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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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

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., (© 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

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.

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 Aβ42/40, 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; Aβ42/40 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 Aβ 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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity., (Copyright © 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer's disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration., (© 2023. The Author(s), under exclusive licence to American Aging Association.)

Introduction: Global estimates on numbers of persons in early stages of Alzheimer's disease (AD), including prodromal and preclinical, are lacking, yet are needed to inform policy decisions on preventive measures and planning for future therapies targeting AD pathology., Methods: We synthesized the literature on prevalence across the AD continuum and derived a model estimating the number of persons, stratified by 5-year age groups, sex, and disease stage (AD dementia, prodromal AD, and preclinical AD)., Results: The global number of persons with AD dementia, prodromal AD, and preclinical AD were estimated at 32, 69, and 315 million, respectively. Together they constituted 416 million across the AD continuum, or 22% of all persons aged 50 and above., Discussion: Considering predementia stages, the number of persons with AD is much larger than conveyed in available literature. Our estimates are uncertain, especially for predementia stages in low- and middle-income regions where biomarker studies are missing., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence., Methods: A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods., Results: Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers., Discussion: On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

In the human brain, physiological aging is characterized by progressive neuronal loss, leading to disruption of synapses and to a degree of failure in neurotransmission and information flow. However, there is increasing evidence to support the notion that the aged brain has a remarkable level of resilience (i.s. ability to reorganize itself), with the aim of preserving its physiological activity. It is therefore of paramount interest to develop objective markers able to characterize the biological processes underlying brain aging in the intact human, and to distinguish them from brain degeneration associated to age-related neurological progressive diseases like Alzheimer's disease. EEG, alone and combined with transcranial magnetic stimulation (TMS-EEG), is particularly suited to this aim, due to the functional nature of the information provided, and thanks to the ease with which it can be integrated in ecological scenarios including behavioral tasks. In this review, we aimed to provide the reader with updated information about the role of modern methods of EEG and TMS-EEG analysis in the investigation of physiological brain aging and Alzheimer's disease. In particular, we focused on data about cortical connectivity obtained by using readouts such graph theory network brain organization and architecture, and transcranial evoked potentials (TEPs) during TMS-EEG. Overall, findings in the literature support an important potential contribution of such neurophysiological techniques to the understanding of the mechanisms underlying normal brain aging and the early (prodromal/pre-symptomatic) stages of dementia., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient's autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the 'cognitive brain wave' P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Noninvasive brain stimulation techniques (NiBS) have gathered substantial interest in the study of dementia, considered their possible role in help defining diagnostic biomarkers of altered neural activity for early disease detection and monitoring of its pathophysiological course, as well as for their therapeutic potential of boosting residual cognitive functions. Nevertheless, current approaches suffer from some limitations. In this study, we review and discuss experimental NiBS applications that might help improve the efficacy of future NiBS uses in Alzheimer's Disease (AD), including perturbation-based biomarkers for early diagnosis and disease tracking, solutions to enhance synchronization of oscillatory electroencephalographic activity across brain networks, enhancement of sleep-related memory consolidation, image-guided stimulation for connectome control, protocols targeting interneuron pathology and protein clearance, and finally hybrid-brain models for in-silico modeling of AD pathology and personalized target selection. The present work aims to stress the importance of multidisciplinary, translational, model-driven interventions for precision medicine approaches in AD., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T 0 ), after treatment (T 1 ), and after 40 weeks (T 2 ), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6 weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD., (© 2021. The Author(s), under exclusive licence to American Aging Association.)

Background: Physiological and pathological brain aging plays a central role in brain network modulation. The aim of the present article was to assess the stability of a proposed method for evaluation of small-world (SW) characteristics for the study of the human connectome. Subjects and Methods: Eighty subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease (AD). Electroencephalograms (EEGs) were recorded during six separate sessions (480 recordings) at an average intersession interval of 3.8 ± 0.2 days. We applied graph theory functions to the weighted and undirected networks obtained by the lagged linear coherence estimated by exact low-resolution electromagnetic tomography (eLORETA). We explored the following frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Results: The proposed method for evaluation of SW characteristics exhibited good reproducibility and stability. Furthermore, results showed the pattern, Young>Elderly>AD, in low-frequency delta and theta bands and vice versa in the higher alpha band. Finally, a correlation with age was confirmed in healthy subjects, showing that the older the age, the higher the SW values for alpha 2. Discussion: Evidences from the present study confirm the stability of the SW index and suggest that the analysis of connectivity patterns evaluated from EEGs can be supported by the graph theory. The proposed method for evaluation of SW characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on administration of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions. Impact statement The graph analysis tools described in this study represent an interesting approach to study the distinctive characteristics of physiological aging by focusing on functional connectivity networks. The proposed method for evaluation of small-world characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on delivery of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions.

Introduction: Healthy elderly, mild cognitive impairment and Alzheimer's disease populations have been among the most affected in the early stages of the COVID-19 pandemic due to the direct effects of the virus, and numerous indirect effects now emerge and will have to be carefully assessed over time., Methods: This article reviews the main articles that have been published so far about the direct and indirect effects of the COVID-19 pandemic on these particularly fragile populations., Results: The pandemic associated to COVID-19 has shifted most of the health resources to the emergency area and has consequently left the three main medical areas dealing with the elderly population (oncology, time-dependent diseases and degenerative disease) temporarily "uncovered". In the phase following the emergency, it will be crucial to guarantee to each area the economic and organizational resources to quickly return to the level of support of the prepandemic state., Conclusions: The emergency phase represented a significant occasion of discussion on the possibilities of telemedicine which will inevitably become increasingly important, but all the limits of its use in the elderly population have to be considered. In the post-lockdown recovery phase, alongside the classic medical evaluation, the psychological evaluation must become even more important for doctors caring about people with cognitive decline as well as with their caregivers.

Background: Most common progressive brain diseases in the elderly are Alzheimer's disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different., Objective: The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis., Methods: 274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool., Results: VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients., Conclusion: Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.

