Embedded Files
What We Do
What We Do
Our research combines machine learning, advanced signal processing and mathematical modeling to analyze multimodal neuroimaging and physiological data, including (f)MRI, M/EEG and peripheral recordings.
Our multimodal integration approach is fundamental to advancing our understanding of the spatiotemporal dynamics of cortical and subcortical brain areas and to characterizing the coupling between the central and peripheral nervous system during both wakefulness and sleep. We investigate this interplay using both advanced laboratory techniques, such as simultaneous EEG-fMRI combined with peripheral recordings, and wearable EEG and peripheral sensors that enable ecological monitoring in real-life conditions. In this framework, the creation of automated algorithms, developed in strong collaboration with clinicians, provides the opportunity for better comprehend brain-disorders co-morbidities with many cardiovascular and metabolic pathologies and identify effective risk factors, with a diagnostic and prognostic value.
We are involved in the development of multivariate approaches (e.g., MVPA, encoding and decoding algorithms) to analyze neuroimaging data in healthy subjects in collaboration with other research topics. Specifically, we use computational modeling (e.g., visual, acoustic and semantic) to obtain exhaustive stimulus descriptions and to understand the brain mechanisms involved in their encoding.
ONGOING PROJECTS
Definition and validation of non-invasive methods for the remote assessment of sympathovagal balance during sleep and wakefulness using wearable devices
Development of models for the tracking and prediction of mental perfomance using central and peripheral wearable devices
Development of innovative signal processing and multimodal integration methods for combined EEG-fMRI recordings, enabling a cortical and subcortical characterization of brain dynamics during wakefulness and sleep
Model Mediated Inter-Subject Correlation in fMRI
Analyze emotions through Natural Language Processing
Who We Are
Who We Are
VALERIE VAN ES
Phd StudentBio-engineer
ALESSANDRA ALGIERI
Phd StudentBio-engineer
MARCO FIORETTI
Phd StudentMathematical engineer
SIMONE CUOZZO
Phd StudentPsychologist
Adrian Onicaș
PhD Student
Valerio Fantozzi
Biomedical Engineering Student (2021-2022)
What We Publish
What We Publish
Thalamic involvement defines distinct slow-wave subtypes in NREM sleepBergamo, Handjaras, Picchioni, Ricciardi, Legendre, Ozbay, de Zwart, Duyn, Bernardi, Betta. Communications Biology , 2026. DOI: 10.18112/openneuro.ds005127.v1.0.4.
Slow waves (0.5–2 Hz) are a key feature of non-rapid-eye-movement (NREM) sleep, traditionally believed to arise from neocortical circuits. However, growing evidence suggests that subcortical structures, particularly the thalamus, may play a crucial role in initiating and synchronizing slow waves. We tested the hypothesis that single slow waves may arise from distinct cortico-cortical and thalamo-cortical mechanisms using simultaneous EEG-fMRI in healthy adults. Spatial mapping based on thalamic fMRI responses revealed two types of slow-waves. A first type (C1) characterized by an early thalamic fMRI-signal increase, corresponded to large, efficiently synchronized waves associated with sleep spindles and with markers of higher arousal and autonomic activation. The second type (C2) is marked by an initial negative fMRI response and corresponds to smaller slow waves potentially resulting from cortico-cortical synchronization. These waves occur more often during phases of stable NREM sleep. These findings highlight distinct slow-wave subtypes with different thalamic involvement and, potentially, synchronization mechanisms.
Predicting acute decompensated heart failure using circadian markers from heart rate time series
Van Es, Van Leunen, de Lathauwer,Verstappen, Tio, Spee, Yuan, Betta, Handjaras, KempsESC Heart Failure , 2025. DOI: 10.1002/ehf2.15395Aims: Hospital admissions for acute decompensated heart failure (ADHF) are linked to high readmission rates, emphasizing the need for early intervention. Dysregulation of the circadian rhythm that regulates key physiological processes, such as heart rate (HR), blood pressure and sleep–wake cycles, may precede weight gain and clinical symptoms of worsening heart failure (HF) by weeks, providing a window for timely intervention. This study aims to develop a predictive algorithm for early and accurate ADHF detection.Methods and results: Sixty-five patients discharged after ADHF hospitalization monitored HR with a wrist-worn device for 6 months after reaching stable HF. Circadian parameters (mesor, amplitude and acrophase) were extracted via cosinor analysis and used to train a long short-term memory neural network. The algorithm analysed 21-day periods before an HF event, defined as unplanned outpatient visits for congestion episode, increased diuretics, ADHF hospitalization or sudden cardiac death. Circadian changes appeared in the 21 days preceding HF events, with elevated mesor (70.6 vs. 73.6 b.p.m.; P < 0.001), reduced amplitude (8.3 vs. 4.9 b.p.m.; P = 0.046) and acrophase shifts (11.3 vs. 12.2 h; P = 0.706). The classification algorithm showed 74% sensitivity, 73% specificity and a 74% AUC (P < 0.001). Amplitude was the strongest predictor, contributing 62% to the algorithm's feature importance.Conclusions: Circadian metrics from a wrist-worn device showed progressive alterations over the 3 weeks preceding ADHF, offering potential early detection of HF decompensation with moderate prediction performance. Future research should refine these metrics and results in larger, diverse populations, using various sensor types and explore early interventions.
