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The aim of this study is to investigate the selective epigenetic modifications and their effect on brain's morphology and functionality in the frontotemporal dementia behavioral variant and bipolar disorder. The open-label, multicentric, interventional case-control study involves the analysis of 3 separate cohorts of patients, partly selected over the course of the past 10 years. More specifically, 80 behavioral variant Frontotemporal Dementia (bvFTD) patients (40, of whom 20 carry G4C2 expansion in the C9orf72 gene, are already available, while 40 will be prospectively recruited), 80 Bipolar Disorder (BD) patients (40, including 20 with early onset and 20 with late onset, are already available, while 40 will be prospectively recruited) and 50 healthy control (HC) subjects (20 of whom are already available from other previously approved studies), will be enrolled in this study.
For each participant a blood sample will be collected, processed, and studied in order analyze the expression of miRNA. Every participant will also undergo Nuclear Magnetic Resonance Imaging (NMR), Nuclear Magnetic Resonance Spectroscopy (1H-MRS), and Positron Emission Tomography (PET) and, lastly, a battery of behavioral scales to explore different cognitive domains will be administered to all participants by a team of psychologists and physicians. The overall estimated duration of the study is 36 months.
Following the inclusion and exclusion criteria, the participants will be recruited and subdivided into three groups:
The investigators will be collecting participants blood samples, which will be processed and analyzed. More specifically, total exosomes will be isolated from 500 microliters of plasma by ExoQuick precipitation solution (SBI). Isolation and purification of NDEs will be performed by ExoFlow purification kit using biotinylated anti-human CD171 (L1CAM) antibody (clone 5G3, Ebiosciences). Total RNA contained in NDEs will be extracted by Total Exosome RNA and Protein Isolation Kit and miRNA expression analysis by TaqMan OpenArray Human Advanced MicroRNA Panel (Thermo Fisher Scientific). Expression analysis of lncRNAs will be conducted by LincFinder Array and inflammatory and autoimmunity arrays (Qiagen).
The participants will also undergo a neuroimaging session, where structural MRI and 1H-MRS sessions will be performed using a 3T MRI scanner available at the Neuroradiology Unit. The 1H-MRS will provide sensitive and reliable assessment of neurochemical changes in specific brain areas. The acquisition voxels will be palced in the dorso- and ventrolateral prefrontal cortex (DLPFC/VLPFC), amygdala, and hippocampus. Finally, an FDG-PET scan will be performed with a Biograph Truepoint 64 PET/TC scanner. T1-weighted and FDG-PET images will be used to explore brain morphological/metabolic differences between the groups. Gray matter and white matter volumes will be estimated locally and compared between groups using voxel-based morphometry. A parallel region of interest comparison (ROIs) will be performed to estimate regional volumes using the Automated Anatomical Labelling (AAL) atlas as a reference, again focusing on the DLPFC/VLPFC, amygdala and hippocampus. Finally, an additional regional analysis based on Freesurfer software will allow the investigators to estimate the cortical thickness, cortical surface area, and cortical gyrification of the Desikan-Killiany atlas regions. All MRI and PET analyses will be performed in the context of a general linear model using a specific software implementation in MATLAB called Statistical Parametric Mapping (SPM).