Objective: Recent findings suggested that subclinical epileptiform activity is prevalent during sleep in a significant proportion of Alzheimer's Disease (AD) patients., The Aims of Our Study Were: (A) comparing the frequency of subclinical epileptiform activity during the sleep in a sample diagnosed with 'probable' AD and Mild Cognitive Impairment (MCI) due to AD, and in healthy subjects; (B) evaluating epileptiform EEG activity as a function of different sleep stages within a well-controlled polysomnographic setting., Methods: We prospectively enrolled 50 'probable' AD patients (73 ± 7.0 years) and 50 subjects with MCI due to AD (72 ± 6.7 years) without history of seizures, comparing them with 50 controls (69 ± 6.7 years). Patients underwent to a full-night video-PSG., Results: Subclinical epileptiform activity was detected in 6.38% of 'probable' AD patients, 11.63% of MCI due to AD subjects and 4.54% of controls (p = 0.43). The comparisons between the three groups for the frequency of epileptiform activity did not reach statistically significant differences neither for total sleep nor for any sleep period considered., Conclusions: Our study shows that, when controlling for sleep stages and the influence of psychoactive drugs, AD patients and MCI due to AD subjects do not exhibit a higher frequency of epileptiform discharges during sleep compared to healthy subjects., Significance: Subclinical epileptiform activity during sleep does not discriminate 'probable' AD from MCI due to AD and healthy controls., Competing Interests: Declaration of Competing of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Amyloid and tau pathological accumulation should be considered for Alzheimer's disease (AD) definition and before subjects' enrollment in disease-modifying trials. Although age, APOEε4, and sex influence cerebrospinal fluid (CSF) biomarker levels, none of these variables are considered by current normality/abnormality cutoffs. Using baseline CSF data from 2 independent cohorts (PharmaCOG/European Alzheimer's Disease Neuroimaging Initiative and Alzheimer's Disease Neuroimaging Initiative), we investigated the effect of age, APOEε4 status, and sex on CSF Aβ42/P-tau distribution and cutoff extraction by applying mixture models with covariates. The Aβ42/P-tau distribution revealed the presence of 3 subgroups (AD-like, intermediate, control-like) and 2 cutoffs. The identification of the intermediate subgroup and of the higher cutoff was APOEε4 dependent in both cohorts. APOE-specific classification (higher cutoff for APOEε4+, lower cutoff for APOEε4-) showed higher diagnostic accuracy in identifying MCI due to AD compared to single Aβ42 and Aβ42/P-tau cutoffs. APOEε4 influences amyloid and tau CSF markers and AD progression in MCI patients supporting i) the use of APOE-specific cutoffs to identify MCI due to AD and ii) the utility of considering APOE genotype for early AD diagnosis., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer's disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

Background: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls cooperating with neuropsychological tests for an early diagnosis. Advanced network analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity and could be used in classification processes. If successfully reached, this goal would add a low-cost, easily accessible, and non-invasive technique with neuropsychological tests., Objective: To investigate the possibility to automatically classify physiological versus pathological aging from cortical sources' connectivity based on a support vector machine (SVM) applied to EEG small-world parameter., Methods: A total of 295 subjects were recruited: 120 healthy volunteers and 175 AD. Graph theory functions were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA. A machine-learning classifier (SVM) was applied. EEG frequency bands were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz)., Results: The receiver operating characteristic curve showed AUC of 0.97±0.03 (indicating very high classification accuracy). The classifier showed 95% ±5% sensitivity, 96% ±3% specificity, and 95% ±3% accuracy for the classification., Conclusion: EEG connectivity analysis via a combination of source/connectivity biomarkers, highly correlating with neuropsychological AD diagnosis, could represent a promising tool in identification of AD patients. This approach represents a low-cost and non-invasive method, one that utilizes widely available techniques which, when combined, reach high sensitivity/specificity and optimal classification accuracy on an individual basis (0.97 of AUC).

Background: Early Alzheimer's disease (AD) detection using cerebrospinal fluid (CSF) biomarkers has been recommended as enrichment strategy for trials involving mild cognitive impairment (MCI) patients., Objective: To model a prodromal AD trial for identifying MRI structural biomarkers to improve subject selection and to be used as surrogate outcomes of disease progression., Methods: APOE ɛ4 specific CSF Aβ42/P-tau cut-offs were used to identify MCI with prodromal AD (Aβ42/P-tau positive) in the WP5-PharmaCog (E-ADNI) cohort. Linear mixed models were performed 1) with baseline structural biomarker, time, and biomarker×time interaction as factors to predict longitudinal changes in ADAS-cog13, 2) with Aβ42/P-tau status, time, and Aβ42/P-tau status×time interaction as factors to explain the longitudinal changes in MRI measures, and 3) to compute sample size estimation for a trial implemented with the selected biomarkers., Results: Only baseline lateral ventricle volume was able to identify a subgroup of prodromal AD patients who declined faster (interaction, p = 0.003). Lateral ventricle volume and medial temporal lobe measures were the biomarkers most sensitive to disease progression (interaction, p≤0.042). Enrichment through ventricular volume reduced the sample size that a clinical trial would require from 13 to 76%, depending on structural outcome variable. The biomarker needing the lowest sample size was the hippocampal subfield GC-ML-DG (granule cells of molecular layer of the dentate gyrus) (n = 82 per arm to demonstrate a 20% atrophy reduction)., Conclusion: MRI structural biomarkers can enrich prodromal AD with fast progressors and significantly decrease group size in clinical trials of disease modifying drugs.

