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Silvia Basaia

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9 papers

1 author row

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 2025 Journal Article

Multi-Center 3D CNN for Parkinson’s disease diagnosis and prognosis using clinical and T1-weighted MRI data

Silvia Basaia
Elisabetta Sarasso
Francesco Sciancalepore
Roberta Balestrino
Simona Musicco
Stefano Pisano
Iva Stankovic
Aleksandra Tomic

OBJECTIVE: Parkinson's disease (PD) presents challenges in early diagnosis and progression prediction. Recent advancements in machine learning, particularly convolutional-neural-networks (CNNs), show promise in enhancing diagnostic accuracy and prognostic capabilities using neuroimaging data. The aims of this study were: (i) develop a 3D-CNN based on MRI to distinguish controls and PD patients and (ii) employ CNN to predict the progression of PD. METHODS: Three cohorts were selected: 86 mild, 62 moderate-to-severe PD patients, and 60 controls; 14 mild-PD patients and 14 controls from Parkinson's Progression Markers Initiative database, and 38 de novo mild-PD patients and 38 controls. All participants underwent MRI scans and clinical evaluation at baseline and over 2-years. PD subjects were classified in two clusters of different progression using k-means clustering based on baseline and follow-up UDPRS-III scores. A 3D-CNN was built and tested on PD patients and controls, with binary classifications: controls vs moderate-to-severe PD, controls vs mild-PD, and two clusters of PD progression. The effect of transfer learning was also tested. RESULTS: CNN effectively differentiated moderate-to-severe PD from controls (74% accuracy) using MRI data alone. Transfer learning significantly improved performance in distinguishing mild-PD from controls (64% accuracy). For predicting disease progression, the model achieved over 70% accuracy by combining MRI and clinical data. Brain regions most influential in the CNN's decisions were visualized. CONCLUSIONS: CNN, integrating multimodal data and transfer learning, provides encouraging results toward early-stage classification and progression monitoring in PD. Its explainability through activation maps offers potential for clinical application in early diagnosis and personalized monitoring.

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YNICL Journal 2022 Journal Article

Neurogenetic traits outline vulnerability to cortical disruption in Parkinson’s disease

Silvia Basaia
Federica Agosta
Ibai Diez
Elisenda Bueichekú
Federico d'Oleire Uquillas
Manuel Delgado-Alvarado
César Caballero-Gaudes
MariCruz Rodriguez-Oroz

The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.

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YNICL Journal 2021 Journal Article

Impaired recognition of disgust in amyotrophic lateral sclerosis is related to basal ganglia involvement

Veronica Castelnovo
Elisa Canu
Maria Antonietta Magno
Silvia Basaia
Nilo Riva
Barbara Poletti
Vincenzo Silani
Massimo Filippi

In the present study we investigated emotion recognition in pure motor amyotrophic lateral sclerosis (ALS) patients and its relationship with the integrity of basal ganglia, hippocampus and amygdala. Twenty ALS patients without either cognitive or behavioural impairment, and 52 matched healthy controls performed a neuropsychological assessment including the Comprehensive Affect Testing System (CATS) investigating emotion recognition. All participants underwent also a 3T brain MRI. Volumes of basal ganglia, hippocampus and amygdala bilaterally were measured using FIRST in FSL. Sociodemographic, cognitive and MRI data were compared between groups. In ALS patients, correlations between CATS significant findings, brain volumes, cognition, mood and behaviour were explored. ALS patients showed altered performances at the CATS total score and, among the investigated emotions, patients were significantly less able to recognize disgust compared with controls. No brain volumetric differences were observed between groups. In ALS patients, a lower performance in disgust recognition was related with a reduced volume of the left pallidum and a lower performance on the Edinburgh Cognitive and Behavioural ALS Screen. Cognitively/behaviourally unimpaired ALS patients showed impaired disgust recognition, which was associated with pallidum volume. The association with cognitive alterations may suggest impaired disgust recognition as an early marker of cognitive decline.

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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 2020 Journal Article

Functional brain connectome in posterior cortical atrophy

Raffaella Migliaccio
Federica Agosta
Silvia Basaia
Camilla Cividini
Marie-Odile Habert
Aurélie Kas
Maxime Montembeault
Massimo Filippi