Remote monitoring of sympathovagal imbalance during sleep and its implications in cardiovascular risk assessment: a systematic review
van Es, Handjaras, de Lathauwer, Kemps, Handjaras, BettaBioengineering, 2024. DOI: 10.3390/bioengineering11101045Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.
Pulse wave amplitude drops index: a biomarker of cardiovascular risk in obstructive sleep apnea Solelhac , Sánchez, Blanchard, Berger, Hirotsu, Imler, Sánchez-De-La-Torre ..., Betta, Siclari, Barbé, Gagnadoux, Heinzer American journal of respiratory and critical care medicine , 2023. DOI: 10.1164/rccm.202206-1223OC
Rationale: It is currently unclear which patients with obstructive sleep apnea (OSA) are at increased cardiovascular risk.Objective: To investigate the value of pulse wave amplitude drops (PWADs), reflecting sympathetic activations and vasoreactivity, as a biomarker of cardiovascular risk in OSA.Methods: PWADs were derived from pulse oximetry–based photoplethysmography signals in three prospective cohorts: HypnoLaus (N = 1,941), the Pays-de-la-Loire Sleep Cohort (PLSC; N = 6,367), and “Impact of Sleep Apnea syndrome in the evolution of Acute Coronary syndrome. Effect of intervention with CPAP” (ISAACC) (N = 692). The PWAD index was the number of PWADs (>30%) per hour during sleep. All participants were divided into subgroups according to the presence or absence of OSA (defined as ≥15 or more events per hour or <15/h, respectively, on the apnea–hypopnea index) and the median PWAD index. Primary outcome was the incidence of composite cardiovascular events.Measurements and Main Results: Using Cox models adjusted for cardiovascular risk factors (hazard ratio; HR [95% confidence interval]), patients with a low PWAD index and OSA had a higher incidence of cardiovascular events compared with the high-PWAD and OSA group and those without OSA in the HypnoLaus cohort (HR, 2.16 [1.07–4.34], P = 0.031; and 2.35 [1.12–4.93], P = 0.024) and in the PLSC (1.36 [1.13–1.63], P = 0.001; and 1.44 [1.06–1.94], P = 0.019), respectively. In the ISAACC cohort, the low-PWAD and OSA untreated group had a higher cardiovascular event recurrence rate than that of the no-OSA group (2.03 [1.08–3.81], P = 0.028). In the PLSC and HypnoLaus cohorts, every increase of 10 events per hour in the continuous PWAD index was negatively associated with incident cardiovascular events exclusively in patients with OSA (HR, 0.85 [0.73–0.99], P = 0.031; and HR, 0.91 [0.86–0.96], P < 0.001, respectively). This association was not significant in the no-OSA group and the ISAACC cohort.Conclusions:In patients with OSA, a low PWAD index reflecting poor autonomic and vascular reactivity was independently associated with a higher cardiovascular risk.
Betta, Handjaras, Ricciardi, Pietrini, Haba-Rubio, Siclari, Heinzer, BernardiSleep Medicine, 2020. DOI: 10.1016/j.sleep.2019.12.030
Sudden drops in pulse wave amplitude (PWA) measured by finger photoplethysmography (PPG) are known to reflect peripheral vasoconstriction resulting from sympathetic activation. Previous work demonstrated that sympathetic activations during sleep typically accompany the occurrence of pathological respiratory and motor events, and their alteration may be associated with the arising of metabolic and cardiovascular diseases. Importantly, PWA-drops often occur in the absence of visually identifiable cortical micro-arousals and may thus represent a more accurate marker of sleep disruption/fragmentation. In this light, an objective and reproducible quantification and characterization of sleep-related PWA-drops may offer a valuable, non-invasive approach for the diagnostic and prognostic evaluation of patients with sleep disorders. However, the manual identification of PWA-drops represents a time-consuming practice potentially associated with high intra/inter-scorer variability. Since validated algorithms are not readily available for research and clinical purposes, here we present a novel automated approach to detect and characterize significant drops in the PWA-signal. The algorithm was tested against expert human scorers who visually inspected corresponding PPG-recordings. Results demonstrated that the algorithm reliably detects PWA-drops and is able to characterize them in terms of parameters with a potential physiological and clinical relevance, including timing, amplitude, duration and slopes. The method is completely user-independent, processes all-night PSG-data, automatically dealing with potential artefacts, sensor loss/displacements, and stage-dependent variability in PWA-time-series. Such characteristics make this method a valuable candidate for the comparative investigation of large clinical datasets, to gain a better insight into the reciprocal links between sympathetic activity, sleep-related alterations, and metabolic and cardiovascular diseases.