Background: Assessment of human brain atrophy in temporal regions using magnetic resonance imaging (MRI), resting state functional MRI connectivity in the left parietal cortex, and limbic electroencephalographic (rsEEG) rhythms as well as plasma amyloid peptide 42 (Aβ42) has shown that each is a promising biomarker of disease progression in amnestic mild cognitive impairment (aMCI) patients with prodromal Alzheimer's disease (AD). However, the value of their combined use is unknown., Objective: To evaluate the association with cognitive decline and the effect on sample size calculation when using a biomarker composite matrix in prodromal AD clinical trials., Methods: Multicenter longitudinal study with follow-up of 2 years or until development of incident dementia. APOE ɛ4-specific cerebrospinal fluid (CSF) Aβ42/P-tau cut-offs were used to identify aMCI with prodromal AD. Linear mixed models were performed 1) with repeated matrix values and time as factors to explain the longitudinal changes in ADAS-cog13, 2) with CSF Aβ42/P-tau status, time, and CSF Aβ42/P-tau status×time interaction as factors to explain the longitudinal changes in matrix measures, and 3) to compute sample size estimation for a trial implemented with the selected matrices., Results: The best composite matrix included the MRI volumes of hippocampal dentate gyrus and lateral ventricle. This matrix showed the best sensitivity to track disease progression and required a sample size 31% lower than that of the best individual biomarker (i.e., volume of hippocampal dentate gyrus)., Conclusion: Optimal matrices improved the statistical power to track disease development and to measure clinical progression in the short-term period. This might contribute to optimize the design of future clinical trials in MCI.

Alzheimer's disease is the most common age-related neurodegenerative disorder and its burden on patients, families, and society grows significantly with lifespan. Early modifications of risk-enhancing lifestyles and treatment initiation expand personal autonomy and reduce management costs. Many clinical trials with potentially disease-modifying drugs are devoted to mild cognitive impairment (MCI) prodromal-to-Alzheimer's disease. The identification of biomarkers for early diagnosis may thus be crucial for early intervention and identification of high-risk subjects, the most appropriate target of new drugs as soon as they will be discovered. INTERCEPTOR is a strategic project by the Italian Ministry of Health and the Italian Medicines Agency (AIFA), aiming to validate the best combination (highly accurate, non-invasive, available on the whole national territory and financially sustainable) of biomarkers and organizational model for early diagnosis. 500 MCI subjects will be enrolled at baseline and followed-up for 3 years for at least 400 of them in order to define a "hub & spoke" nationwide model with recruiting (spokes) centers for MCI identification and expert (hubs) centers for risk diagnosis.

Auditory "oddball" event-related potentials (aoERPs), resting state functional magnetic resonance imaging (rsfMRI) connectivity, and electroencephalographic (rsEEG) rhythms were tested as longitudinal functional biomarkers of prodromal Alzheimer's disease (AD). Data were collected at baseline and four follow-ups at 6, 12, 18, and 24 months in amnesic mild cognitive impairment (aMCI) patients classified in two groups: "positive" (i.e., "prodromal AD"; n = 81) or "negative" (n = 63) based on a diagnostic marker of AD derived from cerebrospinal samples (Aβ42/P-tau ratio). A linear mixed model design was used to test functional biomarkers for Group, Time, and Group×Time effects adjusted by nuisance covariates (only data until conversion to dementia was used). Functional biomarkers that showed significant Group effects ("positive" versus "negative", p < 0.05) regardless of Time were 1) reduced rsfMRI connectivity in both the default mode network (DMN) and the posterior cingulate cortex (PCC), both also giving significant Time effects (connectivity decay regardless of Group); 2) increased rsEEG source activity at delta (<4 Hz) and theta (4-8 Hz) rhythms and decreased source activity at low-frequency alpha (8-10.5 Hz) rhythms; and 3) reduced parietal and posterior cingulate source activities of aoERPs. Time×Group effects showed differential functional biomarker progression between groups: 1) increased rsfMRI connectivity in the left parietal cortex of the DMN nodes, consistent with compensatory effects and 2) increased limbic source activity at theta rhythms. These findings represent the first longitudinal characterization of functional biomarkers of prodromal AD relative to "negative" aMCI patients based on 5 serial recording sessions over 2 years.

It is an open issue whether blood biomarkers serve to diagnose Alzheimer's disease (AD) or monitor its progression over time from prodromal stages. Here, we addressed this question starting from data of the European FP7 IMI-PharmaCog/E-ADNI longitudinal study in amnesic mild cognitive impairment (aMCI) patients including biological, clinical, neuropsychological (e.g., ADAS-Cog13), neuroimaging, and electroencephalographic measures. PharmaCog/E-ADNI patients were classified as "positive" (i.e., "prodromal AD" n = 76) or "negative" (n = 52) based on a diagnostic cut-off of Aβ42/P-tau in cerebrospinal fluid as well as APOE ε 4 genotype. Blood was sampled at baseline and at two follow-ups (12 and 18 months), when plasma amyloid peptide 42 and 40 (Aβ42, Aβ40) and apolipoprotein J (clusterin, CLU) were assessed. Linear Mixed Models found no significant differences in plasma molecules between the "positive" (i.e., prodromal AD) and "negative" groups at baseline. In contrast, plasma Aβ42 showed a greater reduction over time in the prodromal AD than the "negative" aMCI group (p = 0.048), while CLU and Aβ40 increased, but similarly in the two groups. Furthermore, plasma Aβ42 correlated with the ADAS-Cog13 score both in aMCI patients as a whole and the prodromal AD group alone. Finally, CLU correlated with the ADAS-Cog13 only in the whole aMCI group, and no association with ADAS-Cog13 was found for Aβ40. In conclusion, plasma Aβ42 showed disease progression-related features in aMCI patients with prodromal AD.

Objective: Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic-MCI (aMCI) subjects progress to dementia (typically to Alzheimer-Dementia = AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo-E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year., Methods: 145 aMCI subjects were divided into two sub-groups and, according to the clinical follow-up, were classified as Converted to AD (C-MCI, 71) or Stable (S-MCI, 74)., Results: Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first-order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo-E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50% sensitivity, 69% specificity and 59.6% accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%)., Interpretation: Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. Ann Neurol 2018 Ann Neurol 2018;84:302-314., (© 2018 American Neurological Association.)

