Effects of cervical transcutaneous spinal direct current stimulation on spinal excitability.

OBJECTIVE: To investigate the effects of transcutaneous spinal direct current stimulation (tsDCS) on spinal cord excitability using neurophysiological methods. METHODS: Spinal cord motoneuron excitability was assessed using various neurophysiological techniques in a sham-controlled randomized experiment, which involved delivering 2 mA tsDCS and testing four different montages. Transcranial magnetic stimulation (TMS), F-waves to supramaximal ulnar nerve stimulation and somatosensory evoked potentials to upper limb nerves stimulation were measured in the participants with the electrode configuration that yielded the greatest effect, for a total of about 18 min. 18 young volunteers were recruited. RESULTS: Among the tested ones, the most promising tsDCS montage was the one with the anode placed on the 7th cervical spinous process and the cathode on the glottis. With this configuration, a significant enhancement of motor responses in the hand muscles to TMS of the contralateral hand motor area was observed during tsDCS (p<0.00001), reaching a plateau after 6 min. This facilitation rapidly declined within a few minutes after the tsDCS was stopped. CONCLUSION: Results of the different techniques suggest a possible contribution to facilitatory neuromodulation of the motoneurons at the cervical spine level. SIGNIFICANCE: The occurrence of enhanced excitability after tsDCS suggests potential application in individuals with partial corticospinal fiber impairment affecting hand motor function.

The impact of virtual reality and distractors on attentional processes: insights from EEG.

Virtual reality (VR) allows to create controlled scenarios in which the quantity of stimuli can be modulated, as happen in real-life, where humans are subjected to various multisensory-often overlapping-stimuli. The present research aimed to study changes in attentional processes within an auditory oddball paradigm during a virtual exploration, while varying the amount of distractors. Twenty healthy volunteers underwent electroencephalography (EEG) during three different experimental conditions: an auditory oddball without VR (No-VR condition), an auditory oddball during VR exploration without distractors (VR-Empty condition), and an auditory oddball during VR exploration with a high level of distractors (VR-Full condition). Event-related potentials (ERPs) were computed averaging epochs of EEGs and analyzing peaks at 100 ms (N100) and 300 ms (P300) latencies. Results showed modulation of N100 amplitude in Fz and of P300 amplitude in Pz. Statistically significant differences in latency were observed only for P300 where the latency results delayed from the No-VR to VR-Full. The scalp topography revealed for P100 no significant differences between frequent and rare stimuli in either the No-VR and VR-Empty conditions. However, significant results were found in N100 in VR-Full condition. For P300, results showed differences between frequent and rare stimuli, in every condition. However, this difference is gradually less widespread from No-VR condition to the VR-Full. The emerging integration of VR with EEG may have important implications for studying brain attentional processing.

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

Exploring the complexity of EEG patterns in Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.

Long-lasting improvements in episodic memory among subjects with mild cognitive impairment who received transcranial direct current stimulation combined with cognitive treatment and telerehabilitation: a multicentre, randomized, active-controlled study.

Background: In recent years, an increasing number of studies have examined the potential efficacy of cognitive training procedures in individuals with normal ageing and mild cognitive impairment (MCI). Objective: The aims of this study were to (i) evaluate the efficacy of the cognitive Virtual Reality Rehabilitation System (VRRS) combined with anodal transcranial direct current stimulation (tDCS) applied to the left dorsolateral prefrontal cortex compared to placebo tDCS stimulation combined with VRRS and (ii) to determine how to prolong the beneficial effects of the treatment. A total of 109 subjects with MCI were assigned to 1 of 5 study groups in a randomized controlled trial design: (a) face-to-face (FTF) VRRS during anodal tDCS followed by cognitive telerehabilitation (TR) (clinic-atDCS-VRRS+Tele@H-VRRS); (b) FTF VRRS during placebo tDCS followed by TR (clinic-ptDCS-VRRS+Tele@H-VRRS); (c) FTF VRRS followed by cognitive TR (clinic-VRRS+Tele@H-VRRS); (d) FTF VRRS followed by at-home unstructured cognitive stimulation (clinic-VRRS+@H-UCS); and (e) FTF cognitive treatment as usual (clinic-TAU). Results: An improvement in episodic memory was observed after the end of clinic-atDCS-VRRS (p < 0.001). We found no enhancement in episodic memory after clinic-ptDCS-VRRS or after clinic-TAU.Moreover, the combined treatment led to prolonged beneficial effects (clinic-atDCS-VRRS+Tele@H-VRRS vs. clinic-ptDCS-VRRS+Tele@H-VRRS: p = 0.047; clinic-atDCS-VRRS+Tele@H-VRRS vs. clinic-VRRS+Tele@H-VRRS: p = 0.06). Discussion: The present study provides preliminary evidence supporting the use of individualized VRRS combined with anodal tDCS and cognitive telerehabilitation for cognitive rehabilitation. Clinical trial registration: https://clinicaltrials.gov/study/NCT03486704?term=NCT03486704&rank=1, NCT03486704.

Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach.

INTRODUCTION: Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seems to provide a better understanding of the pathological mechanisms underlying the onset of dementia. The purpose of this review was to discuss the current ML application in the field of neuropsychology and electrophysiology, exploring its results in both prediction and diagnosis for different forms of dementia, such as Alzheimer's disease (AD), Vascular Dementia (VaD), Dementia with Lewy bodies (DLB), and Frontotemporal Dementia (FTD). METHODS: Main ML-based papers focusing on neuropsychological assessments and electroencephalogram (EEG) studies were analyzed for each type of dementia. RESULTS: An accuracy ranging between 70 % and 90 % or even more was observed in all neurophysiological and electrophysiological results trained by ML. Among all forms of dementia, the most significant findings were observed for AD. Relevant results were mostly related to diagnosis rather than prediction, because of the lack of longitudinal studies with appropriate follow-up duration. However, it remains unclear which ML algorithm performs better in diagnosing or predicting dementia. CONCLUSIONS: Neuropsychological and electrophysiological measurements, together with ML analysis, may be considered as reliable instruments for early detection of dementia.

Development of a prediction model of conversion to Alzheimer's disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project.

BACKGROUND: In recent years, significant efforts have been directed towards the research and development of disease-modifying therapies for dementia. These drugs focus on prodromal (mild cognitive impairment, MCI) and/or early stages of Alzheimer's disease (AD). Literature evidence indicates that a considerable proportion of individuals with MCI do not progress to dementia. Identifying individuals at higher risk of developing dementia is essential for appropriate management, including the prescription of new disease-modifying therapies expected to become available in clinical practice in the near future. METHODS: The ongoing INTERCEPTOR study is a multicenter, longitudinal, interventional, non-therapeutic cohort study designed to enroll 500 individuals with MCI aged 50-85 years. The primary aim is to identify a biomarker or a set of biomarkers able to accurately predict the conversion from MCI to AD dementia within 3 years of follow-up. The biomarkers investigated in this study are neuropsychological tests (mini-mental state examination (MMSE) and delayed free recall), brain glucose metabolism ([18F]FDG-PET), MRI volumetry of the hippocampus, EEG brain connectivity, cerebrospinal fluid (CSF) markers (p-tau, t-tau, Aß1-42, Aß1-42/1-40 ratio, Aß1-42/p-Tau ratio) and APOE genotype. The baseline visit includes a full cognitive and neuropsychological evaluation, as well as the collection of clinical and socio-demographic information. Prognostic models will be developed using Cox regression, incorporating individual characteristics and biomarkers through stepwise selection. Model performance will be evaluated in terms of discrimination and calibration and subjected to internal validation using the bootstrapping procedure. The final model will be visually represented as a nomogram. DISCUSSION: This paper contains a detailed description of the statistical analysis plan to ensure the reproducibility and transparency of the analysis. The prognostic model developed in this study aims to identify the population with MCI at higher risk of developing AD dementia, potentially eligible for drug prescriptions. The nomogram could provide a valuable tool for clinicians for risk stratification and early treatment decisions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03834402. Registered on February 8, 2019.

Associations Between Free and Cued Selective Reminding Test and Cerebrospinal Fluid Biomarkers in Amnestic Mild Cognitive Impairment.

