Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Paesaggi di casa. Avvertire i luoghi dell'abitare

Indagine sul paesaggio e iluoghi domestici come interrogazione pluridisciplinare sull'abitare, sulla corporeità e sulla soggettività, in ottiche molteplici, con rimandi complessi alla realtà e a contesto epocale

Bonesio Luisa, Luoghi e forme

Pianificazione, valorizzazione e tutela dei luoghi nella loro natura di paesaggi culturali e luoghi di vita dopo la Convenzione Europea del Paesaggio. Sobrietà, nuovi modelli di territorializzazione. Identità, comunità di luogo, paesaggio come identità locale nel contesto della globalizzazione

Paesaggio: l'anima dei luoghi

I vari contributi del volume costituiscono una mappa di teorie, posizioni, stili d'indagine e approcci progettuali che restituiscono la complessa trama di realtà paesaggistiche lacerate, ma anche la consapevolezza collettiva del significato dell'abitare, della memoria, delle identità condivise e dei progetti sostenibili di futuro. Una riscoperta dell'anima dei luoghi come responsabilità di un'etica pubblica, itionerario formativo ma anche ripensamento dei paradigmi che hanno condotto alla devastazione dei paesaggi, allo sfiguramento delle città, alla dilapidazione di un patrimonio culturale irripetibile e dell'identità civile della nazione

Transgenic Fatal Familial Insomnia Mice Indicate Prion Infectivity-Independent Mechanisms of Pathogenesis and Phenotypic Expression of Disease

<div><p>Fatal familial insomnia (FFI) and a genetic form of Creutzfeldt-Jakob disease (CJD¹⁷⁸) are clinically different prion disorders linked to the D178N prion protein (PrP) mutation. The disease phenotype is determined by the 129 M/V polymorphism on the mutant allele, which is thought to influence D178N PrP misfolding, leading to the formation of distinctive prion strains with specific neurotoxic properties. However, the mechanism by which misfolded variants of mutant PrP cause different diseases is not known. We generated transgenic (Tg) mice expressing the mouse PrP homolog of the FFI mutation. These mice synthesize a misfolded form of mutant PrP in their brains and develop a neurological illness with severe sleep disruption, highly reminiscent of FFI and different from that of analogously generated Tg(CJD) mice modeling CJD¹⁷⁸. No prion infectivity was detectable in Tg(FFI) and Tg(CJD) brains by bioassay or protein misfolding cyclic amplification, indicating that mutant PrP has disease-encoding properties that do not depend on its ability to propagate its misfolded conformation. Tg(FFI) and Tg(CJD) neurons have different patterns of intracellular PrP accumulation associated with distinct morphological abnormalities of the endoplasmic reticulum and Golgi, suggesting that mutation-specific alterations of secretory transport may contribute to the disease phenotype.</p></div

Tg(FFI) mice show an altered response to sleep deprivation.

<p>Time course of the loss and recovery of time spent in rapid eye movement (REM) (A) and non-rapid eye movement (NREM) (B) sleep, during and after sleep deprivation. Values were from 8 non-Tg/<i>Prnp</i>^+/+, 10 non-Tg/<i>Prnp</i>^0/0, 9 Tg(FFI-26)/<i>Prnp</i>^0/0 and 8 Tg(FFI-26)/<i>Prnp</i>^+/0. Mice were kept awake during the first 6 h of the light phase (crosshatched bar) by gentle handling, and allowed to sleep freely in the next 18 h. The black bar indicates the dark portion of the light-dark cycle. REM and NREM sleep were calculated hourly for each animal as the difference between the amount of time spent in a given state (REM or NREM sleep) during and after sleep deprivation, and the amount spent in the corresponding hour during baseline conditions (undisturbed). The hour-by-hour differences were then summed to obtain a cumulative curve. Data (means ± SEM) are presented in 2-h intervals. Single symbols: p < 0.05; double symbols: p < 0.01. *, Tg(FFI-26)/<i>Prnp</i>^0/0 vs non-Tg/<i>Prnp</i>^0/0; °, Tg(FFI-26)/<i>Prnp</i>^0/0 vs. non-Tg/<i>Prnp</i>^+/+; §, Tg(FFI-26)/<i>Prnp</i>^0/0 vs. Tg(FFI-26)/<i>Prnp</i>^+/0; #, Tg(FFI-26)/<i>Prnp</i>^+/0 vs. non-Tg/<i>Prnp</i>^+/+. A mixed model analysis of variance for repeated measures was done on 6 h blocks. Between-strains post-hoc comparisons by one-way ANOVA with Bonferroni correction: (panel A) 0–6 h: F_3,101 = 4.98, p = 0.003; 7–12 h: F_3,101 = 5.25, p = 0.002; 13–18 h: F_3,101 = 2.88, p = 0.05; 19–24 h: F_3,101 = 3.30, p = 0.023. (panel B) 0–6 h: F_3,101 = 1.01, p = 0.391; 7–12 h: F_3,101 = 1.78, p = 0.156; 13–18 h: F_3,101 = 3.76, p = 0.013; 19–24 h: F_3,101 = 3.97, p = 0.010.</p

Sleep architecture.

<p>Values are the mean ± SEM of 8 non-Tg/<i>Prnp</i>^+/+, 10 non-Tg/<i>Prnp</i>^0/0 mice, 8 Tg(FFI-26)/<i>Prnp</i>^+/0 mice and 9 Tg(FFI-26)/<i>Prnp</i>^0/0 mice. The grey areas indicate the dark portion of the light-dark cycle. *p ≤ 0.05; **p ≤ 0.01 (mixed model for repeated measures followed by between-strain one-way ANOVA with Bonferroni's correction).</p