A Novel Workflow for Electrophysiology Studies in Patients with Brugada Syndrome.

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the 'type 1' ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

Functional connectome of arousal and motor brainstem nuclei in living humans by 7 Tesla resting-state fMRI.

Brainstem nuclei play a pivotal role in many functions, such as arousal and motor control. Nevertheless, the connectivity of arousal and motor brainstem nuclei is understudied in living humans due to the limited sensitivity and spatial resolution of conventional imaging, and to the lack of atlases of these deep tiny regions of the brain. For a holistic comprehension of sleep, arousal and associated motor processes, we investigated in 20 healthy subjects the resting-state functional connectivity of 18 arousal and motor brainstem nuclei in living humans. To do so, we used high spatial-resolution 7 Tesla resting-state fMRI, as well as a recently developed in-vivo probabilistic atlas of these nuclei in stereotactic space. Further, we verified the translatability of our brainstem connectome approach to conventional (e.g. 3 Tesla) fMRI. Arousal brainstem nuclei displayed high interconnectivity, as well as connectivity to the thalamus, hypothalamus, basal forebrain and frontal cortex, in line with animal studies and as expected for arousal regions. Motor brainstem nuclei showed expected connectivity to the cerebellum, basal ganglia and motor cortex, as well as high interconnectivity. Comparison of 3 Tesla to 7 Tesla connectivity results indicated good translatability of our brainstem connectome approach to conventional fMRI, especially for cortical and subcortical (non-brainstem) targets and to a lesser extent for brainstem targets. The functional connectome of 18 arousal and motor brainstem nuclei with the rest of the brain might provide a better understanding of arousal, sleep and accompanying motor functions in living humans in health and disease.

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI.

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus - alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.

Optimising Speaker-Dependent Feature Extraction Parameters to Improve Automatic Speech Recognition Performance for People with Dysarthria.

Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the speaker's voice features and the optimal window and shift parameters that minimises the error of an ASR system, for that specific speaker. For our experiments, we used both impaired and unimpaired Italian speech. Specifically, we used 30 speakers with dysarthria from the IDEA database and 10 professional speakers from the CLIPS database. Both databases are freely available. The results confirm that, if a standard ASR system performs poorly with a speaker with dysarthria, it can be improved by using the new speech analysis. Otherwise, the new approach is ineffective in cases of unimpaired and low impaired speech. Furthermore, there exists a correlation between some speaker's voice features and their optimal parameters.

Assessing the Quality of Heart Rate Variability Estimated from Wrist and Finger PPG: A Novel Approach Based on Cross-Mapping Method.

The non-invasiveness of photoplethysmographic (PPG) acquisition systems, together with their cost-effectiveness and easiness of connection with IoT technologies, is opening up to the possibility of their widespread use. For this reason, the study of the reliability of PPG and pulse rate variability (PRV) signal quality has become of great scientific, technological, and commercial interest. In this field, sensor location has been demonstrated to play a crucial role. The goal of this study was to investigate PPG and PRV signal quality acquired from two body locations: finger and wrist. We simultaneously acquired the PPG and electrocardiographic (ECG) signals from sixteen healthy subjects (aged 28.5 ± 3.5, seven females) who followed an experimental protocol of affective stimulation through visual stimuli. Statistical tests demonstrated that PPG signals acquired from the wrist and the finger presented different signal quality indexes (kurtosis and Shannon entropy), with higher values for the wrist-PPG. Then we propose to apply the cross-mapping (CM) approach as a new method to quantify the PRV signal quality. We found that the performance achieved using the two sites was significantly different in all the experimental sessions (p < 0.01), and the PRV dynamics acquired from the finger were the most similar to heart rate variability (HRV) dynamics.

A Cross-Correlational Analysis between Electroencephalographic and End-Tidal Carbon Dioxide Signals: Methodological Issues in the Presence of Missing Data and Real Data Results.

Electroencephalographic (EEG) irreducible artifacts are common and the removal of corrupted segments from the analysis may be required. The present study aims at exploring the effects of different EEG Missing Data Segment (MDS) distributions on cross-correlation analysis, involving EEG and physiological signals. The reliability of cross-correlation analysis both at single subject and at group level as a function of missing data statistics was evaluated using dedicated simulations. Moreover, a Bayesian-based approach for combining the single subject results at group level by considering each subject's reliability was introduced. Starting from the above considerations, the cross-correlation function between EEG Global Field Power (GFP) in delta band and end-tidal CO2 (PETCO2) during rest and voluntary breath-hold was evaluated in six healthy subjects. The analysis of simulated data results at single subject level revealed a worsening of precision and accuracy in the cross-correlation analysis in the presence of MDS. At the group level, a large improvement in the results' reliability with respect to single subject analysis was observed. The proposed Bayesian approach showed a slight improvement with respect to simple average results. Real data results were discussed in light of the simulated data tests and of the current physiological findings.

Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study.

Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, F0, and its changes. F0-related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean F0 from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to F0 variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.

Radiofrequency coils for magnetic resonance applications: theory, design, and evaluation.

Magnetic resonance imaging and magnetic resonance spectroscopy are noninvasive diagnostic techniques based on the phenomenon of nuclear magnetic resonance. Radiofrequency coils are key components in both the transmission and receiving phases of magnetic resonance systems. Transmitter coils have to produce a highly homogeneous alternating field in a wide field of view, whereas receiver coils have to maximize signal detection while minimizing noise. Development of modern magnetic resonance coils often is based on numerical methods for simulating and predicting coil performance. Numerical methods allows the behavior of the coil in the presence of realistic loads to be simulated and the coil's efficiency at high magnetic fields to be investigated. After being built, coils have to be characterized in the laboratory to optimize their setting and performance by extracting several quality indices. Successively, coils performance has to be evaluated in a scanner using standardized image quality parameters with phantom and human experiments. This article reviews the principles of radiofrequency coils, coil performance parameters, and their estimation methods using simulations, workbench, and magnetic resonance experiments. Finally, an overview of future developments in radiofrequency coils technology is included.

Exploring Emotion Control and Alexithymia in Autistic Adults: An Ecological Momentary Assessment Study.

Difficulties in controlling emotions - a proxy for emotion dysregulation (ED)-and difficulties in expressing feelings in words-'absence of emotion labelling' or alexithymia-co-exist in autism and contribute to elevated levels of impulsive and suicidal behaviour. To date, studies linking the two phenomena have relied on retrospective self-reported measures, lacking support for generalizability to real-life situations. The present study investigated in vivo emotion labelling and its impact on emotion control in 29 autistic adults without intellectual disability (ASC) and 28 neurotypical (NT) individuals of similar age, sex, and educational level. Participants were trained in an Ecological Momentary Assessment (EMA) to label their emotions, the arousal dimension, and their emotion control via smartphone over a one-week period. Findings showed that the ASC group experienced more instances of 'having an emotion that I cannot name' and, when they were able to label their emotions, they reported higher rates of negative and conflicting (simultaneously positive and negative) emotions. In both groups, the absence of emotion labelling, and intense negative emotions were associated with impaired emotion control. However, the association between lack of emotional awareness-'I have no emotion'-and impaired emotion control was only evident in ASC individuals. Our study highlights a nuanced facet of emotional processing in the ASC population. Further research is needed to gain a deeper understanding of the complex relationship between ED and alexithymia in autism.

Emotion perception through the nose: how olfactory emotional cues modulate the perception of neutral facial expressions in affective disorders.

Humans can decode emotional states from the body odors of the conspecifics and this type of emotional communication is particularly relevant in conditions in which social interactions are impaired, as in depression and social anxiety. The present study aimed to explore how body odors collected in happiness and fearful conditions modulate the subjective ratings, the psychophysiological response and the neural processing of neutral faces in individuals with depressive symptoms, social anxiety symptoms, and healthy controls (N = 22 per group). To this aim, electrocardiogram (ECG) and HD-EEG were recorded continuously. Heart Rate Variability (HRV) was extracted from the ECG as a measure of vagal tone, event-related potentials (ERPs) and event-related spectral perturbations (ERPSs) were extracted from the EEG. The results revealed that the HRV increased during the fear and happiness body odors conditions compared to clean air, but no group differences emerged. For ERPs data, repeated measure ANOVA did not show any significant effects. However, the ERPSs analyses revealed a late increase in delta power and a reduced beta power both at an early and a late stage of stimulus processing in response to the neutral faces presented with the emotional body odors, regardless of the presence of depressive or social anxiety symptoms. The current research offers new insights, demonstrating that emotional chemosignals serve as potent environmental cues. This represents a substantial advancement in comprehending the impact of emotional chemosignals in both individuals with and without affective disorders.

A modular framework for multi-scale tissue imaging and neuronal segmentation.

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.

Report from a Tibetan Monastery: EEG neural correlates of concentrative and analytical meditation.

