Schizophrenia classification using machine learning on resting state EEG signal

Schizophrenia is a severe mental disorder associated with a wide spectrum of cognitive and neurophysiological dysfunctions. Early diagnosis is still difficult and based on the manifestation of the disorder. In this study, we have evaluated whether machine learning techniques can help in the diagnosis of schizophrenia, and proposed a processing pipeline in order to obtain machine learning classifiers of schizophrenia based on resting state EEG data. We have computed well-known linear and non-linear measures on sliding windows of the EEG data, selected those measures which better differentiate between patients and healthy controls, and combined them through principal component analysis. These components were finally used as features in five standard machine learning algorithms: k-nearest neighbours (kNN), logistic regression (LR), decision trees (DT), random forest (RF) and support vector machines (SVM). Complexity measures showed a high level of ability in differentiating schizophrenia patients from healthy controls. These differences between groups were mainly located in a delimited zone of the right brain hemisphere, corresponding to the opercular area and the temporal pole. Based on the area under the curve parameter in receiver operating characteristic curve analysis, we obtained high classification power in almost all of the machine learning algorithms tested: SVM (0.89), RF (0.87), LR (0.86), kNN (0.86) and DT (0.68). Our results suggest that the proposed processing pipeline on resting state EEG data is able to easily compute and select a set of features which allow standard machine learning algorithms to perform very efficiently in differentiating schizophrenia patients from healthy subjects.Spanish Government European Commission PID2019-105145RB-I00 MCIN/AEI/10.13039/50110001103

Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease

Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD).We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PDProject PID2019-105145RB-I00Spanish Government (MCIN/AEI/10.13039/501100011033)Italian Ministry of Health—(Ricerca Corrente 2022–2024)National PhD in Artificial Intelligence, XXXVII cycleHealth and life sciences, organized by Università Campus Bio-Medico di Rom

Tellurite-squarate driven assembly of a new family of nanoscale clusters based on (Mo2O2S2)2+

We report the preparation and characterization of a new family of four polyoxothiometalate (POTM) clusters, with varying size and complexity, based upon the dimeric [Mo2O2S2(H2O)6]2+ with the general formula (NMe4)aKb[(Mo2O2S2)c(TeO4)d(C4O4)e(OH)f] where a,b,c,d,e,f = {1,7,14,2,4,10} = 1, {Mo28Te2}; {2,26,36,4,10,48} = 2, {Mo72Te12}; {0,11,15,3,3,21} = 3, {Mo30Te3}; {2,6,12,2,4,16} = 4, {Mo24Te2}. The incorporation of tellurite anions allowed the fine tuning of the templation and bridging of the available building blocks leading to new topologies of increased complexity. The structural diversity of this family of compound, ranges from the highly symmetrical cross-shaped {Mo24Te2} to the stacked ring structure of {Mo72Te12} which is the largest chalcogen-containing POTM cluster reported so far. Also a detailed experimental analysis revealed that the pH isolation window extends form acidic to basic values. ESI-MS analyses not only confirmed the stability of this family in solution but also revealed the stability of the observed virtual building blocks

Spontaneous formation of a chiral (Mo2O2S2)2+-based cluster driven by dimeric {Te2O6}-based templates

Utilization of [Mo2S2O2(H2O)6]2+ and a tellurite anion led to the formation of three new clusters, 1–3, with unique structural features. The tellurite anion not only templated the formation of [(Mo2O2S2)4(TeO3)(OH)9]3− 1 and [(Mo2O2S2)12(TeO3)4(TeO4)2 (OH)18]10− 3, but also the in situ generation of two different types of dimeric {Te2O6} based moieties induced the spontaneous assembly of the chiral [(Mo2O2S2)10(TeO3)(Te2O6)2(OH)18]8− anionic cluster, 2

Caracterització de Crostes. Evolució de la impedància mecànica respecte la humitat del sòl

El treball final de carrera següent pretén analitzar la relació entre la resistència mecànica a la penetració i la humitat del sòl de les crostes generades. Els sòls al rebre l’impacte de les gotes de pluja i/o per la saturació d’aigua tenen tendència a formar una crosta superficial amb el seu assecament. Aquest procés és degut al col·lapse dels agregats. La formació d’encrostament superficial influeix en gran part en l’emergència de les plantes i la infiltració de l’aigua al sòl per això és interessant analitzar el seu comportament. Per analitzar la relació entre la resistència mecànica a la penetració i la humitat, s’ha cregut oportú treballar amb dos mètodes diferents de creació de crostes per poder analitzar i comparar els resultats, així com mesures de camp, on la creació de crostes esdevé un fet natural. La creació de crostes a laboratori es durà a terme a partir dels següents mètodes: pasta saturada i ascens capil·lar. Es treballarà amb mostres de sòl procedents de Viladecans (Agròpolis) i de Caldes de Montbui (Torre Marimon). Les crostes es crearan en anelles de 2,5cm de radi i 1cm d’alçada. Amb l’objectiu d’analitzar la resistència mecànica a la penetració respecte la humitat, es realitzaran diferents lectures des del punt de saturació d’aigua fins al seu assecament. Per a cada punt de lectura es realitzaran 5 rèpliques. La mesura de la resistència mecànica a la penetració s’analitzarà amb el texturòmetre “TAXTplus”, amb el penetròmetre de mà per a capes superiors de tipus IB per les mesures realitzades al camp. La humitat es determina sobre pes sec i pesant la mostra després de ser penetrada i on cop assecada durant 24h a 105ºC. Les mesures a camp serviran per tenir una referència de les condicions reals de camp i determinar quin és el mètode més fiable. Respecte a la relació de la impedància mecànica amb la humitat, s’espera obtenir resultats molt baixos en humitats altes on la crosta no estarà formada i resultats més elevats en humitats baixes on la crosta del sòl ja s’haurà format. Finalment s’analitzarà l’efecte de la qualitat de l’aigua per establir si aquesta pot tenir una influència en la formació de crostes