Electroencephalographic (EEG) rhythms are linked to any kind of learning and cognitive performance including motor tasks. The brain is a complex network consisting of spatially distributed networks dedicated to different functions including cognitive domains where dynamic interactions of several brain areas play a pivotal role. Brain connectome could be a useful approach not only to mechanisms underlying brain cognitive functions, but also to those supporting different mental states. This goal was approached via a learning task providing the possibility to predict performance and learning along physiological and pathological brain aging. Eighty-six subjects (22 healthy, 47 amnesic mild cognitive impairment, 17 Alzheimer's disease) were recruited reflecting the whole spectrum of normal and abnormal brain connectivity scenarios. EEG recordings were performed at rest, with closed eyes, both before and after the task (Sensory Motor Learning task consisting of a visual rotation paradigm). Brain network properties were described by Small World index (SW), representing a combination of segregation and integration properties. Correlation analyses showed that alpha 2 SW in pre-task significantly predict learning (r  =  -0.2592, p < 0.0342): lower alpha 2 SW (higher possibility to increase during task and better the learning of this task), higher the learning as measured by the number of reached targets. These results suggest that, by means of an innovative analysis applied to a low-cost and widely available techniques (SW applied to EEG), the functional connectome approach as well as conventional biomarkers would be effective methods for monitoring learning progress during training both in normal and abnormal conditions.

Brain imaging plays an important role in the study of Alzheimer's disease (AD), where atrophy has been found to occur in the hippocampal formation during the very early disease stages and to progress in parallel with the disease's evolution. The aim of the present study was to evaluate a possible correlation between "Small World" characteristics of the brain connectivity architecture-as extracted from EEG recordings-and hippocampal volume in AD patients. A dataset of 144 subjects, including 110 AD (MMSE 21.3) and 34 healthy Nold (MMSE 29.8) individuals, was evaluated. Weighted and undirected networks were built by the eLORETA solutions of the cortical sources' activities moving from EEG recordings. The evaluation of the hippocampal volume was carried out on a subgroup of 60 AD patients who received a high-resolution T1-weighted sequence and underwent processing for surface-based cortex reconstruction and volumetric segmentation using the Freesurfer image analysis software. Results showed that, quantitatively, more correlation was observed in the right hemisphere, but the same trend was seen in both hemispheres. Alpha band connectivity was negatively correlated, while slow (delta) and fast-frequency (beta, gamma) bands positively correlated with hippocampal volume. Namely, the larger the hippocampal volume, the lower the alpha and the higher the delta, beta, and gamma Small World characteristics of connectivity. Accordingly, the Small World connectivity pattern could represent a functional counterpart of structural hippocampal atrophying and related-network disconnection.

Although a slowing of electroencephalographic (EEG) activity during wakefulness and -to some extent- sleep of Alzheimer disease (AD) patients (i.e., increased slow-frequency activity) was documented, recent findings in healthy elderly show a decreased 0.6-1 Hz slow wave activity (SWA) during NREM, which was associated to β-amyloid deposition and impaired hippocampal memory consolidation. We hypothesize that the apparent contradiction may be explained by the partial overlap between 0.6-1 Hz EEG activity and K-Complex (KC). According to this view, we studied both frontal KCs and SWA in 20 AD patients and 20 healthy age-matched controls (HC) during nightly sleep, under the hypothesis that KCs better discriminate patients from healthy elderly than ≤1 Hz SWA. A drastic decrease of KC density during stage 2 NREM was found in AD compared to HC. Patients show more than 40% reduction of the KC density, allowing a correct classification of 80%. On the other hand, ≤1 Hz SWA of AD patients is slightly (not significantly) higher in most cortical areas compared to HC. Although no significant changes of ≤1 Hz SWA are detectable over frontal areas in AD, KC density decreases over the same location, and its decrease is related to the cognitive decline.

Meta-analyses show that serum copper non-bound-to-ceruloplasmin (non-Cp-Cu) is higher in patients with Alzheimer's disease (AD). ATP7B gene variants associate with AD, modulating the size of non-Cp-Cu pool. However, a dedicated genetic study comparing AD patients after stratification for a copper biomarker to demonstrate the existence of a copper subtype of AD has not yet been carried out. An independent patient sample of 287 AD patients was assessed for non-Cp-Cu serum concentrations, rs1801243, rs1061472, and rs732774 ATP7B genetic variants and the APOE4 genotype. Patients were stratified into two groups based on a non-Cp-Cu cutoff (1.9 μM). Single-locus and haplotype-group analyses were performed to define their frequencies in dependence of the non-Cp-Cu group. The two AD subgroups did not differ regarding age, sex, MMSE score, or APOE4 frequency allele, while they did differ regarding non-Cp-Cu concentrations in serum, allele, genotype, and haplotype frequencies of rs1061472 A > G and rs732774 C > T after multiple testing corrections. AD patients with a GG genotype had a 1.76-fold higher risk of having a non-Cp-Cu higher than 1.9 μmol/L (p = 0.029), and those with a TT genotype for rs732774 C > T of 1.8-fold (p = 0.018). After 100,000 permutations for multiple testing corrections, the haplotype containing the AC alleles appeared more frequently in AD patients with normal non-Cp-Cu [43 vs. 33 %; Pm = 0.03], while the haplotype containing the GT risk alleles appeared more frequently in the higher non-Cp-Cu AD (66 vs. 55 %; Pm = 0.01). Genetic heterogeneity sustains a copper AD metabolic subtype; non-Cp-Cu is a marker of this copper AD.

In a recent study, analyzing the modulation of γ-band oscillations, Naro and colleagues demonstrated that transcranial alternating current stimulation could drive the gamma rhythms in the human EEG in cognitive healthy elderly subjects but not in mild cognitive impairment (MCI) prodromal to Alzheimer's disease (AD) and in early AD patients. Therefore, this method is proposed to intercept early in the disease course those MCI subjects who are in a pre-symptomatic stage of an already established AD. This prediction index may help the clinician to adopt a better prevention/follow-up strategy. In this direction, the novel advances in EEG analysis for the evaluation of brain reactivity and connectivity-namely via innovative mathematical approach, i.e., graph theory-represent a promising tool for a non-invasive and easy-to-perform neurophysiological marker that could be used for the pre-symptomatic diagnosis of AD and to predict MCI progression to dementia.

