
Amir DehsarviLudwig-Maximilians-Universität in Munich | LMU · Institute for Stroke and Dementia Research (ISD)
Amir Dehsarvi
PhD in Electronic Engineering
About
44
Publications
3,209
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126
Citations
Introduction
I work on the application of machine learning/deep learning for the analysis of biomedical data for disease diagnosis, target identification, and validation, using different types of large, complex, biomedical datasets.
Additional affiliations
August 2021 - September 2024
February 2020 - August 2021
Position
- Research Fellow
Description
- In this research project, I am working with a multidisciplinary team of clinicians and scientists who are focused upon unravelling the mechanisms of chronic fatigue, a salient issue across the chronic disease spectrum in the multicentre large project of The Lessening the Impact of Fatigue Trial (LIFT).
Education
February 2014 - July 2018
October 2012 - September 2013
October 2007 - September 2010
The University of Science and Art, Yazd, Iran
Field of study
- Electrical Engineering - Electronics
Publications
Publications (44)
In Alzheimer’s disease (AD), amyloid-β (Aβ) triggers the aggregation and spreading of tau pathology, which drives neurodegeneration and cognitive decline. However, the pathophysiological link between Aβ and tau remains unclear, which hinders therapeutic efforts to attenuate Aβ-related tau accumulation. Aβ has been found to trigger neuronal hyperact...
Patients with Alzheimer’s disease (AD) and clinically overlapping neurodegenerative diseases are classified molecularly using the A/T/N classification system. Apart from fluid biomarkers and structural MRI, the three-dimensional A/T/N system incorporates characteristic features from β-amyloid-PET (A), tau-PET (T), and FDG-PET (N). We evaluated if d...
In Alzheimer’s disease (AD), Aβ triggers p-tau secretion, which drives tau aggregation. Therefore, it is critical to characterize modulators of Aβ-related p-tau increases which may alter AD trajectories. Here, we assessed whether factors known to alter tau levels in AD modulate the association between fibrillar Aβ and secreted p-tau 181 determined...
Traumatic brain injury is widely viewed as a risk factor for dementia, but the biological mechanisms underlying this association are still unclear. In previous studies, traumatic brain injury has been associated with the hallmark pathologies of Alzheimer’s disease, i.e. amyloid-β plaques and neurofibrillary tangles comprised of hyperphosphorylated...
Background
Memory clinic patients typically present with Alzheimer’s disease (AD) and cerebral small vessel disease (SVD) to varying degrees. Therefore, it is crucial to determine the etiology of cognitive deficits for facilitating patient‐centered treatment in memory clinics. Plasma biomarkers (ptau217, Glial Fibrillary Acidic Protein [GFAP], Neur...
Background
Lewy body pathology consisting of aggregated alpha‐Synuclein (a‐Syn) is the hallmark pathology in Parkinson’s disease, yet a‐Syn aggregates are also commonly observed post‐mortem as a co‐pathology in Alzheimer’s disease (AD) patients. Preclinical research has shown that a‐Syn can amplify Ab‐associated tau seeding and aggregation, hence a...
Background
Understanding modulators of Alzheimer's disease’s (AD) progression is crucial for determining optimal treatment windows and targets. Apolipoprotein E e4 (ApoE4), i.e. a key AD risk factor, is associated with earlier tau accumulation at lower Aß levels (Steward et al. 2023), yet, the mechanisms driving this connection remain unclear. Thus...
Background
Neuroimaging studies have revealed age and sex‐specific differences in Alzheimer’s disease (AD) trajectories. However, how age and sex modulate tau spreading remains unclear. Thus, we investigated how age and sex modulate the amyloid‐beta (Aß)‐induced accumulation and spreading of tau pathology from local epicenters across connected brai...
Background
In Alzheimer’s disease (AD), cortical tau aggregation is a strong predictor of cortical brain atrophy as shown by MRI and PET studies, particularly driving the degeneration of neuronal somata in the grey matter. However, tau’s physiological role is to stabilize microtubules within axons in the brain’s white matter (WM) pathways. Therefor...
