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Giuseppe Magnani

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8 papers

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YNICL Journal 2026 Journal Article

Resting-State EEG captures functional network correlates of plasma p-Tau-217 in Alzheimer’s disease

Giordano Cecchetti
Jacopo Lanzone
Luca Zanchi
Giulia Rugarli
Silvia Basaia
Marco Cursi
Federico Coraglia
Edoardo G. Spinelli

BACKGROUND: Scalable biomarkers are needed for early Alzheimer's disease (AD) detection. Plasma p-tau217 reflects AD pathology, while resting-state EEG captures functional brain alterations. Their relationship remains unclear. METHODS: We enrolled 128 patients with subjective cognitive decline (SCD), mild cognitive impairment due to AD (AD-MCI), or AD dementia (AD-DEM), who underwent 32-channel EEG and plasma biomarker assessment. EEG features included spectral, aperiodic, phase-amplitude coupling, and complexity metrics. Machine learning was used to classify p-tau217 positivity. RESULTS: AD-MCI and AD-DEM patients showed increased p-tau217 and spectral slowing (higher theta, lower alpha). While no correlations survived correction for multiple comparisons, stage-specific analyses revealed positive associations between theta power and p-tau217 in AD-MCI and AD-DEM. A random forest classifier achieved an AUC of 0.75 in predicting p-tau217 positivity. CONCLUSIONS: EEG captures functional alterations reflecting AD pathology beyond molecular measures, supporting its value as a complementary, non-invasive biomarker for early stratification in clinical settings.

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YNICL Journal 2021 Journal Article

Resting-state electroencephalographic biomarkers of Alzheimer’s disease

Giordano Cecchetti
Federica Agosta
Silvia Basaia
Camilla Cividini
Marco Cursi
Roberto Santangelo
Francesca Caso
Fabio Minicucci

OBJECTIVE: We evaluated the value of resting-state EEG source biomarkers to characterize mild cognitive impairment (MCI) subjects with an Alzheimer's disease (AD)-like cerebrospinal fluid (CSF) profile and to track neurodegeneration throughout the AD continuum. We further applied a resting-state functional MRI (fMRI)-driven model of source reconstruction and tested its advantage in terms of AD diagnostic accuracy. METHODS: Thirty-nine consecutive patients with AD dementia (ADD), 86 amnestic MCI, and 33 healthy subjects enter the EEG study. All ADD subjects, 37 out of 86 MCI patients and a distinct group of 53 healthy controls further entered the fMRI study. MCI subjects were divided according to the CSF phosphorylated tau/β amyloid-42 ratio (MCIpos: ≥ 0.13, MCIneg: < 0.13). Using Exact low-resolution brain electromagnetic tomography (eLORETA), EEG lobar current densities were estimated at fixed frequencies and analyzed. To combine the two imaging techniques, networks mostly affected by AD pathology were identified using Independent Component Analysis applied to fMRI data of ADD subjects. Current density EEG analysis within ICA-based networks at selected frequency bands was performed. Afterwards, graph analysis was applied to EEG and fMRI data at ICA-based network level. RESULTS: ADD patients showed a widespread slowing of spectral density. At a lobar level, MCIpos subjects showed a widespread higher theta density than MCIneg and healthy subjects; a lower beta2 density than healthy subjects was also found in parietal and occipital lobes. Evaluating EEG sources within the ICA-based networks, alpha2 band distinguished MCIpos from MCIneg, ADD and healthy subjects with good accuracy. Graph analysis on EEG data showed an alteration of connectome configuration at theta frequency in ADD and MCIpos patients and a progressive disruption of connectivity at alpha2 frequency throughout the AD continuum. CONCLUSIONS: Theta frequency is the earliest and most sensitive EEG marker of AD pathology. Furthermore, EEG/fMRI integration highlighted the role of alpha2 band as potential neurodegeneration biomarker.

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YNICL Journal 2019 Journal Article

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

Silvia Basaia
Federica Agosta
Luca Wagner
Elisa Canu
Giuseppe Magnani
Roberto Santangelo
Massimo Filippi

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients.