This study investigated the functional brain connectome architecture in patients with Posterior Cortical Atrophy (PCA). Eighteen PCA patients and 29 age- and sex- matched healthy controls were consecutively recruited in a specialized referral center. Participants underwent neurologic examination, cerebrospinal fluid (CSF) examination for Alzheimer's disease (AD) biomarkers, cognitive assessment, and brain MRI. For a smaller subset of participants, FDG-PET examination was available. We assessed topological brain network properties and regional functional connectivity as well as intra- and inter-hemispheric connectivity, using graph analysis and connectomics. Supplementary analyses were performed to explore the association between the CSF AD profile and the connectome status, and taking into account hypometabolic, atrophic, and spared regions (nodes). PCA patients showed diffuse functional connectome alterations at both global and regional level, as well as a connectivity breakdown between the posterior brain nodes. They had a widespread loss of both intra- and inter-hemispheric connections, exceeding the structural damage, and including the frontal connections. In PCA, connectome alterations were identified in all the brain nodes irrespectively of their structural and metabolic classification and were associated with a connectivity breakdown between damaged and spared areas. Taken together, these findings suggest the potentially high sensitivity of graph-analysis and connectomic in capturing the progression and maybe early signs of neurodegeneration in PCA patients.

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YNICL Journal 2020 Journal Article

Progression of brain functional connectivity and frontal cognitive dysfunction in ALS

Veronica Castelnovo
Elisa Canu
Davide Calderaro
Nilo Riva
Barbara Poletti
Silvia Basaia
Federica Solca
Vincenzo Silani

OBJECTIVE: To investigate the progression of resting-state functional connectivity (rs-FC) changes in patients with amyotrophic lateral sclerosis (ALS) and their relationship with frontal cognitive alterations. METHODS: This is a multicentre, observational and longitudinal study. At baseline and after six months, 25 ALS patients underwent 3D T1-weighted MRI, resting-state functional MRI (rs-fMRI), and the computerized Test of Attentional Performance (TAP). Using independent component analysis, rs-FC changes of brain networks involving connections to frontal lobes and their relationship with baseline cognitive scores and cognitive changes over time were assessed. With a seed-based approach, rs-FC longitudinal changes of the middle frontal gyrus (MFG) were also explored. RESULTS: After six months, ALS patients showed an increased rs-FC of the left anterior cingulate, left middle frontal gyrus (MFG) and left superior frontal gyrus within the frontostriatal network, and of the left MFG, left supramarginal gyrus and right angular gyrus within the left frontoparietal network. Within the frontostriatal network, a worse baseline performance at TAP divided attention task was associated with an increased rs-FC over time in the left MFG and a worse baseline performance at the category fluency index was related with increased rs-FC over time in the left frontal superior gyrus. After six months, the seed-based rs-FC analysis of the MFG with the whole brain showed decreased rs-FC of the right MFG with frontoparietal regions in patients compared to controls. CONCLUSIONS: Rs-FC changes in ALS patients progressed over time within the frontostriatal and the frontoparietal networks and are related to frontal-executive dysfunction. The MFG seems a potential core region in the framework of a frontoparietal functional breakdown, which is typical of frontotemporal lobar degeneration. These findings offer new potential markers for monitoring extra-motor progression in ALS.

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YNICL Journal 2020 Journal Article

Progressive brain atrophy and clinical evolution in Parkinson’s disease

Massimo Filippi
Elisabetta Sarasso
Noemi Piramide
Tanja Stojkovic
Iva Stankovic
Silvia Basaia
Andrea Fontana
Aleksandra Tomic

Clinical manifestations and evolution are very heterogeneous among individuals with Parkinson's disease (PD). The aims of this study were to investigate the pattern of progressive brain atrophy in PD according to disease stage and to elucidate to what extent cortical thinning and subcortical atrophy are related to clinical motor and non-motor evolution. 154 patients at different PD stages were assessed over time using motor, non-motor and structural MRI evaluations for a maximum of 4 years. Cluster analysis defined clinical subtypes. Cortical thinning and subcortical atrophy were assessed at baseline in patients relative to 60 healthy controls. Longitudinal trends of brain atrophy progression were compared between PD clusters. The contribution of brain atrophy in predicting motor, non-motor, cognitive and mood deterioration was explored. Two main PD clusters were defined: mild (N = 87) and moderate-to-severe (N = 67). Two mild subtypes were further identified: mild motor-predominant (N = 43) and mild-diffuse (N = 44), with the latter group being older and having more severe non-motor and cognitive symptoms. The initial pattern of brain atrophy was more severe in patients with moderate-to-severe PD. Over time, mild-diffuse PD patients had the greatest brain atrophy accumulation in the cortex and the left hippocampus, while less distributed atrophy progression was observed in moderate-to-severe and mild motor-predominant patients. Baseline and 1-year cortical thinning was associated with long-term progression of motor, cognitive, non-motor and mood symptoms. Cortical and subcortical atrophy is accelerated early after the onset of PD and becomes prominent in later stages of disease according to the development of cognitive, non-motor and mood dysfunctions. Structural MRI may be useful for monitoring and predicting disease progression in PD.

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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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