Skills (10)
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35 Questions2417 Followers
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274 Questions6650 Followers
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211 Questions16951 Followers
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0 Questions83 Followers
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58 Questions11717 Followers
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59 Questions15816 Followers
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17 Questions2663 Followers
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19 Questions103 Followers
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533 Questions68720 Followers
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3 Questions235 Followers
Research experience
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Oct 2012–
presentResearch: Computer-assisted diagnosis of dementia and its neuropsychiatric symptoms
University of Bergen (UiB) · Department of Clinical MedicineNorway · Bergen -
Aug 2011–
presentResearch: Computer-assisted diagnosis of dementia and its neuropsychiatric symptoms
Stavanger University Hospital · Centre for Age-Related MedicineNorway · Stavanger -
Sep 2008
Research: Multimodal study of depression
St. Petersburg Military Medical Academy · Psychiatry · St. Petersburg Military Medical AcademyRussia · St. PetersburgNeuroimaging, fMRI, PET, DTI, depression, TRD -
Sep 2006–
Sep 2008Research: Multimodal study of resistance in anxious-obsessive disorders
St. Petersburg Military Medical Academy · Psychiatry · St. Petersburg Military Medical AcademyRussia · St. PetersburgNeuroimaging, fMRI, PET, DTI, OCD, stereotaxis, Deep Brain Stimulation
Education
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Sep 2010–
Jul 2011I.P. Pavlov State Medical University
Residency in PsychiatryRussia · St. Petersburg -
Sep 2008–
Jul 2010I.P. Pavlov State Medical University
MDRussia · St. Petersburg -
Aug 2003–
Aug 2008St. Petersburg Military Military Medical Academy
Russia · St. Petersburg
Awards & achievements
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Mar 2013Scholarship: MoodNet (University of Bergen) Strategic Funding 2013
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Feb 2013Scholarship: MoodNet (University of Bergen) Short-term Scholarship
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May 2012Grant: MedIm (Norwegian Research School in Medical Imaging) Bridging Grant
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Apr 2010Award: 1st place at the Interuniversity Medical Students’ Conference (Saint Petersburg, Russia) & Section winner (“Fundamental Medicine”) at the XVII International Conference of students, postgraduates and young scientists «Lomonosov-2010» (Moscow, Russia)
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Dec 2009Award: 1st place at the III International Youth Medical Congress "The Saint-Petersburg Scientific Readings-2009" (Saint Petersburg, Russia)
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Apr 2009Award: 1st place at the Interuniversity Medical Students’ Conference (Saint Petersburg, Russia)
Other
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LanguagesRussian, English, Norwegian
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Scientific MembershipsEuropean Psychatric Association (EPA) Affiliate Member (2012).
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Other Interestsguitar, martial arts, mountaineering
Questions and Answers (24) View all
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Answer added in Advanced Statistical Analysis8 Selecting "candidates" for Latent Variables: linear PLS path modeling settingBy Alexander Lebedev · University of BergenAlexander Lebedev · University of BergenThank you very much for your response Tom! This is exactly what I have been looking for. Provided more details in the personal message.Thank you very much for your response Tom! This is exactly what I have been looking for. Provided more details in the personal message.Following
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Answer added in Advanced Statistical Analysis8 Selecting "candidates" for Latent Variables: linear PLS path modeling settingBy Alexander Lebedev · University of BergenAlexander Lebedev · University of BergenHello Tahir, Thank you for your response. I should have clarified this from the very beginning. I am looking for the best strategy to identify candida... [more]Hello Tahir, Thank you for your response. I should have clarified this from the very beginning. I am looking for the best strategy to identify candidates to form my LVs (they are going to be reflective constructs). For clinical data, I can use several scales to define one LV (for example, NPId and MADRS depression assessment scales in order to define LV "Depression"). For imaging data this does not seem to be so clear... Because, say, you can define "striatum" as a LV and include caudate nuclei and putamen (neuroanatomically and functionally related brain structures)... But, on the other hand, you can define "strio-pallidar" system as a LV and include caudate, putamen, but also globus pallidus... Therefore, I was wondering whether there are any unbiased solutions for doing this.. Of course, I can try different combinations and afterwards just select the best... But this is a very biased approach, I think..Following
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Question asked in Advanced Statistical Analysis8 Selecting "candidates" for Latent Variables: linear PLS path modeling settingI have just started getting familiar with SEM and am currently playing around with Partial Least Squares (PLS) path modeling. Everything seems to be r... [more]I have just started getting familiar with SEM and am currently playing around with Partial Least Squares (PLS) path modeling. Everything seems to be relatively clear so far, except for one thing. I work with neuroimaging data (mainly structural) from patients with neurological disorders. My interest is to model cognitive functions and to estimate an impact of different factors on them (e.g. treatment). Selecting "candidates" for latent variables from high-dimensional clinical data seems relatively straightforward (e.g. use PCA to form "symptom dimensions"), but I'm not sure what to do with imaging data, given the fact that I usually have ~150 regional brain measurements of different modalities. And although I do have some understanding of brain-cognition associations, I'm not 100% sure how to reduce dimensionality in this particular case (merging together some of the anatomically and functionally related brain structures in order to form reflective constructs). My first idea was to perform hierarchial clustering on imaging data using Pearson r^2 as a metric, but then I would be biased towards selection of r^2 cluster-forming threshold. Therefore, maybe it might be better to perform linear modeling first and then select the most significantly correlated structures from GLM results as "candidates" for LVs. Does it make sense? What would you suggest?By Alexander Lebedev · University of BergenFollowing