Several studies have shown that, in spite of the fact that motor symptoms manifest late in the course of Alzheimer's disease (AD), neuropathological progression in the motor cortex parallels that in other brain areas generally considered more specific targets of the neurodegenerative process. It has been suggested that motor cortex excitability is enhanced in AD from the early stages, and that this is related to disease's severity and progression. To investigate the neurophysiological hallmarks of motor cortex functionality in early AD we combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We demonstrated that in mild AD the sensorimotor system is hyperexcitable, despite the lack of clinically evident motor manifestations. This phenomenon causes a stronger response to stimulation in a specific time window, possibly due to locally acting reinforcing circuits, while network activity and connectivity is reduced. These changes could be interpreted as a compensatory mechanism allowing for the preservation of sensorimotor programming and execution over a long period of time, regardless of the disease's progression. Hum Brain Mapp 37:2083-2096, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Brain activity is complex; a reflection of its structural and functional organization. Among other measures of complexity, the fractal dimension is emerging as being sensitive to neuronal damage secondary to neurological and psychiatric diseases. Here, we calculated Higuchi's fractal dimension (HFD) in resting-state eyes-closed electroencephalography (EEG) recordings from 41 healthy controls (age: 20-89 years) and 67 Alzheimer's Disease (AD) patients (age: 50-88 years), to investigate whether HFD is sensitive to brain activity changes typical in healthy aging and in AD. Additionally, we considered whether AD-accelerating effects of the copper fraction not bound to ceruloplasmin (also called "free" copper) are reflected in HFD fluctuations. The HFD measure showed an inverted U-shaped relationship with age in healthy people (R2 = .575, p < .001). Onset of HFD decline appeared around the age of 60, and was most evident in central-parietal regions. In this region, HFD decreased with aging stronger in the right than in the left hemisphere (p = .006). AD patients demonstrated reduced HFD compared to age- and education-matched healthy controls, especially in temporal-occipital regions. This was associated with decreasing cognitive status as assessed by mini-mental state examination, and with higher levels of non-ceruloplasmin copper. Taken together, our findings show that resting-state EEG complexity increases from youth to maturity and declines in healthy, aging individuals. In AD, brain activity complexity is further reduced in correlation with cognitive impairment. In addition, elevated levels of non-ceruloplasmin copper appear to accelerate the reduction of neural activity complexity. Overall, HDF appears to be a proper indicator for monitoring EEG-derived brain activity complexity in healthy and pathological aging.

Here we presented a single electroencephalographic (EEG) marker for a neurophysiological assessment of Alzheimer's disease (AD) patients already diagnosed by current guidelines. The ability of the EEG marker to classify 127 AD individuals and 121 matched cognitively intact normal elderly (Nold) individuals was tested. Furthermore, its relationship to AD patients' cognitive status and structural brain integrity was examined. Low-resolution brain electromagnetic tomography (LORETA) freeware estimated cortical sources of resting state eyes-closed EEG rhythms. The EEG marker was defined as the ratio between the activity of parieto-occipital cortical sources of delta (2-4 Hz) and low-frequency alpha (8-10.5 Hz) rhythms. Results showed 77.2% of sensitivity in the recognition of the AD individuals; 65% of specificity in the recognition of the Nold individuals; and 0.75 of area under the receiver-operating characteristic curve. Compared to the AD subgroup with the EEG maker within one standard deviation of the Nold mean (EEG-), the AD subgroup with EEG+ showed lower global cognitive status, as revealed by Mini-Mental State Evaluation score, and more abnormal values of white-matter and cerebrospinal fluid normalized volumes, as revealed by structural magnetic resonance imaging. We posit that cognitive and functional status being equal, AD patients with EEG+ should receive special clinical attention due to a neurophysiological "frailty". EEG+ label can be also used in clinical trials (i) to form homogeneous groups of AD patients diagnosed by current guidelines and (ii) as end-point to evaluate intervention effects.

Several studies have identified two types of sleep spindles: fast (13-15 Hz) centroparietal and slow (11-13 Hz) frontal spindles. Alterations in spindle activity have been observed in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). Only few studies have separately assessed fast and slow spindles in these patients showing a reduction of fast spindle count, but the possible local specificity of this phenomenon and its relation to cognitive decline severity are not clear. Moreover, fast and slow spindle density have never been assessed in AD/MCI. We have assessed fast and slow spindles in 15 AD patients, 15 amnesic MCI patients, and 15 healthy elderly controls (HC). Participants underwent baseline polysomnographic recording (19 cortical derivations). Spindles during nonrapid eye movements sleep were automatically detected, and spindle densities of the three groups were compared in the derivations where fast and slow spindles exhibited their maximum expression (parietal and frontal, resp.). AD and MCI patients showed a significant parietal fast spindle density decrease, positively correlated with Minimental State Examination scores. Our results suggest that AD-related changes in spindle density are specific for frequency and location, are related to cognitive decline severity, and may have an early onset in the pathology development.

Meta-analyses demonstrate copper involvement in Alzheimer's disease (AD), and the systemic ceruloplasmin status in relation to copper is an emerging issue. To deepen this matter, we evaluated levels of ceruloplasmin concentration, ceruloplasmin activity, ceruloplasmin specific activity (eCp/iCp), copper, non-ceruloplasmin copper iron, transferrin, the ceruloplasmin/transferrin ratio, and the APOE genotype in a sample of 84 AD patients and 58 healthy volunteers. From the univariate logistic analyses we found that ceruloplasmin concentration, eCp/iCp, copper, transferrin, the ceruloplasmin/transferrin ratio, and the APOE genotype were significantly associated with the probability of AD. In the multivariable logistic regression analysis, we selected the best subset of biological predictors by the forward stepwise procedure. The analysis showed a decrease of the risk of having AD for eCp/iCp (p = 0.001) and an increase of this risk for non-ceruloplasmin copper (p = 0.008), age (p = 0.001), and APOE-ɛ4 allele (p <  0.001). The estimated model showed a good power in discriminating AD patients from healthy controls (area under curve: 88% ; sensitivity: 66% ; specificity 93%). These data strength the breakdown of copper homeostasis and propose eCp/iCp as a reliable marker of ceruloplasmin status.

