Explore the vast range of titles available.

The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer’s disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.

In the original publication [...]

Background/Objectives: The aim of this investigation consisted of evaluating if the prevalence of anosognosia in right-brain-damaged patients is greater for tasks in which the right hemisphere plays a dominant role and if this prevalence is at least in part due to automatic processing mechanisms typical of this hemisphere. Methods: We assessed defective awareness of person-recognition disorders in 14 patients with the right variant (rv-SD) and 15 with the left variant (lv-SD) of Semantic Dementia. A battery exploring person-recognition disorders through familiarity judgement of faces, voices and names was applied. In patients with pathological performance in one of these modalities, anosognosia was assessed comparing the patients’ subjective judgment to the objective result of their performance (objective evaluation) and to the subjective judgment given by an informed caregiver (external comparison). Results: In the comparison between subjective awareness and objective scores in the various person-recognition modalities, only anosognosia for face recognition disorders was significantly more frequent of in patients with rv-SD. When compared to their caregivers, subjects with rv-SD were significantly less aware than caregivers of their difficulties only on face recognition. On the contrary, patients with a lv-SD showed a greater (non-significant) trend to be unaware of their name recognition deficit. Conclusions: These data show that the prevalence of anosognosia in right-brain-damaged patients is greater for face recognition in which the right hemisphere plays a dominant role and that this prevalence is at least in part due to automatic processing mechanisms (evocation of familiarity feelings) typical of this hemisphere.

Neurodegenerative diseases severely impact the life of millions of patients worldwide, and their occurrence is more and more increasing proportionally to longer life expectancy. Electroencephalography has become an important diagnostic tool for these diseases, due to its relatively simple procedure, but it requires analyzing a large number of data, often carrying a small fraction of informative content. For this reason, machine learning tools have gained a considerable relevance as an aid to classify potential signs of a specific disease, especially in its early stages, when treatments can be more effective. In this work, long short-term memory-based neural networks with different numbers of units were properly designed and trained after accurate data pre-processing, in order to perform a multi-class detection. To this end, a custom dataset of EEG recordings from subjects affected by five neurodegenerative diseases (Alzheimer’s disease, frontotemporal dementia, dementia with Lewy bodies, progressive supranuclear palsy, and vascular dementia) was acquired. Experimental results show that an accuracy up to 98% was achieved with data belonging to different classes of disease, up to six including the control group, while not requiring particularly heavy computational resources.

In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extract functional brain responses from EEG signals, prolonged and repeated stimuli are needed because of the artifacts generated in recordings which adversely impact the stimulus-response analysis. To mitigate the artifact effect, correlation analysis (CA) methods are applied in the literature, where the predominant approaches focus on enhancing stimulus-response correlations through the use of linear analysis methods like canonical correlation analysis (CCA). This paper introduces a novel CA framework based on a neural network with a loss function specifically designed to maximize correlation between EEG and speech stimuli. Compared with other deep learning CA approaches (DCCAs) in the literature, this framework introduces a single multilayer perceptron (MLP) network instead of two networks for each stimulus. To validate the proposed approach, a comparison with linear CCA (LCCA) and DCCA was performed, using a dataset containing the EEG traces of subjects listening to speech stimuli. The experimental results show that the proposed method improves the overall Pearson correlation by 10.56% compared with the state-of-the-art DCCA method.

There is strong evidence that homocysteine is a risk factor not only for cerebrovascular diseases but also for degenerative dementias. A recent consensus statement renewed the importance and the role of high levels of homocysteine in cognitive decline in several forms of degenerative dementia, such as Alzheimer’s disease. Although the molecular mechanisms by which homocysteine causes cell dysfunction are known, both the impact of homocysteine on specific cognitive functions and the relationship between homocysteine level and non-Alzheimer dementias have been poorly investigated. Most of the studies addressing the impact of hyperhomocysteinemia on dementias have not examined the profile of performance across different cognitive domains, and have only relied on screening tests, which provide a very general and coarse-grained picture of the cognitive status of the patients. Yet, trying to understand whether hyperhomocysteinemia is associated with the impairment of specific cognitive functions would be crucial, as it would be, in parallel, learning whether some brain circuits are particularly susceptible to the damage caused by hyperhomocysteinemia. These steps would allow one to (i) understand the actual role of homocysteine in the pathogenesis of cognitive decline and (ii) improve the diagnostic accuracy, differential diagnosis and prognostic implications. This review is aimed at exploring and revising the state of the art of these two strictly related domains. Suggestions for future research are provided.