Background: The Free and Cued Selective Reminding Test (FCSRT), assessing verbal episodic memory with controlled learning and semantic cueing, has been recommended for detecting the genuine encoding and storage deficits characterizing AD-related memory disorders. Objective: The present study aims at investigating the ability of FCSRT in predicting cerebrospinal fluid (CSF) evidence of amyloid-ß positivity in subjects with amnestic mild cognitive impairment (aMCI) and exploring its associations with amyloidopathy, tauopathy and neurodegeneration biomarkers. Methods: 120 aMCI subjects underwent comprehensive neurological and neuropsychological examinations, including the FCSRT assessment, and CSF collection; CSF Aß42/40 ratio, p-tau181, and total-tau quantification were conducted by an automated CLEIA method on Lumipulse G1200. Based on the Aß42/40 ratio value, subjects were classified as either A+ or A-. Results: All FCSRT subitem scores were significantly lower in A+ group and significantly predicted the amyloid-ß status, with Immediate Total Recall (ITR) being the best predictor. No significant correlations were found between FCSRT and CSF biomarkers in the A- aMCI group, while in the A+ aMCI group, all FCSRT subitem scores were negatively correlated with CSF p-tau181 and total-tau, but not with the Aß42/40 ratio. Conclusions: FCSRT confirms its validity as a tool for the diagnosis of AD, being able to predict the presence of amyloid-ß deposition with high specificity. The associations between FCSRT subitem scores and CSF p-tau-181 and total-tau levels in aMCI due to AD could further encourage the clinical use of this simple and cost-effective test in the evaluation of individuals with aMCI.

Small World derived index to distinguish Alzheimer's type dementia and healthy subjects.

BACKGROUND: This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. METHODS: Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. RESULTS: Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. CONCLUSIONS: These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.

EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.

In recent decades, entropy measures have gained prominence in neuroscience due to the nonlinear behaviour exhibited by neural systems. This rationale justifies the application of methods from the theory of nonlinear dynamics to cerebral activity, aiming to detect and quantify its variability more effectively. In the context of electroencephalogram (EEG) signals, entropy analysis offers valuable insights into the complexity and irregularity of electromagnetic brain activity. By moving beyond linear analyses, entropy measures provide a deeper understanding of neural dynamics, particularly pertinent in elucidating the mechanisms underlying brain aging and various acute/chronic-progressive neurological disorders. Indeed, various pathologies can disrupt nonlinear structuring in neural activity, which may remain undetected by linear methods such as power spectral analysis. Consequently, the utilization of nonlinear tools, including entropy analysis, becomes crucial for capturing these alterations. To establish the relevance of entropy analysis and its potential to discern between physiological and pathological conditions, this review discusses its diverse applications in studying healthy brain aging and neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Various entropy parameters, such as approximate entropy (ApEn), sample entropy (SampEn), multiscale entropy (MSE), and permutation entropy (PermEn), are analysed within this context. By quantifying the complexity and irregularity of EEG signals, entropy analysis may serve as a valuable biomarker for early diagnosis, treatment monitoring, and disease management. Such insights offer clinicians crucial information for devising personalized treatment and rehabilitation plans tailored to individual patients.

Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice.

Stroke is one of the leading causes of disability worldwide. There are many different rehabilitation approaches aimed at improving clinical outcomes for stroke survivors. One of the latest therapeutic techniques is the non-invasive brain stimulation. Among non-invasive brain stimulation, transcranial direct current stimulation has shown promising results in enhancing motor and cognitive recovery both in animal models of stroke and stroke survivors. In this framework, one of the most innovative methods is the bihemispheric transcranial direct current stimulation that simultaneously increases excitability in one hemisphere and decreases excitability in the contralateral one. As bihemispheric transcranial direct current stimulation can create a more balanced modulation of brain activity, this approach may be particularly useful in counteracting imbalanced brain activity, such as in stroke. Given these premises, the aim of the current study has been to explore the recovery after stroke in mice that underwent a bihemispheric transcranial direct current stimulation treatment, by recording their electric brain activity with local field potential and by measuring behavioural outcomes of Grip Strength test. An innovative parameter that explores the complexity of signals, namely the Entropy, recently adopted to describe brain activity in physiopathological states, was evaluated to analyse local field potential data. Results showed that stroke mice had higher values of Entropy compared to healthy mice, indicating an increase in brain complexity and signal disorder due to the stroke. Additionally, the bihemispheric transcranial direct current stimulation reduced Entropy in both healthy and stroke mice compared to sham stimulated mice, with a greater effect in stroke mice. Moreover, correlation analysis showed a negative correlation between Entropy and Grip Strength values, indicating that higher Entropy values resulted in lower Grip Strength engagement. Concluding, the current evidence suggests that the Entropy index of brain complexity characterizes stroke pathology and recovery. Together with this, bihemispheric transcranial direct current stimulation can modulate brain rhythms in animal models of stroke, providing potentially new avenues for rehabilitation in humans.

The role of the sleep K-complex on the conversion from mild cognitive impairment to Alzheimer's disease.