Background: Meta-analyses show that percentages of non-Cp-Cu-copper that is not bound to ceruloplasmin (also known as 'free' copper)-in serum are higher in Alzheimer's disease (AD) patients. Genetic heterogeneity in AD patients stratified on the basis of non-Cp-Cu cut-off sustains the existence of a copper AD metabolic subtype. Non-Cp-Cu abnormalities correlated with alterations of electroencephalographic rhythms (EEG)., Objective: We aimed to determine whether an EEG-derived brain cortical rhythm's heterogeneity between two AD groups stratified on the basis of a copper marker., Method: We assessed levels of copper, ceruloplasmin, Non-Cp-Cu, and the APOE4 genotype in 67 AD patients and compared resting EEG-derived eLORETA cortical rhythms between AD groups stratified in terms of 'Normal' and 'High' non-Cp-Cu., Results: The High non-Cp-Cu group experienced a lower power in all bands (0.2-48 Hz) in the parietal cortices (p=0.019) and a more limited alpha band (8-13 Hz) power in the sensory lobes (temporal, occipital, and parietal p>0.05 consistently) than the Normal non-Cp-Cu AD group. When corrected for MMSE, the non-Cp-Cu levels correlated with a reduction of high-frequency brain activity (from high alpha to gamma, 10.5-48 Hz)., Conclusion: This neurophysiological heterogeneity in EEG-derived brain cortical rhythms between the two AD groups sustains a copper AD metabolic subtype; Non-Cp-Cu is a marker of this copper AD.

Objective: This paper proposes a new, complex algorithm for the blind classification of the original electroencephalogram (EEG) tracing of each subject, without any preliminary pre-processing. The medical need in this field is to reach an early differential diagnosis between subjects affected by mild cognitive impairment (MCI), early Alzheimer's disease (AD) and the healthy elderly (CTR) using only the recording and the analysis of few minutes of their EEG., Methods and Material: This study analyzed the EEGs of 272 subjects, recorded at Rome's Neurology Unit of the Policlinico Campus Bio-Medico. The EEG recordings were performed using 19 electrodes, in a 0.3-70Hz bandpass, positioned according to the International 10-20 System. Many powerful learning machines and algorithms have been proposed during the last 20 years to effectively resolve this complex problem, resulting in different and interesting outcomes. Among these algorithms, a new artificial adaptive system, named implicit function as squashing time (I-FAST), is able to diagnose, with high accuracy, a few minutes of the subject's EEG track; whether it manifests an AD, MCI or CTR condition. An updating of this system, carried out by adding a new algorithm, named multi scale ranked organizing maps (MS-ROM), to the I-FAST system, is presented, in order to classify with greater accuracy the unprocessed EEG's of AD, MCI and control subjects., Results: The proposed system has been measured on three independent pattern recognition tasks from unprocessed EEG tracks of a sample of AD subjects, MCI subjects and CTR: (a) AD compared with CTR; (b) AD compared with MCI; (c) CTR compared with MCI. While the values of accuracy of the previous system in distinguishing between AD and MCI were around 92%, the new proposed system reaches values between 94% and 98%. Similarly, the overall accuracy with best artificial neural networks (ANNs) is 98.25% for the distinguishing between CTR and MCI., Conclusions: This new version of I-FAST makes different steps forward: (a) avoidance of pre-processing phase and filtering procedure of EEG data, being the algorithm able to directly process an unprocessed EEG; (b) noise elimination, through the use of a training variant with input selection and testing system, based on naïve Bayes classifier; (c) a more robust classification phase, showing the stability of results on nine well known learning machine algorithms; (d) extraction of spatial invariants of an EEG signal using, in addition to the unsupervised ANN, the principal component analysis and the multi scale entropy, together with the MS-ROM; a more accurate performance in this specific task., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Occipital sources of resting-state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here, we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging. Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects. Neurodegeneration of occipital lobe was indexed by weighted averages of gray matter density, estimated from structural MRIs. EEG rhythms of interest were alpha 1 (8-10.5 Hz) and alpha 2 (10.5-13 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography. Results showed a positive correlation between occipital gray matter density and amplitude of occipital alpha 1 sources in Nold, MCI, and AD subjects as a whole group (r = 0.3, p = 0.000004, N = 235). Furthermore, there was a positive correlation between the amplitude of occipital alpha 1 sources and cognitive status as revealed by Mini Mental State Examination score across all subjects (r = 0.38, p = 0.000001, N = 235). Finally, amplitude of occipital alpha 1 sources allowed a moderate classification of individual Nold and AD subjects (sensitivity: 87.8%; specificity: 66.7%; area under the receiver operating characteristic curve: 0.81). These results suggest that the amplitude of occipital sources of resting-state alpha rhythms is related to AD neurodegeneration in occipital lobe along pathologic aging., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: Meta-analyses show that nonbound ceruloplasmin (non-Cp) copper (also known as free or labile copper) in serum is higher in patients with Alzheimer disease (AD). It differentiates subjects with mild cognitive impairment (MCI) from healthy controls. However, a longitudinal study on an MCI cohort has not yet been performed to assess the accuracy of non-Cp copper for the prediction of conversion from MCI to AD during a long-term follow-up., Methods: The study included 42 MCI converters and 99 stable MCI subjects. We assessed levels of copper, ceruloplasmin, non-Cp copper, iron, transferrin, ferritin, and APOE genotype. A multiple Cox regression analysis-with age, sex, baseline Mini-Mental State Examination, APOE4, iron, non-Cp copper, transferrin, ferritin, hypercholesterolemia, and hypertension as covariates-was applied to predict the conversion from MCI to AD., Results: Among the evaluated parameters, the only significant predictor of conversion to AD was non-Cp copper (hazard ratio = 1.23, 95% confidence interval = 1.03-1.47, p = 0.022); for each additional micromole per liter unit (μmol/l) of non-Cp copper, the hazard increased by ~20%. Subjects with non-Cp copper levels >1.6 μmol/l had a hazard conversion rate (50% of conversion in 4 years) that was ~3× higher than those with values ≤1.6 μmol/l (<20% in 4 years). The rate of conversion was similar between APOE4 carriers and noncarriers (p = 0.321), indicating that the non-Cp copper association was independent of APOE4., Interpretation: Non-Cp copper appears to predict conversion from MCI to AD. These results encourage healthy life style choices and dietary intervention to modify this risk., (© 2014 American Neurological Association.)