Rampinini, Handjaras, Leo, Cecchetti, Betta, Marotta, Ricciardi, PietriniFrontiers in Human Neuroscience, 2019. DOI: 10.3389/fnhum.2019.00032
Classical studies have isolated a distributed network of temporal and frontal areas engaged in the neural representation of speech perception and production. With modern literature arguing against unique roles for these cortical regions, different theories have favored either neural code-sharing or cortical space-sharing, thus trying to explain the intertwined spatial and functional organization of motor and acoustic components across the fronto-temporal cortical network. In this context, the focus of attention has recently shifted towards specific model fitting, aimed at motor and/or acoustic space reconstruction in brain activity within the language network. Here, we tested a model based on acoustic properties (formants), and one based on motor properties (articulation), where model-free decoding of evoked fMRI activity during perception, imagery and production of vowels had been successful. Results revealed that phonological information organizes around formant structure during perception of vowels; interestingly, such model was reconstructed in a broad temporal region, outside the primary auditory cortex, but also in the pars triangularis of the left inferior frontal gyrus. Conversely, articulatory features were not associated to brain activity in these regions. Overall, our results call for a degree of interdependence based on acoustic information, between the frontal and temporal ends of the language network.
Modality-independent encoding of individual concepts in the left parietal cortex
Handjaras, Leo, Cecchetti, Papale, Lenci, Marotta, Pietrini, RicciardiNeuropsychologia, 2017. DOI: 10.1016/j.neuropsychologia.2017.05.001The organization of semantic information in the brain has been mainly explored through category-based models, on the assumption that categories broadly reflect the organization of conceptual knowledge. However, the analysis of concepts as individual entities, rather than as items belonging to distinct superordinate categories, may represent a significant advancement in the comprehension of how conceptual knowledge is encoded in the human brain.Here, we studied the individual representation of thirty concrete nouns from six different categories, across different sensory modalities (i.e., auditory and visual) and groups (i.e., sighted and congenitally blind individuals) in a core hub of the semantic network, the left angular gyrus, and in its neighboring regions within the lateral parietal cortex. Four models based on either perceptual or semantic features at different levels of complexity (i.e., low- or high-level) were used to predict fMRI brain activity using representational similarity encoding analysis. When controlling for the superordinate component, high-level models based on semantic and shape information led to significant encoding accuracies in the intraparietal sulcus only. This region is involved in feature binding and combination of concepts across multiple sensory modalities, suggesting its role in high-level representation of conceptual knowledge. Moreover, when the information regarding superordinate categories is retained, a large extent of parietal cortex is engaged. This result indicates the need to control for the coarse-level categorial organization when performing studies on higher-level processes related to the retrieval of semantic information.
What We Develop
What We Develop
Detection and classification of Eye Movements, and removal of ocular artifacts from EEG
fMRI encoding/decoding algorithms
Our Talks
Our Talks
Betta - EEG-fMRI in sleep. Refine Workshop: "Studio dell’accoppiamento neurovascolare durante la veglia ed il sonno nelle epilessie generalizzate genetiche: un nuovo approccio di endofenotipizzazione per la correlazione genotipo-fenotipo”, Modena, Italy, 2025.
Betta - Evoluzione spazio-temporale dell'attività emodinamica corticale e sottocorticale associata alle onde lente del sonno NREM. Congresso Nazionale di Neurofisiologia Clinica, Rome, Italy, 2021.
Betta - Human sleep slow waves are associated with traveling hemodynamic waves at cortical level . 25th Congress of the European Sleep Research Society, Virtual Congress, 2020.
Betta - Hemodynamic cortical and subcortical activity underlying human sleep Slow Waves. Conference of the Italian Society of Psychophysiology and Cognitive Neuroscience, Ferrara, Italy, 2019.
Handjaras - Gradient-based analysis of cortical topography using fMRI. Satellite event of the Annual Conference of the Italian Society of Psychophysiology and Cognitive Neuroscience, Ferrara, Italy, 2019.
Funding
Funding
PROGETTO PRIN PNRR
Title : "Applying closed-loop acoustic stimulation during sleep to reduce cortical excitability and epileptic activity in focal epilepsy"
Head of Unit: Monica Betta
Funded by the European Union – NextGenerationEU as part of the National Recovery and Resilience Plan, Mission 4, Component 2 – Investment 1.1
Page updated
Report abuse