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YNICL Journal 2019 Journal Article

The clinico-metabolic correlates of language impairment in corticobasal syndrome and progressive supranuclear palsy

Alessandra Dodich
Chiara Cerami
Emanuela Inguscio
Sandro Iannaccone
Giuseppe Magnani
Alessandra Marcone
Priscilla Guglielmo
Giovanna Vanoli

PURPOSE: To assess the clinical-metabolic correlates of language impairment in a large sample of patients clinically diagnosed as corticobasal syndrome (CBS) and progressive supranuclear palsy syndrome (PSPs). METHODS: We included 70 patients fulfilling current criteria for CBS (n = 33) or PSPs (n = 37). All subjects underwent clinical-neuropsychological and FDG-PET assessments at the time of diagnosis. The whole patient's cohort was grouped into three subgroups according to the language characteristics, i.e., (a) nfv-PPA; (b) subtle language impairments, LANG-; (c) no language deficits, NOL-. FDG-PET data were analysed using an optimized voxel-based SPM method at the single-subject and group levels in order to evaluate specific hypometabolic patterns and regional dysfunctional FDG-PET commonalities in subgroups. RESULTS: 21 patients had a nfvPPA diagnosis (i.e., nfv-PPA/CBS = 12 and nfv-PPA/PSPs = 9), while 20 patients had a subtle language impairment LANG- (i.e., CBS = 12 and PSPs = 8), not fulfilling the criteria for a nfv-PPA diagnosis. The remaining sample (i.e., 9/33 CBS and 20/37 PSPs patients) did not show any language deficit. FDG-PET results in individuals with a nfv-PPA diagnosis were consistent with the typical nfv-PPA pattern of hypometabolism (i.e., left fronto-insular and superior medial frontal cortex involvement), both in CBS and PSPs. The LANG-CBS and LANG-PSPs subjects had different FDG-PET hypometabolic patterns involving, respectively, parietal and frontal regions. As expected, NOL-CBS and NOL-PSPs showed a predominant right hemisphere involvement, with selective functional metabolic signatures typical of the two syndromes. CONCLUSIONS: Language impairments, fulfilling the nfv-PPA criteria, are associated with both CBS and PSPs clinical presentations early in the disease course. Subtle language deficits may be present in an additional proportion of patients not fulfilling the nfv-PPA criteria. The topography of brain hypometabolism is a major dysfunctional signature of language deficits in CBS and PSPs clinical phenotypes.

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YNICL Journal 2018 Journal Article

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort

Silvia Paola Caminiti
Tommaso Ballarini
Arianna Sala
Chiara Cerami
Luca Presotto
Roberto Santangelo
Federico Fallanca
Emilia Giovanna Vanoli

Background/aims: In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias. Methods: We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as "typical-AD", "atypical-AD" (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), "non-AD" (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or "negative" patterns. To perform the statistical analyses, the individual patterns were grouped either as "AD dementia vs. non-AD dementia (all diseases)" or as "FTD vs. non-FTD (all diseases)". Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated. Results: The multivariate logistic model identified FDG-PET "AD" SPM classification (Expβ = 19.35, 95% C.I. 4.8-77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64-25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The "FTD" SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1-63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55-70.46, p < 0.001). Conclusions: Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers.

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YNICL Journal 2015 Journal Article

Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia

Chiara Cerami
Pasquale Anthony Della Rosa
Giuseppe Magnani
Roberto Santangelo
Alessandra Marcone
Stefano F. Cappa
Daniela Perani

[18F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [18F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. Their scans were compared to a large normal reference dataset developed and validated for comparison at single-subject level. Additionally, Aβ42 and Tau CSF values were available in 34 MCI subjects. Clinical follow-up (mean 28. 5 ± 7. 8 months) assessed subsequent progression to AD or non-AD dementias. The SPM analysis showed: 1) normal brain metabolism in 14 MCI cases, none of them progressing to dementia; 2) the typical temporo-parietal pattern suggestive for prodromal AD in 15 cases, 11 of them progressing to AD; 3) brain hypometabolism suggestive of frontotemporal lobar degeneration (FTLD) subtypes in 7 and dementia with Lewy bodies (DLB) in 2 subjects (all fulfilled FTLD or DLB clinical criteria at follow-up); and 4) 7 MCI cases showed a selective unilateral or bilateral temporo-medial hypometabolism without the typical AD pattern, and they all remained stable. In our sample, objective voxel-based analysis of [18F]FDG-PET scans showed high predictive prognostic value, by identifying either normal brain metabolism or hypometabolic patterns suggestive of different underlying pathologies, as confirmed by progression at follow-up. These data support the potential usefulness of this SPM [18F]FDG PET analysis in the early dementia diagnosis and for improving subject selection in clinical trials based on MCI definition.