The positive effects of meditation on human wellbeing are indisputable, ranging from emotion regulation improvement to stress reduction and present-moment awareness enhancement. Changes in brain activity regulate and support these phenomena. However, the heterogeneity of meditation practices and their cultural background, as well as their poor categorization limit the generalization of results to all types of meditation. Here, we took advantage of a collaboration with the very singular and precious community of the Monks and Geshes of the Tibetan University of Sera-Jey in India to study the neural correlates of the two main types of meditation recognized in Tibetan Buddhism, namely concentrative and analytical meditation. Twenty-three meditators with different levels of expertise underwent to an ecological (i.e., within the monastery) EEG acquisition consisting of an analytical and/or concentrative meditation session at "their best," and with the only constraint of performing a 5-min-long baseline at the beginning of the session. Time-varying power-spectral-density estimates of each session were compared against the baseline (i.e., within session) and between conditions (i.e., analytical vs. concentrative). Our results showed that concentrative meditation elicited more numerous and marked changes in the EEG power compared to analytical meditation, and mainly in the form of an increase in the theta, alpha and beta frequency ranges. Moreover, the full immersion in the Monastery life allowed to share the results and discuss their interpretation with the best scholars of the Monastic University, ensuring the identification of the most expert meditators, as well as to highlight better the differences between the different types of meditation practiced by each of them.

The dynamics of EEG gamma responses to unpleasant visual stimuli: from local activity to functional connectivity.

Many electroencephalographic (EEG) studies on the cortical dynamics induced by unpleasant picture viewing demonstrated the modulation of event-related potentials (ERPs) components as a function of valence and the increase of gamma band responses to emotional stimuli; while only a few studies investigated phase synchronization phenomena such as inter-trial or between regions phase locking of gamma responses to emotional stimulation. The aim of this study was to provide a complete description of the cortical dynamics induced by unpleasant and neutral pictures viewing, from the ERP averages to gamma rhythm modulation, and its phase synchronization. Gamma rhythm modulation was estimated by the event-related synchronization (ERS) approach, and phase synchrony between trials and between cortical regions was studied by extending the phase-locking statistics (PLS) approach. Consistent with previous literature, an increase in P300 and late positive potential and an increase in gamma activity during viewing of unpleasant pictures as compared to neutral ones were found. No inter-trial synchronization was evoked by the stimuli, whereas widespread phase locking between sites was identified. In particular, differences in gamma synchronization between unpleasant and neutral stimuli were found. Specifically, at early (0-250 ms) lags from stimulus onset, in the 38-45 Hz gamma interval, stronger inter-site synchronizations for the unpleasant stimuli, even though quite widespread across the scalp, mainly involved the interhemispheric synchronization between temporal and frontal regions. In contrast, in the 30-37 Hz gamma interval, stronger synchronizations for the responses to neutral trials were found in the 500-750 time interval, mainly involving the temporo-parietal regions. These findings suggest that the full elaboration of unpleasant stimuli requires a tight interhemispheric communication between temporal and frontal regions that is realized by means of phase synchronization at about 40 Hz. In addition, in contrast with the idea of a broadband modulation of high-frequency activity by cognitive/emotional stimuli, the present findings i.e. stronger BRS responses to either emotional or neutral trials at specific frequency and time range, indicate that specific intervals of gamma activity could be each primarily involved in a specific aspect of stimulus processing.

A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis.

BACKGROUND AND OBJECTIVE: The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified. METHODS: We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels' generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers. RESULTS: According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works. CONCLUSIONS: The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis.

Analysis, comparison and representation of occupational exposure to a static magnetic field in a 3-T MRI site.

The purpose of this study is to analyze exposure to the time-varying magnetic field caused by worker movements in a 3-T clinical magnetic resonance imaging (MRI) scanner. Measurements of the static magnetic field (B) in the proximity of the MRI scanner were performed to create a detailed map of the spatial gradient of B, in order to indicate the areas at high risk of exposure. Moreover, a personal exposure recording system was used in order to analyze and compare exposure to the static magnetic field during different routine procedures in MRI. We found that for all of the performed work activities, exposure was compliant with International Commission on Non-Ionizing Radiation Protection levels. However, our findings confirm that there is great variability of exposure between different workers and suggest the importance of performing personal exposure measurements and of detailed knowledge of the magnetic field spatial distribution.

Human body odors of happiness and fear modulate the late positive potential component during neutral face processing: a preliminary ERP study on healthy subjects.

