensitivity and specificity. Furthermore, we show that the Riemannian distance can be used for measuring multi-dimensional connectivity. This measure captures both univariate and multivariate connectivity and is also more sensitive to nonlinear regional interactions compared to the state-of-the-art measures. Applying our proposed metric to neural network representations of natural images, we demonstrate that it also possesses outstanding performance in quantifying similarity in models. Taken together, our results lend credence to the proposition that RSA should consider the manifold of the representational matrices to summarize response patterns in the brain and in models.

Transformer-based deep neural network language models for Alzheimer's disease risk assessment from targeted speech.

BACKGROUND: We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer's disease from the picture description test. METHODS: The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on top of the pre-trained deep language model. RESULTS: The models are evaluated on picture description test transcripts of the Pitt corpus, which contains data of 170 AD patients with 257 interviews and 99 healthy controls with 243 interviews. The large bidirectional encoder representations from transformers (BERTLarge) embedding with logistic regression classifier achieves classification accuracy of 88.08%, which improves the state-of-the-art by 2.48%. CONCLUSIONS: Using pre-trained language models can improve AD prediction. This not only solves the problem of lack of sufficiently large datasets, but also reduces the need for expert-defined features.

Olfactory response as a marker for Alzheimer's disease: Evidence from perceptual and frontal lobe oscillation coherence deficit.

High-frequency oscillations of the frontal cortex are involved in functions of the brain that fuse processed data from different sensory modules or bind them with elements stored in the memory. These oscillations also provide inhibitory connections to neural circuits that perform lower-level processes. Deficit in the performance of these oscillations has been examined as a marker for Alzheimer's disease (AD). Additionally, the neurodegenerative processes associated with AD, such as the deposition of amyloid-beta plaques, do not occur in a spatially homogeneous fashion and progress more prominently in the medial temporal lobe in the early stages of the disease. This region of the brain contains neural circuitry involved in olfactory perception. Several studies have suggested that olfactory deficit can be used as a marker for early diagnosis of AD. A quantitative assessment of the performance of the olfactory system can hence serve as a potential biomarker for Alzheimer's disease, offering a relatively convenient and inexpensive diagnosis method. This study examines the decline in the perception of olfactory stimuli and the deficit in the performance of high-frequency frontal oscillations in response to olfactory stimulation as markers for AD. Two measurement modalities are employed for assessing the olfactory performance: 1) An interactive smell identification test is used to sample the response to a sizable variety of odorants, and 2) Electroencephalography data are collected in an olfactory perception task with a pair of selected odorants in order to assess the connectivity of frontal cortex regions. Statistical analysis methods are used to assess the significance of selected features extracted from the recorded modalities as Alzheimer's biomarkers. Olfactory decline regressed to age in both healthy and mild AD groups are evaluated, and single- and multi-modal classifiers are also developed. The novel aspects of this study include: 1) Combining EEG response to olfactory stimulation with behavioral assessment of olfactory perception as a marker of AD, 2) Identification of odorants most significantly affected in mild AD patients, 3) Identification of odorants which are still adequately perceived by mild AD patients, 4) Analysis of the decline in the spatial coherence of different oscillatory bands in response to olfactory stimulation, and 5) Being the first study to quantitatively assess the performance of olfactory decline due to aging and AD in the Iranian population.

Discovering Human Activities from Binary Data in Smart Homes.

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual's daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual's patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods.

Decoding olfactory stimuli in EEG data using nonlinear features: A pilot study.

BACKGROUND: While decoding visual and auditory stimuli using recorded EEG signals has enjoyed significant attention in the past decades, decoding olfactory sensory input from EEG data remains a novelty. Recent interest in the brain's mechanisms of processing olfactory stimuli partly stems from the association of the olfactory system and its deficit with neurodegenerative diseases. NEW METHODS: An olfactory stimulus decoder using features that represent nonlinear behavior content in the recorded EEG data has been introduced for classifying 4 olfactory stimuli in 5 healthy male subjects. RESULTS: We show that by using nonlinear and chaotic features, a subject-specific classifier can be developed for identifying the odors that subjects perceive with an average accuracy of 96.71 % and 88.79 % in the eyes-open and eyes-closed conditions, respectively. We also employ our methodology in building cross-subject classifiers: once for identifying pleasant and unpleasant odors, and once for the classification of all four olfactory stimuli. The accuracy of our proposed methodology is 91.7 % and 82.1 % in the eyes-open and eyes-closed conditions, for the odor pleasantness classification. The accuracy of cross-subject classification of all odors is 64.3 % and 54.8 % for the eyes-open and eyes-closed conditions, respectively, which is well above chance level. COMPARISON WITH EXISTING METHODS: Comparison with similar studies reveals that our proposed method outperforms other classification schemes in terms of accuracy. CONCLUSIONS: The results can help researchers design more accurate classifiers for the detection of perceived odors using EEG signals. These results can contribute to gaining more insight into the brain's process of odor perception.

Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks.

Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an encoding model, we investigated the reverse model: if the stimuli's surprise is assumed as the cause of the observer's surprise, it must be possible to decode the surprise of each stimulus, for every single subject, given only their neural responses, i.e. to tell how unexpected a specific stimulus has been for them. Employing machine learning tools, we developed a surprise decoding model for binary oddball tasks. We constructed our model using the ideal observer proposed by Meyniel et al. in 2016, and applied it to three datasets, one with visual, one with auditory, and one with both visual and auditory stimuli. We demonstrated that our decoding model performs very well for both of the sensory modalities with or without the presence of the subject's motor response.

Human detection in occluded scenes through optically inspired multi-camera image fusion.

In this paper, a novel approach for foreground extraction has been proposed based on a popular three-dimensional imaging technique in optics, called integral imaging. In this approach, multiple viewpoint images captured from a three-dimensional scene are used to extract range information of the scene and effectively extract an object or a person, even in the presence of heavy occlusion. The algorithm consists of two parts: depth estimation and reconstruction of the targeted object at the estimated depth distance. Further processing of the resulting reconstructed image can lead to the detection of a face or a pedestrian in the scene, which may not otherwise be detectable due to partial occlusion in each of the views. The validity of our approach has been demonstrated by experimental results in different scenarios.

Human mobility monitoring in very low resolution visual sensor network.

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 × 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

User behavior shift detection in ambient assisted living environments.

Identifying users' frequent behaviors is considered a key step to achieving real, intelligent environments that support people in their daily lives. These patterns can be used in many different applications. An algorithm that compares current behaviors of users with previously discovered frequent behaviors has been developed. In addition, it identifies the differences between both behaviors. Identified shifts can be used not only to adapt frequent behaviors, but also shifts may indicate initial signs of some diseases linked to behavioral modifications, such as depression or Alzheimer's. The algorithm was validated using datasets collected from smart apartments where five different ADLs (Activities of Daily Living) were recognized. It was able to identify all shifts from frequent behaviors, as well as identifying necessary modifications in all cases.

Using Spatio-Temporal Interest Points (STIP) for myoclonic jerk detection in nocturnal video.

In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition.We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.