Background
In Alzheimer’s disease, Aß triggers tau spreading which drives neurodegeneration and cognitive decline. However, the mechanistic link between Aß and tau remains unclear, which hinders therapeutic efforts to attenuate Aß‐related tau accumulation. Preclinical research could show that tau spreads across connected neurons in an activity‐depe...
Background
Memory clinic patients typically present with Alzheimer’s disease (AD) and cerebral small vessel disease (SVD) to varying degrees. Therefore, it is crucial to determine the etiology of cognitive deficits for facilitating patient‐centered treatment in memory clinics. Plasma biomarkers (ptau217, Glial Fibrillary Acidic Protein [GFAP], Neur...
Background
Understanding modulators of Alzheimer's disease’s (AD) progression is crucial for determining optimal treatment windows and targets. Apolipoprotein E ε4 (ApoE4), i.e. a key AD risk factor, is associated with earlier tau accumulation at lower Aβ levels (Steward et al. 2023), yet, the mechanisms driving this connection remain unclear. Thus...
Background
Lewy body pathology consisting of aggregated alpha‐Synuclein (a‐Syn) is the hallmark pathology in Parkinson’s disease, yet a‐Syn aggregates are also commonly observed post‐mortem as a co‐pathology in Alzheimer’s disease (AD) patients. Preclinical research has shown that a‐Syn can amplify Ab‐associated tau seeding and aggregation, hence a...
Background
In Alzheimer’s disease, Aβ triggers tau spreading which drives neurodegeneration and cognitive decline. However, the mechanistic link between Aβ and tau remains unclear, which hinders therapeutic efforts to attenuate Aβ‐related tau accumulation. Preclinical research could show that tau spreads across connected neurons in an activity‐depe...
Background
In Alzheimer’s disease (AD), cortical tau aggregation is a strong predictor of cortical brain atrophy as shown by MRI and PET studies, particularly driving the degeneration of neuronal somata in the grey matter. However, tau’s physiological role is to stabilize microtubules within axons in the brain’s white matter (WM) pathways. Therefor...
Background
Neuroimaging studies have revealed age and sex‐specific differences in Alzheimer’s disease (AD) trajectories. However, how age and sex modulate tau spreading remains unclear. Thus, we investigated how age and sex modulate the amyloid‐beta (Aβ)‐induced accumulation and spreading of tau pathology from local epicenters across connected brai...
Background
Preclinical, postmortem, and positron emission tomography (PET) imaging studies have pointed to neuroinflammation as a key pathophysiological hallmark in primary 4‐repeat (4R) tauopathies and its role in accelerating disease progression.
Objective
We tested whether microglial activation (1) progresses in similar spatial patterns as the...
Four-repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R tauopathies are progressive supranuclear palsy (PSP) and corticobasal degeneration characterized by subcortical tau accumulation and cortical neuronal dysfunction, as shown by PET-assessed hypoperfusion and g...
Objective
Chronic fatigue is a major clinical unmet need among patients with rheumatoid arthritis (RA). Current therapies are limited to nonpharmacological interventions, such as personalized exercise programs (PEPs) and cognitive–behavioral approaches (CBAs); however, most patients still continue to report severe fatigue. To inform more effective...
In Alzheimer’s disease, amyloid-beta (Aβ) triggers the trans-synaptic spread of tau pathology, and aberrant synaptic activity has been shown to promote tau spreading. Aβ induces aberrant synaptic activity, manifesting in increases in the presynaptic growth-associated protein 43 (GAP-43), which is closely involved in synaptic activity and plasticity...
Background
Recent post‐mortem studies suggest that cerebrovascular disease contributes to Alzheimer’s disease (AD) pathophysiology and clinical progression. In particular, cerebral amyloid angiopathy is highly common in AD and has been linked to faster cognitive decline. Yet, in‐vivo evidence on the contribution of cerebrovascular disease to pathob...
Background
In Alzheimer’s disease (AD), Amyloid‐beta deposition (Ab) is associated with tau spreading from epicenters to connected regions. Yet, Ab and tau accumulate in strikingly different patterns, and it is unclear how Abdrives tau spreading. We envision two mechanisms, i.e. i) that cortical Ab exerts remote effects and “pulls” tau out of conne...