The aim of this study was to investigate the neuronal network characteristics in physiological and pathological brain aging. A database of 378 participants divided in three groups was analyzed: Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal elderly (Nold) subjects. Through EEG recordings, cortical sources were evaluated by sLORETA software, while graph theory parameters (Characteristic Path Length λ, Clustering coefficient γ, and small-world network σ) were computed to the undirected and weighted networks, obtained by the lagged linear coherence evaluated by eLORETA software. EEG cortical sources from spectral analysis showed significant differences in delta, theta, and alpha 1 bands. Furthermore, the analysis of eLORETA cortical connectivity suggested that for the normalized Characteristic Path Length (λ) the pattern differences between normal cognition and dementia were observed in the theta band (MCI subjects are find similar to healthy subjects), while for the normalized Clustering coefficient (γ) a significant increment was found for AD group in delta, theta, and alpha 1 bands; finally, the small world (σ) parameter presented a significant interaction between AD and MCI groups showing a theta increase in MCI. The fact that AD patients respect the MCI subjects were significantly impaired in theta but not in alpha bands connectivity are in line with the hypothesis of an intermediate status of MCI between normal condition and overt dementia.

To understand the role of the key copper-regulating gene, ATP7B, in copper dyshomeostasis associated with Alzheimer's disease (AD), we analyzed the serum levels of copper, ceruloplasmin and 'free' (i.e., non-ceruloplasmin bound) copper in 399 patients with AD and 303 elderly healthy controls. We also performed analyses of informative variants of ATP7B. AD patients had higher levels of copper and free copper than controls. Individuals with free copper levels higher than 1.6 μmol/L (the upper value of the normal reference range) were more frequent among cases (p < 0.001). Among these individuals, those who were carriers of the ATP7B variants accounted for a large proportion of the free copper levels, specifically in the AD group (p < 0.01). Our results suggest the existence of a 'copper dysfunction' phenotype of sporadic AD which has a genetic basis. They also suggest that free copper is a risk factor for this disorder, modulating additional pathways leading to the disease cascade.

Cortical gray matter volume and resting state cortical electroencephalographic rhythms are typically abnormal in subjects with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here we tested the hypothesis that in amnesic MCI and AD subjects, abnormalities of EEG rhythms are a functional reflection of cortical atrophy across the disease. Eyes-closed resting state EEG data were recorded in 57 healthy elderly (Nold), 102 amnesic MCI, and 108 AD patients. Cortical gray matter volume was indexed by magnetic resonance imaging recorded in the MCI and AD subjects according to Alzheimer's disease neuroimaging initiative project (http://www.adni-info.org/). EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). These rhythms were indexed by LORETA. Compared with the Nold, the MCI showed a decrease in amplitude of alpha 1 sources. With respect to the Nold and MCI, the AD showed an amplitude increase of delta sources, along with a strong amplitude reduction of alpha 1 sources. In the MCI and AD subjects as a whole group, the lower the cortical gray matter volume, the higher the delta sources, the lower the alpha 1 sources. The better the score to cognitive tests the higher the gray matter volume, the lower the pathological delta sources, and the higher the alpha sources. These results suggest that in amnesic MCI and AD subjects, abnormalities of resting state cortical EEG rhythms are not epiphenomena but are strictly related to neurodegeneration (atrophy of cortical gray matter) and cognition., (Copyright © 2011 Wiley Periodicals, Inc.)

Acetylcholinesterase inhibitors (AChEIs) are the most widely used symptomatic treatment for mild to severe Alzheimer's disease (AD) patients, while N-methyl-d-aspartic acid (NMDA) receptor antagonist memantine is licensed for use in moderate to severe AD patients. In this article, the effect of these compounds on resting state eyes-closed electroencephalographic (EEG) rhythms in AD patients is reviewed to form a knowledge platform for the European Innovative Medicine Initiative project "PharmaCog" (IMI Grant Agreement No. 115009) aimed at developing innovative translational models for drug testing in AD. Indeed, quite similar EEG experiments and the same kind of spectral data analysis can be performed in animal models of AD and in elderly individuals with prodromal or manifest AD. Several studies have shown that AChEIs affect both resting state EEG rhythms and cognitive functions in AD patients. After few weeks of successful treatment, delta (0-3 Hz) or theta (4-7 Hz) rhythms decrease, dominant alpha rhythms (8-10 Hz) increase, and cognitive functions slightly improve. Beneficial effects of these rhythms and cognitive functions were also found in AD responders to the long-term successful treatment (i.e. 6-12 months). In contrast, only one study has explored the long-term effects of memantine on EEG rhythms in AD patients, showing reduced theta rhythms. The present review enlightens the expected effects of AChEIs on resting state EEG rhythms in AD patients as promising EEG markers for the development of translational protocols both within the PharmaCog project and for wider use., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Different kinds of challenge can alter spontaneous ongoing electroencephalographic (EEG) rhythms in animal models, thus providing paradigms to evaluate treatment effects in drug discovery. The effects of challenges represented by pharmacological agents, hypoxia, sleep deprivation and transcranial magnetic stimulation (TMS) on EEG rhythms are here reviewed to build a knowledge platform for innovative translational models for drug discovery in Alzheimer's disease (AD). It has been reported that antagonists of cholinergic neurotransmission cause synchronisation of spontaneous ongoing EEG rhythms in terms of enhanced power of EEG low frequencies and decreased power of EEG high frequencies. Acetylcholinesterase inhibitors and serotonergic drugs may restore a normal pattern of EEG desynchronisation. Sleep deprivation and hypoxia challenges have also been reported to elicit abnormal synchronisation of spontaneous ongoing EEG rhythms in rodents. The feasibility and reproducibility of TMS have been demonstrated in rodents but information on a consistent modulation of EEG after TMS manipulation is very limited. Transgenic mice over-expressing human amyloid precursor protein complementary DNAs (cDNAs) harbouring the 'Swedish' mutation and PS-1 cDNAs harbouring the A264E mutation, which recapitulate some of the pathological features of AD, exhibit alterations of spontaneous ongoing EEG rhythms at several low and high frequencies. This does not appear, however, to be a consequence of beta-amyloid deposition in the brain. The present review provides a critical evaluation of changes of spontaneous ongoing EEG rhythms due to the experimental manipulations described above, in order to stimulate the promote more adherent models fitting dynamics in humans., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Copper dyshomeostasis leading to a labile Cu(2+) not bound to ceruloplasmin ("free" copper) may influence Alzheimer's disease (AD) onset or progression. To investigate this hypothesis, we investigated ATP7B, the gene that controls copper excretion through the bile and concentrations of free copper in systemic circulation. Our study analyzed informative ATP7B single-nucleotide polymorphisms (SNPs) in a case-control population (n=515). In particular, we evaluated the genetic structure of the ATP7B gene using the HapMap database and carried out a genetic association investigation. Linkage disequilibrium (LD) analysis highlighted that our informative SNPs and their LD SNPs covered 96% of the ATP7B gene sequence, distinguishing two "strong LD" blocks. The first LD block contains the gene region encoding for transmembrane and copper-binding, whereas the second LD block encodes for copper-binding domains. The genetic association analysis showed significant results after multiple testing correction for all investigated variants (rs1801243, odds ratio [OR]=1.52, 95% confidence interval [CI]=1.10-2.09, p=0.010; rs2147363, OR=1.58, 95% CI=1.11-2.25, p=0.010; rs1061472, OR=1.73, 95% CI=1.23-2.43, p=0.002; rs732774, OR=2.31, 95% CI=1.41-3.77, p<0.001), indicating that SNPs in transmembrane domains may have a stronger association with AD risk than variants in copper-binding domains. Our study provides novel insights that confirm the role of ATP7B as a potential genetic risk factor for AD. The analysis of ATP7B informative SNPs confirms our previous hypothesis about the absence of ATP7B in the significant loci of genome-wide association studies of AD and the genetic association study suggests that transmembrane and adenosine triphosphate (ATP) domains in the ATP7B gene may harbor variants/haplotypes associated with AD risk.