Human body odors (HBOs) are powerful stimuli that can affect emotional, cognitive and behavioral processes. However, the characterization of the physiological response to HBOs is still to be fully investigated. Here, we analyzed the self-assessed emotion perception and the EEG event-related potentials (ERP) on 17 healthy young women during a simultaneous visual-olfactory stimulation. Particularly, we evaluated the effect of happiness and fear HBO on the amplitude of ERP waveforms elicited by neutral face processing. In addition, we evaluated the subjective valence and arousal perception of the presented neutral faces by means of the self-assessment-manikin test. We observed a significant increase in the amplitude of the late positive potential (LPP) for central left sites (i.e., C3) during the administration of HBOs with respect to clean air. On the other hand, we did not observe any significant change in the subjective valence and arousal scores as well as for the early components of the ERP (i.e., P100, N170, Vertex-Positive-Potential). Our preliminary results suggest that fear and happiness HBO can induce a protracted increase in the LPP, and possibly reflect an automatic and sustained engagement with emotionally significant content.

A novel tool for estimation of magnetic resonance occupational exposure to spatially varying magnetic fields.

OBJECT: Staff operating in the environment of magnetic resonance imaging (MRI) scanners are exposed daily to static magnetic fields (MFs). To protect workers several guidelines are present in literature reporting exposure limits values expressed in terms of magnetic flux density or induced current density. We present here a novel tool for estimating the induced current density due to worker movement in the MR environment. MATERIALS AND METHODS: A Matlab script was created to estimate the induced current density J due to operator movements along a chosen walking path. RESULTS: The induced current density associated with any worker's movements during MR procedures is dependent on the walking speed and on the spatial gradient fields associated with a specific path. Some examples of possible worker paths were considered here for a 3 T MR scanner and a maximum value of 160 cm/s walking speed. CONCLUSION: This tool permits one to find exposure level for specific worker walking path and speed; it can be used as assessment tool in any MRI centre and for workers safety education. It is valid for any kind of commercial scanner because it requires only the knowledge of the MR scanner room map with isogauss lines.

Reliability of Pulse Rate Variability in Elderly Men and Women: an Application of Cross-Mapping Approach.

Photoplethysmography (PPG) is a completely noninvasive, optical method of assessing blood flow dynamics in peripheral vasculature. Wearable devices for PPG recording are becoming increasingly popular, due to their cost-effectiveness and ease of use. For these reasons, many recent scientific studies have proposed the use of pulse rate variability (PRV) extracted from PPG as a surrogate for heart rate variability (HRV), in monitoring autonomic activity and cardiovascular health.In this work, we used a cross-mapping approach, a methodology based on chaos theory, to compare PRV and HRV dynamics, and investigate their agreement according to age and gender of healthy subjects. We used ECG and PPG data acquired from 57 subjects (41 young and 16 elderly) during resting state in the supine position. Signals were gathered from the publicly available VORTAL dataset. Our results showed a statistically significant decrease of PRV reliability as an HRV surrogate in old participants, which was confirmed as significant when only men subjects were analyzed (p-value<0.01).Our findings, although preliminary, suggest greater caution in the use of PPG devices for monitoring cardiovascular health, especially in elderly men.

Odor valence modulates cortico-cortical interactions: a preliminary study using DCM for EEG.

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity.

Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder.

There is a lack of consensus on the diagnostic thresholds that could improve the detection accuracy of bipolar mixed episodes in clinical settings. Some studies have shown that voice features could be reliable biomarkers of manic and depressive episodes compared to euthymic states, but none thus far have investigated whether they could aid the distinction between mixed and non-mixed acute bipolar episodes. Here we investigated whether vocal features acquired via verbal fluency tasks could accurately classify mixed states in bipolar disorder using machine learning methods. Fifty-six patients with bipolar disorder were recruited during an acute episode (19 hypomanic, 8 mixed hypomanic, 17 with mixed depression, 12 with depression). Nine different trials belonging to four conditions of verbal fluency tasks-letter, semantic, free word generation, and associational fluency-were administered. Spectral and prosodic features in three conditions were selected for the classification algorithm. Using the leave-one-subject-out (LOSO) strategy to train the classifier, we calculated the accuracy rate, the F1 score, and the Matthews correlation coefficient (MCC). For depression versus mixed depression, the accuracy and F1 scores were high, i.e., respectively 0.83 and 0.86, and the MCC was of 0.64. For hypomania versus mixed hypomania, accuracy and F1 scores were also high, i.e., 0.86 and 0.75, respectively, and the MCC was of 0.57. Given the high rates of correctly classified subjects, vocal features quickly acquired via verbal fluency tasks seem to be reliable biomarkers that could be easily implemented in clinical settings to improve diagnostic accuracy.