Background
Targeting amyloid (Aß) may show highest clinical efficacy when preventing downstream tau spreading and subsequent neurodegeneration. Thus, it’s critical to establish Aß‐PET thresholds at which tau spreading is triggered, which may depend on ApoE4, i.e. a key genetic risk factor for Aß, tau and cognitive decline. Yet, ApoE4’s impact on Aß...
Background
Animal and in vitro studies of Alzheimer’s disease (AD) found that tau spreads trans‐synaptically in an activity‐dependent manner, suggesting that synapses are key routes for tau spread. Importantly, amyloid‐beta (Ab) induces synaptic remodeling and aberrant synaptic activity, which may accelerate trans‐synaptic tau spread. In AD patient...
Background
State‐of‐the‐art preprocessing of MRI and PET data is crucial for Alzheimer’s disease (AD) neuroimaging research. Therefore, standardization and harmonization of neuroimaging preprocessing across sites is key to generate comparable and sharable datasets and to reduce potential bias introduced by different preprocessing strategies. Furthe...
Background
State‐of‐the‐art preprocessing of MRI and PET data is crucial for Alzheimer’s disease (AD) neuroimaging research. Therefore, standardization and harmonization of neuroimaging preprocessing across sites is key to generate comparable and sharable datasets and to reduce potential bias introduced by different preprocessing strategies. Furthe...
Background
Recent post‐mortem studies suggest that cerebrovascular disease contributes to Alzheimer’s disease (AD) pathophysiology and clinical progression. In particular, cerebral amyloid angiopathy is highly common in AD and has been linked to faster cognitive decline. Yet, in‐vivo evidence on the contribution of cerebrovascular disease to pathob...
Background
Animal and in vitro studies of Alzheimer’s disease (AD) found that tau spreads trans‐synaptically in an activity‐dependent manner, suggesting that synapses are key routes for tau spread. Importantly, amyloid‐beta (Ab) induces synaptic remodeling and aberrant synaptic activity, which may accelerate trans‐synaptic tau spread. In AD patient...
Background
Targeting amyloid (Aß) may show highest clinical efficacy when preventing downstream tau spreading and subsequent neurodegeneration. Thus, it’s critical to establish Aß‐PET thresholds at which tau spreading is triggered, which may depend on ApoE4, i.e. a key genetic risk factor for Aß, tau and cognitive decline. Yet, ApoE4’s impact on Aß...
Background
In Alzheimer’s disease (AD), Amyloid‐beta deposition (Ab) is associated with tau spreading from epicenters to connected regions. Yet, Ab and tau accumulate in strikingly different patterns, and it is unclear how Abdrives tau spreading. We envision two mechanisms, i.e. i) that cortical Ab exerts remote effects and “pulls” tau out of conne...
Background: Previous studies investigating disruptions in central nervous system (CNS) barriers in schizophrenia spectrum disorders (SSD) mainly focused on cerebrospinal fluid (CSF) markers, that cannot adequately assess blood-brain barrier (BBB) integrity. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents a sensitive method...
Importance
For the Alzheimer disease (AD) therapies to effectively attenuate clinical progression, it may be critical to intervene before the onset of amyloid-associated tau spreading, which drives neurodegeneration and cognitive decline. Time points at which amyloid-associated tau spreading accelerates may depend on individual risk factors, such a...
Background: Dementia screening tools typically involve face-to-face cognitive testing. Indeed, this introduces an increasing burden on the clinical staff, particularly in low-resource settings. The objective of our study is to develop an integrated online platform for efficient dementia screening, using a brief and cost-effective assessment.
Method...
With the increase in large multimodal cohorts and high‐throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate...
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicabi...
Background: Chronic Fatigue is a major clinical unmet need among patients with Rheumatoid Arthritis (RA). Current therapies are limited to non-pharmacological interventions, such as personalised exercise programmes (PEP) and cognitive behavioural approaches (CBA), however, still most patients continue to report severe fatigue. To inform more effect...
Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. Resu...
Introduction:
Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.
Methods:
We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research....