The present literature points to an alteration of the human K-complex during non-rapid eye movement sleep in Alzheimer's disease. Nevertheless, the few findings on the K-complex changes in mild cognitive impairment and their possible predictive role on the Alzheimer's disease conversion show mixed findings, lack of replication, and a main interest for the frontal region. The aim of the present study was to assess K-complex measures in amnesic mild cognitive impairment subsequently converted in Alzheimer's disease over different cortical regions, comparing them with healthy controls and stable amnesic mild cognitive impairment. We assessed baseline K-complex density, amplitude, area under the curve and overnight changes in frontal, central and parietal midline derivations of 12 amnesic mild cognitive impairment subsequently converted in Alzheimer's disease, 12 stable amnesic mild cognitive impairment and 12 healthy controls. We also assessed delta electroencephalogram power, to determine if K-complex alterations in amnesic mild cognitive impairment occur with modification of the electroencephalogram power in the frequency range of the slow-wave activity. We found a reduced parietal K-complex density in amnesic mild cognitive impairment subsequently converted in Alzheimer's disease compared with stable amnesic mild cognitive impairment and healthy controls, without changes in K-complex morphology and overnight modulation. Both amnesic mild cognitive impairment groups showed decreased slow-wave sleep percentage compared with healthy controls. No differences between groups were observed in slow-wave activity power. Our findings suggest that K-complex alterations in mild cognitive impairment may be observed earlier in parietal regions, likely mirroring the topographical progression of Alzheimer's disease-related brain pathology, and express a frontal predominance only in a full-blown phase of Alzheimer's disease. Consistently with previous results, such K-complex modification occurs in the absence of significant electroencephalogram power changes in the slow oscillations range.

Neuroplasticity in levodopa-induced dyskinesias: An overview on pathophysiology and therapeutic targets.

Levodopa-induced dyskinesias (LIDs) are a common complication in patients with Parkinson's disease (PD). A complex cascade of electrophysiological and molecular events that induce aberrant plasticity in the cortico-basal ganglia system plays a key role in the pathophysiology of LIDs. In the striatum, multiple neurotransmitters regulate the different forms of physiological synaptic plasticity to provide it in a bidirectional and Hebbian manner. In PD, impairment of both long-term potentiation (LTP) and long-term depression (LTD) progresses with disease and dopaminergic denervation of striatum. The altered balance between LTP and LTD processes leads to unidirectional changes in plasticity that cause network dysregulation and the development of involuntary movements. These alterations have been documented, in both experimental models and PD patients, not only in deep brain structures but also at motor cortex. Invasive and non-invasive neuromodulation treatments, as deep brain stimulation, transcranial magnetic stimulation, or transcranial direct current stimulation, may provide strategies to modulate the aberrant plasticity in the cortico-basal ganglia network of patients affected by LIDs, thus restoring normal neurophysiological functioning and treating dyskinesias. In this review, we discuss the evidence for neuroplasticity impairment in experimental PD models and in patients affected by LIDs, and potential neuromodulation strategies that may modulate aberrant plasticity.

Neuropsychological predictors of conversion from mild cognitive impairment to dementia at different timepoints.

INTRODUCTION: Impairment of episodic memory is largely considered the main cognitive marker of prodromic Alzheimer's disease (AD). Nevertheless, the neuropathological process in AD starts several years before and, apart from biomarkers well defined in the Amyloid (A), Tauopathy (T), Neurodegeneration (N) framework, early clinical and neuropsychological markers able to detect mild cognitive impairment (MCI) due to AD before the appearance of memory disorders are lacking in clinical practice. Investigations on semantic memory have shown promising results in providing an earlier marker of dementia in MCI patients. METHODS: A total of 253 MCI subjects were followed up every 6 months for 6 years-186 converted to dementia and 67 remained stable at the sixth year of follow-up. Twenty-seven patients progressed in the first 2 years (fast converters), 107 in the third to fourth year (intermediate converters), and 51 after the fourth year of follow-up (slow converters). RESULTS: Stable MCI subjects performed better than fast decliners in Mini-Mental State Examination (MMSE), several long-term memory scores, and category verbal fluency test (CFT); stable and intermediate converters differ only in MMSE and CFT tests; and stable and slow converters differ only in MMSE and phonological/semantic discrepancy score. CONCLUSION: Early impairment of semantic memory could predict the evolution to AD before the onset of episodic memory disorders, and the discrepancy between phonological and semantic verbal fluency could be able to detect this impairment in advance in respect of simple CFT tests. The assessment of different aspects of semantic memory and its degradation could represent an early cognitive marker to intercept MCI due to AD in clinical practice.