Lastly, neuroimaging data and ncRNA expression profiles will be used as predictors in the ML analysis. Given the small sample size, initially linear models (e.g., Support Vector Machine) will be used. Later, to improve the predictive power of our models, the investigators will apply the ML method called random forest, a more versatile and powerful classification algorithm, and the XGBoost (eXtreme Gradient Boosting) algorithm. To avoid overfitting of the learning process, a "grid" search of model hyperparameters will be performed. A 5-fold cross-validation will be used to validate the results, with a split between training-tests of 80%-20%.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Frontotemporal Dementia, Behavioral Variant (bvFTD) | Other |
| |
| Bipolar Disorder (BD) | Other |
| |
| Healthy controls (HC) | Other |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| DISBAND protocol | Diagnostic Test | For each participant blood samples will be collected, processed and studied to analyse microRNA expression, more specifically investigating non-coding RNA (ncRNA). Each participant will undergo a multimodal neuroimaging session, composed of structural MRI, 1H-MRS and PET. |
| Measure | Description | Time Frame |
|---|---|---|
| Identification of ncRNA transcripts and a specific noncoding RNA profile in neuron-derived exosomes | Identification of ncRNA transcripts and a specific noncoding RNA profile in neuron-derived exosomes in patients with bvFTD and BD | 36 months |
| Differences in brain structure in terms of white matter volumes | Evaluation of the differences in brain structure in terms of white matter volumes comparing the three groups, using structural MRI | 36 months |
| Differences in brain structure in terms of gray matter volumes | Evaluation of the differences in brain structure in terms of gray matter volumes comparing the three groups, using structural MRI | 36 months |
| Differences in brain structure in terms of cortical gyrification | Evaluation of the differences in brain structure in terms of cortical gyrification comparing the three groups, using structural MRI | 36 months |
| Differences in brain structure in terms of superficial cortical area | Evaluation of the differences in brain structure in terms of superficial cortical area comparing the three groups, using structural MRI | 36 months |
| Differences in brain structure in terms of cortical thickness | Evaluation of the differences in brain structure in terms of cortical thickness comparing the three groups, using structural MRI | 36 months |
| Differences in brain metabolism |
| Measure | Description | Time Frame |
|---|---|---|
| Creation of a machine learning model | Creation of a machine learning model that could integrate neuroimaging data and miRNA expression data to validate the best candidates identified combining imaging and epigenetic data | 36 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Paolo Brambilla, Professor | Contact | 02 55035982 | paolo.brambilla@policlinico.mi.it |
| Name | Affiliation | Role |
|---|---|---|
| Elio Scarpini, Professor | UOSD Malattie Neurodegenerative | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico | Recruiting | Milan | MI | 20100 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 23473366 | Background | Galimberti D, Fenoglio C, Serpente M, Villa C, Bonsi R, Arighi A, Fumagalli GG, Del Bo R, Bruni AC, Anfossi M, Clodomiro A, Cupidi C, Nacmias B, Sorbi S, Piaceri I, Bagnoli S, Bessi V, Marcone A, Cerami C, Cappa SF, Filippi M, Agosta F, Magnani G, Comi G, Franceschi M, Rainero I, Giordana MT, Rubino E, Ferrero P, Rogaeva E, Xi Z, Confaloni A, Piscopo P, Bruno G, Talarico G, Cagnin A, Clerici F, Dell'Osso B, Comi GP, Altamura AC, Mariani C, Scarpini E. Autosomal dominant frontotemporal lobar degeneration due to the C9ORF72 hexanucleotide repeat expansion: late-onset psychotic clinical presentation. Biol Psychiatry. 2013 Sep 1;74(5):384-91. doi: 10.1016/j.biopsych.2013.01.031. Epub 2013 Mar 7. | |
| 22534552 |
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|
Evaluation of the differences in brain metabolism comparing the three groups, using FDG/PET |
| 36 months |
| Differences in glutamatergic neurotransmission in the prefrontal-limbic cortex | Evaluation of the differences in the glutamatergic neurotransmission of the prefrontal-limbic cortex, comparing the three groups, using 1H-MRS | 36 months |
| Background |
| Vieta E, Popovic D, Rosa AR, Sole B, Grande I, Frey BN, Martinez-Aran A, Sanchez-Moreno J, Balanza-Martinez V, Tabares-Seisdedos R, Kapczinski F. The clinical implications of cognitive impairment and allostatic load in bipolar disorder. Eur Psychiatry. 2013 Jan;28(1):21-9. doi: 10.1016/j.eurpsy.2011.11.007. Epub 2012 Apr 24. |
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| 18723524 | Background | Rademakers R, Eriksen JL, Baker M, Robinson T, Ahmed Z, Lincoln SJ, Finch N, Rutherford NJ, Crook RJ, Josephs KA, Boeve BF, Knopman DS, Petersen RC, Parisi JE, Caselli RJ, Wszolek ZK, Uitti RJ, Feldman H, Hutton ML, Mackenzie IR, Graff-Radford NR, Dickson DW. Common variation in the miR-659 binding-site of GRN is a major risk factor for TDP43-positive frontotemporal dementia. Hum Mol Genet. 2008 Dec 1;17(23):3631-42. doi: 10.1093/hmg/ddn257. Epub 2008 Aug 21. |
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| ID | Term |
|---|---|
| D001714 | Bipolar Disorder |
| D020774 | Pick Disease of the Brain |
| D057180 | Frontotemporal Dementia |
| ID | Term |
|---|---|
| D000068105 | Bipolar and Related Disorders |
| D019964 | Mood Disorders |
| D001523 | Mental Disorders |
| D057174 | Frontotemporal Lobar Degeneration |
| D003704 | Dementia |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D019965 | Neurocognitive Disorders |
| D057177 | TDP-43 Proteinopathies |
| D019636 | Neurodegenerative Diseases |
| D057165 | Proteostasis Deficiencies |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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