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Answer added in fMRI32 Best stimulus delivery software for fMRI experiments?By Alexander Lebedev · University of BergenAlexander Lebedev · University of BergenDear Colleagues! Thank you very much for sharing your experience. I am currently looking at the PsychoPy. First impressions are very good. I'm almost ... [more]Dear Colleagues! Thank you very much for sharing your experience. I am currently looking at the PsychoPy. First impressions are very good. I'm almost sure that after watching youtube tutorial you'll be able to write a simple block design. However, if you are planning to design something a bit more complicated (for instance, if you want to capture and report response time), you'll probably need some coding skills (not much, but preferably in Python). Nevertheless, GUI is pretty intuitive.. I would definitely suggest at least trying it.Following
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Answer added in FSL7 Do I need to repeat pre-processing for Tracula?By Prerona Mukherjee · The University of EdinburghAlexander Lebedev · University of BergenHi Prerona! I'm afraid that you have to (if you did standard FSL analysis like TBSS), as TRACULA reconstructs tracts using anatomical landmarks derive... [more]Hi Prerona! I'm afraid that you have to (if you did standard FSL analysis like TBSS), as TRACULA reconstructs tracts using anatomical landmarks derived from the Freesurfer's output (recon-all).Following
Publications (9) View all
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Article: Использование методов статистического параметрического картирования в нейровизуализационных исследованиях патогенеза депрессивных расстройств.
[show abstract] [hide abstract]
ABSTRACT: Рассматриваются возможности статистического параметрического картирования для анализа структурных [анатомическая магнитно-резонансная томография (МРТ), диффузионно-тензорная визуализация] и функциональных (функциональная МРТ) нейровизуализационных данных на примере исследования депрессивных расстройств. Выпуск журнала "Биотехносфера" № 3 (9)/2010 доступен для свободного скачивания на сайте http://polytechnics.ru/Биотехносфера. 08/2010; -
SourceAvailable from: Alexander V Lebedev
Article: Multivariate classification of patients with Alzheimer's and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study.
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ABSTRACT: CONTEXT: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are the most common neurodegenerative dementia types. It is important to differentiate between them because of the differences in prognosis and treatment approaches. OBJECTIVE: Investigate if sparse partial least squares (SPLS) classification of cortical thickness measurements could differentiate between AD and DLB. METHODS: Two independent cohorts without MR-protocol alignment in Norway and Slovenia with 97 AD and DLB subjects were enrolled. Cortical thickness measurements acquired with Freesurfer were used in subsequent SPLS classification runs. The cohorts were analyzed separately and afterwards combined. The models were trained with leave-one-out cross-validation and test datasets were used when available. To study the impact of MR-protocol alignment, the classifiers were additionally tested on sets drawn exclusively from the independent cohorts. RESULTS: The obtained sensitivity/specificity/AUC values were 94.4/88.89/0.978 and 88.2/94.1/0.969 in the Norwegian and Slovenian cohorts, respectively. Both cohorts showed AD-associated pattern of thinning in mid-anterior temporal, occipital and subgenual cingulate cortex, whereas the pattern supportive for DLB included thinning in dorsal cingulate, posterior temporal and lateral orbitofrontal regions. When combining the cohorts, sensitivity/specificity/AUC were 82.1/85.7/0.948 for the training and 77.8/75/0.731 for the testing datasets with the same pattern-of-difference. The models tested on datasets drawn exclusively from the independent cohorts did not produce adequate accuracy. CONCLUSION: SPLS classification of cortical thickness is a good method for differentiating between AD and DLB, relatively stable even for mixed data, but not when tested on completely independent data drawn from different cohorts (without MR-protocol alignment).Journal of Neurology 12/2012; · 3.47 Impact Factor -
Conference Proceeding: Neuroimaging in diagnosis of depressive disorders.
Traditions and Innovations in Psychiatry.; 06/2010 -
Conference Proceeding: Clinical–physiological approach to the selection of therapy strategies in resistant anxious–obsessive disorders.
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ABSTRACT: Aims: To develop the clinical–physiological approach to the selection of therapy strategies in resistant anxious–obsessive disorders Methods: Positron emission tomography, functional MRI, magnetic resonance spectroscopy and neurosurgery Results: We summarise the literature data and our own observations from functional neuroimaging and neurosurgical studies Conclusions: We suggested that for the better objective justification in the target selection, it is advisable not to use common clinical systematics of obsessions but their “neuropsychiatric” classification. We distinguish among them three main varieties: “hypermotivational” obsessions (hypothalamic-striatal level of major pathogenetic mechanisms), “difficulties in way of action selection” (thalamic-striatal level) and “difficulties in completing of action” (thalamic-cortical level).Section of Neuropsychiatry (SoN) Royal College of Psychiatrists and British Neuropsychiatry Association: Joint conference at the British Neuropsychiatry Association.; 04/2009 -
Conference Proceeding: Differences and similarities between reactive, endogenous and organic depressions: a neuroimaging study.
Traditions and Innovations in Psychiatry.; 06/2010