Different factors interact to develop neurodegeneration in patients with dementia and other neurodegenerative disorders. Oxidative stress and the ε4 allele of apolipoprotein E (ApoE) are associated with significant alteration in lipid metabolism, in turn connected to a variety of neurodegenerative diseases and aging. Thus, a better understanding of the pathogenetic pathways associated with lipid dyshomeostasis may elucidate the causes of neurodegenerative processes. To address this issue, we evaluated the effects of antioxidant status and APOE genotype on neurodegeneration in patients with dementia of the Alzheimer type (AD), with vascular dementia (VaD), and in elderly healthy controls. Eighty-two AD, 42 VaD patients, and 26 healthy controls were recruited and underwent medial temporal lobe atrophy (MTA) assessment, white matter hyperintensities rating (WMH), serum total antioxidant status assaying (TAS), and APOE genotyping. A logistic regression algorithm applied to our data revealed that a 0.01 mmol/L decrease of TAS concentration increased the probability of MTA by 24% (p=0.038) and that carriers of the APOE ε4 allele showed higher WMH scores (p=0.018), confirming that small variations in antioxidant systems homeostasis are associated with relevant modifications of disease risk. Furthermore, in individuals with analogous TAS values, the presence of the ε4 allele increased the predicted probability of having MTA. These outcomes further sustain the interaction of oxidative stress and APOE genotype to neurodegeneration.

The link between biometals and Alzheimer's disease (AD) has been investigated with a focus on local metal accumulations. In this work, we have looked at systemic metal changes and computed a score (M-score) based on metal disarrangements to discriminate patients with AD from patients with vascular dementia (VaD) and from controls. We measured serum levels of iron, copper, ceruloplasmin, transferrin, and total antioxidant capacity (TAS), performed Apolipoprotein E (APOE) genotyping and calculated non-ceruloplasmin copper ('free' copper') levels, transferrin saturation, total iron binding capacity, and ceruloplasmin-transferrin ratio (Cp/Tf) in 93 patients with AD, 45 patients with VaD, and 48 controls. All subjects underwent biochemical, neuroimaging and cognitive evaluations. Significant differences were observed among the tested groups for the levels of copper, free copper, peroxides, and TAS and for the Cp/Tf with disparity in couple comparison. On this basis we created the M-score as linear combination of biometal variables and APOE genotype. Besides its ability to discriminate AD patients vs. controls (ROC AUC=90%), M-score was able to distinguish AD vs. VaD (ROC AUC=79%). Moreover, we calculated the sensitivity and the specificity for M-score and for the other significant variables: M-score reached the highest sensitivity without a relevant loss in terms of specificity. When we compared M-score with APOE genotype and Medial Temporal Atrophy score, it resulted statistically better than these diagnostic markers. In conclusion, we confirm the link between biometals and AD and suggest its potential as diagnostic tool. Further studies may elucidate its potential role as reliable diagnostic test.

Cortical sources of resting state electroencephalographic (EEG) rhythms are abnormal in subjects with Alzheimer's disease (AD). Here we tested the hypothesis that these sources are also sensitive to the progression of early stage AD over the course of one year. The resting state eyes-closed EEG data were recorded in 88 mild AD patients at baseline (Mini Mental State Evaluation, MMSE I = 21.7 ± 0.2 standard error, SE) and at approximately one-year follow up (13.3 months ± 0.5 SE; MMSE II = 20 ± 0.4 SE). All patients received standard therapy with acetylcholinesterase inhibitors. EEG recordings were also performed in 35 normal elderly (Nold) subjects as controls. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Cortical EEG sources were estimated by low-resolution brain electromagnetic tomography (LORETA). Compared to the Nold subjects, the mild AD patients were characterized by a power increase of widespread delta sources and by a power decrease of posterior alpha sources. In the mild AD patients, the follow-up EEG recordings showed increased power of widespread delta sources as well as decreased power of widespread alpha and posterior beta 1 sources. These results suggest that the resting state EEG sources were sensitive, at least at group level, to the cognitive decline occurring in the mild AD group over a one-year period, and might represent cost-effective and non-invasive markers with which to enrich cohorts of AD patients that decline faster for clinical studies.