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YNICL Journal 2014 Journal Article

Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting

Daniela Perani
Pasquale Anthony Della Rosa
Chiara Cerami
Francesca Gallivanone
Federico Fallanca
Emilia Giovanna Vanoli
Andrea Panzacchi
Flavio Nobili

Diagnostic accuracy in FDG-PET imaging highly depends on the operating procedures. In this clinical study on dementia, we compared the diagnostic accuracy at a single-subject level of a) Clinical Scenarios, b) Standard FDG Images and c) Statistical Parametrical (SPM) Maps generated via a new optimized SPM procedure. We evaluated the added value of FDG-PET, either Standard FDG Images or SPM Maps, to Clinical Scenarios. In 88 patients with neurodegenerative diseases (Alzheimer's Disease—AD, Frontotemporal Lobar Degeneration—FTLD, Dementia with Lewy bodies—DLB and Mild Cognitive Impairment—MCI), 9 neuroimaging experts made a forced diagnostic decision on the basis of the evaluation of the three types of information. There was also the possibility of a decision of normality on the FDG-PET images. The clinical diagnosis confirmed at a long-term follow-up was used as the gold standard. SPM Maps showed higher sensitivity and specificity (96% and 84%), and better diagnostic positive (6.8) and negative (0.05) likelihood ratios compared to Clinical Scenarios and Standard FDG Images. SPM Maps increased diagnostic accuracy for differential diagnosis (AD vs. FTD; beta 1.414, p = 0.019). The AUC of the ROC curve was 0.67 for SPM Maps, 0.57 for Clinical Scenarios and 0.50 for Standard FDG Images. In the MCI group, SPM Maps showed the highest predictive prognostic value (mean LOC = 2.46), by identifying either normal brain metabolism (exclusionary role) or hypometabolic patterns typical of different neurodegenerative conditions.

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YNIMG Journal 2001 Journal Article

Fatigue in Multiple Sclerosis Is Associated with Abnormal Cortical Activation to Voluntary Movement—EEG Evidence

Letizia Leocani
Bruno Colombo
Giuseppe Magnani
Filippo Martinelli-Boneschi
Marco Cursi
Paolo Rossi
Vittorio Martinelli
Giancarlo Comi

Converging evidence is consistent with the view that fatigue in Multiple Sclerosis is independent from pyramidal tract involvement, suggesting a possible involvement of frontal areas. During voluntary movement, changes of the EEG rhythms can be observed over sensorimotor areas. Event-related desynchronization (ERD) of the 10 and 20 Hz frequency bands occurs during motor planning and execution and is followed after movement termination by event-related synchronization (ERS), expressing cortical idling or inhibition. We evaluated the pattern of cortical activation to voluntary movement in MS patients complaining of fatigue assessed using the Fatigue Severity Scale. Fifteen MS patients complaining of fatigue, 18 MS patients without fatigue, and 14 normal controls were studied. The two patients groups were similar for age, sex, disease duration, and were not disabled (score <1. 5 at the Expanded Disability Status Scale). Twenty-nine channel EEG was recorded during about 60 self-paced extensions of the right thumb. The onset latency and amount of the contralateral sensorimotor (C3 electrode) 10 and 18–22 Hz ERD were similar in the three groups. ERD was more widespread anteriorly in the fatigue group compared with normal controls (P < 0. 01 over Fz electrode). Postmovement contralateral sensorimotor 18–22 Hz ERS was significantly lower in fatigue MS patients compared with normal subjects (P < 0. 005) and with nonfatigue MS patients (P = 0. 02). These findings are consistent with a central origin of fatigue in MS and indicate cortical dysfunction even during a simple motor task, resulting in hyperactivity during movement execution and failure of the inhibitory mechanisms intervening after movement termination.

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