Cerebrospinal fluid neurofilament light chain and total-tau as biomarkers of neurodegeneration in Alzheimer's disease and frontotemporal dementia.

INTRODUCTION: CSF Neurofilament light chain(NfL) is a promising biomarker of neurodegeneration, but its utility in discriminating between Alzheimer's disease(AD) and frontotemporal dementia(FTD) is limited. METHODS: 105 patients with clinical-biological diagnosis of mild cognitive impairment(MCI) due to AD (N = 72) or clinical diagnosis of FTD (N = 33) underwent neuropsychological assessment and CSF Aß42/40, p-tau181, total-tau and NfL quantification. Group comparisons, correlations between continuous variables and ROC curve analysis were carried out to assess NfL role in discriminating between MCI due to AD and FTD, exploring the associations between NfL, ATN biomarkers and neuropsychological measures. RESULTS: NfL levels were significantly lower in the AD group, while levels of total-tau were higher. In the FTD group, significant correlations were found between NfL, p-tau181 and total-tau, and between NfL and cognitive performances. In the AD group, NfL levels were directly correlated with total-tau and p-tau181; Aß42/40 ratio was inversely correlated with total-tau and p-tau181, but not with NfL. Moreover, p-tau181 and t-tau levels were found to be associated with episodic memory and lexical-semantic impairment. Total-tau/NfL ratio differentiated prodromal-AD from FTD with an AUC of 0.951, higher than the individual measures. DISCUSSION & CONCLUSIONS: The results support that NfL and total-tau levels reflect distinct pathophysiological neurodegeneration mechanisms, independent and dependent of Aß pathology, respectively, Combining them may enhance both markers reliability, their ratio showing high accuracy in distinguishing MCI due to AD from FTD. Moreover, our results revealed associations between NfL and disease severity in FTD and between tauopathy and episodic memory and lexical-semantic impairment in prodromal-AD.

Informal caregiver's socio demographic profile for community-dwelling women and men with mild to moderate Alzheimer's disease, compliance, and satisfaction to treatment: A post-hoc analysis of the AXEPT study.

In this post-hoc analysis of the AXEPT study, 855 patients were analyzed, 544 (63.6%) females. The mean (± SD) MMSE score in women vs men was 20.8 ± 2.6 vs. 21.2 ± 2.5; p = 0.0087, and women were more likely affected by psychiatric disorders (n = 76, 14.0% women vs. n = 21, 6.8% men; p = 0.0015). Men were mainly assisted by their wives (n = 207, 66.6%), women mainly by their daughters (n = 243, 44.7%) and only in a minority of cases by their husbands (n = 92, 16.9%). Women less frequently cohabited with their caregivers than men (n = 233, 43.1% vs. n = 240, 77.9%, p < 0.0001), and received less daily time of caregiving (mean (± SD): 10.0 ± 7.2 vs. 15.2 ± 8.2; p < 0.0001). No gender differences were highlighted in compliance to treatment and caregiver satisfaction, while gender differences in caregiving were found at disadvantage of women affected by more severe cognitive and psychiatric conditions.

Complexity analysis from EEG data in congestive heart failure: A study via approximate entropy.

AIM: Congestive heart failure (CHF) is a very complex clinical syndrome that may lead to ischemic cerebral hypoxia condition. The aim of the present study is to analyze the effects of CHF on brain activity through electroencephalographic (EEG) complexity measures, like approximate entropy (ApEn). METHODS: Twenty patients with CHF and 18 healthy elderly people were recruited. ApEn values were evaluated in the total spectrum (0.2-47 Hz) and main EEG frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz) to identify differences between CHF group and control. Moreover, a correlation analysis was performed between ApEn parameters and clinical data (i.e., B-type natriuretic peptides (BNP), New York Heart Association (NYHA), and systolic blood pressure (SBP)) within the CHF group. RESULTS: Statistical topographic maps showed statistically significant differences between the two groups in the total spectrum and theta frequency band. Within the CHF group, significant negative correlations were found between total ApEn and BNP in O2 channel and between theta ApEn and NYHA scores in Fp1, Fp2, and Fz channels; instead, a significant positive correlation was found between theta ApEn and SBP in C3 channel and a nearly significant positive correlation was obtained between theta ApEn and SBP in F4 channel. CONCLUSION: EEG abnormalities in CHF are very similar to those observed in cognitive-impaired patients, suggesting analogies between the effects of neurodegeneration and brain chronic hypovolaemia due to heart disorder and underlying high brain sensitivity to CHF.