The semantic variant of primary progressive aphasia (svPPA), known also as “semantic dementia (SD),” is a neurodegenerative disorder that pertains to the frontotemporal lobar degeneration clinical syndromes. There is currently no approved pharmacological therapy for all frontotemporal dementia variants. Transcranial direct current stimulation (tDCS) is a promising non-invasive brain stimulation technique capable of modulating cortical excitability through a sub-threshold shift in neuronal resting potential. This technique has previously been applied as adjunct treatment in Alzheimer’s disease, while data for frontotemporal dementia are controversial. In this scoped review, we summarize and critically appraise the currently available evidence regarding the use of tDCS for improving performance in naming and/or matching tasks in patients with svPPA. Clinical trials addressing this topic were identified through MEDLINE (accessed by PubMed) and Web of Science, as of November 2022, week 3. Clinical trials have been unable to show a significant benefit of tDCS in enhancing semantic performance in svPPA patients. The heterogeneity of the studies available in the literature might be a possible explanation. Nevertheless, the results of these studies are promising and may offer valuable insights into methodological differences and overlaps, raising interest among researchers in identifying new non-pharmacological strategies for treating svPPA patients. Further studies are therefore warranted to investigate the potential therapeutic role of tDCS in svPPA.

Phospholipids of the erythrocyte plasma membrane are mostly composed of phosphatidylcholine, sphingomyelin, phosphatidylserine, phosphatidylethanolamine, and a composition of fatty acids which reflects that of the brain (Hedue et al., 2003). [...]we also measured erythrocyte membrane fluidity in MCI and AD patients and controls, observing a significative decrease of fluidity in MCI subjects and, interestingly, no differences in AD patients compared to controls (Vignini et al., 2019). (2004) reported a negative correlation between membrane fluidity and plasma oxidative stress, we believe that the high oxidative stress in MCI patients might be responsible for lipid peroxidation with the consequent membrane rigidity. [...]many studies have shown that cerebrovascular disease (CeVD) is related with these two conditions; in particular, recent findings provide the evidence that CeVD can affect the early structural neural network degeneration (Vipin et al., 2018). A reduced blood flow implies a decreased delivery of nutrients and oxygen, compromising the energetic metabolism of the cell. Since there is no effective pharmacological treatment for hindering the progression of these diseases so far, natural products have become a significant possibility in neurodegenerative research.

Recent electrophysiological investigations of the auditory system in primates along with functional neuroimaging studies of auditory perception in humans have suggested there are two pathways arising from the primary auditory cortex. In the primate brain, a ‘ventral’ pathway is thought to project anteriorly from the primary auditory cortex to prefrontal areas along the superior temporal gyrus while a separate ‘dorsal’ route connects these areas posteriorly via the inferior parietal lobe. We use diffusion MRI tractography, a noninvasive technique based on diffusion-weighted MRI, to investigate the possibility of a similar pattern of connectivity in the human brain for the first time. The dorsal pathway from Wernicke's area to Broca's area is shown to include the arcuate fasciculus and connectivity to Brodmann area 40, lateral superior temporal gyrus (LSTG), and lateral middle temporal gyrus. A ventral route between Wernicke's area and Broca's area is demonstrated that connects via the external capsule/uncinate fasciculus and the medial superior temporal gyrus. Ventral connections are also observed in the lateral superior and middle temporal gyri. The connections are stronger in the dominant hemisphere, in agreement with previous studies of functional lateralization of auditory-language processing.

IntroductionProgressive supranuclear palsy (PSP) and behavioural variant frontotemporal dementia – (bv-FTD) share common neuropsychological features except for online monitoring awareness. Therefore, the aim of our study is to explore if this assessment could be used in standard clinical practice.Materials and methodsWe retrospectively analyse 93 subjects (27 FTD, 25 PSP, 42 healthy controls). Neuropsychological and instrumental examinations were performed for each patient.ResultsFTD patients made fewer self-corrections than PSP patients despite a similar number of total errors. We also performed ROC curves: the area under the curve (AUC) is 0.79. A model for a logistic regression was also developed: the only significant predictor is the number of self-corrections (p = 0.004 β = 1244).Discussion and conclusionsIn conclusion, our findings show online awareness is more compromised in FTD patients than in PSP patients. This difference could be useful for making a differential diagnosis between the two diseases: for each extra point in number of self-corrections the probability of suffering from PSP increases by about three and a half times (OR 3.47).