Background
Overt sentence reading in mild cognitive impairment (MCI) and mild-to-moderate Alzheimer’s disease (AD) has been associated with slowness of speech, characterized by a higher number of pauses, shorter speech units and slower speech rate and attributed to reduced working memory/ attention and language capacity.
Objective
This preliminary...
This paper reports an automated approach to the clinical monitoring of Parkinson’s disease (PD) by applying Evolutionary Algorithms (EAs) to resting-state functional magnetic imaging (rs-fMRI) data. The novel application of EAs to both map and predict the functional connectivity is considered in patients receiving the drug Modafinil versus placebo....
Background : Increasing efforts have focused on the establishment of novel biomarkers for the early detection of Alzheimer’s disease (AD) and prediction of Mild Cognitive Impairment (MCI)-to-AD conversion. Behavioral changes over the course of healthy ageing, at disease onset and during disease progression, have been recently put forward as promisi...
Background and Objective: It is commonly accepted that accurate monitoring of neurodegenerative diseases is crucial for effective disease management and delivery of medication and treatment. This research develops automatic clinical monitoring techniques for PD, following treatment, using the novel application of EAs. Specifically, the research que...
Accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. A fu...
Accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for olfactory dysfunction in early stage Parkinson's disease (PD) by considering the novel application of evolutionary algori...
Questions
Question (1)
I am running a script based on different batches in SPM on a dataset with 26 participants (2 sessions and 3 runs). This dataset has been analysed before in FSL (2 publications with statistical analysis) and I am doing a reanalysis using SPM12. The code runs smoothly for some of the participants and does the spatial preprocessing and the processing by defining a GLM and then moving on to DCM to extract the DCM.Ep.A (I have modified the sum_run_fmri_spec in config so that it stops asking for a confirmation for over writing the SPM file).
Can this be because for regressing out the WM and CSF I have two different jobs? This is the same when I want to extract the four VOIs.
I have done this according to the sample script in the practical example for rs-DCM.
Unfortunately, it works well with the exception of a few participants/runs.
The error message is:
------------------------------------------------------------------------
Running job #1
------------------------------------------------------------------------
Running 'Volume of Interest'
Warning: Empty region.
> In spm_regions (line 155)
In spm_run_voi (line 63)
In cfg_run_cm (line 29)
In cfg_util>local_runcj (line 1688)
In cfg_util (line 959)
In spm_jobman>fill_run_job (line 458)
In spm_jobman (line 247)
In spDCM_fun_test (line 207)
Failed 'Volume of Interest'
Reference to non-existent field 'v'.
In file ".../spm12/config/spm_run_voi.m" (v6301), function "spm_run_voi" at line 76.
The following modules did not run:
Failed: Volume of Interest
Error using MATLABbatch system
Job execution failed. The full log of this run can be found in MATLAB command
window, starting with the lines (look for the line showing the exact #job as
displayed in this error message)
------------------
Running job #1
------------------
Is there anything that I should do to fix this?
On a different note, I had to modify the code in DCM specification:
Sess = SPM.Sess(xY(1).Sess);
as this was causing errors as xY(1).Sess is 2 at points, and not 1. Therefore, I changed it to:
Sess = SPM.Sess(1);
Is it going to be problematic? And, if yes, is there any other way of fixing the issue?
I have three "for" loops for the dataset, one for the subjects, one for the sessions, and the last one for the runs... I managed to get the DCM.mat for 16 subjects out of 26 (and then it stops for the error I get in the last section). Then I am just taking the average of the DCM.Ep.A for each subject in each session (which I am not sure is the right thing to do - the alternative method can be doing a BMS RFX to find out which model is best and just use the DCM.Ep.A for that model). The data includes samples taken from 26 subjects in two sessions and three runs.
Unfortunately, the RFX BMS does not seem to be working outside the GUI (when I change the values) either. The script generated by the batch seems to be like (I have changed it slightly):
clear matlabbatch;
mkdir([session_folder_name '/func/BMS']);
matlabbatch{1}.spm.dcm.bms. inference.dir = ...
cellstr([session_folder_name '/func/BMS']);
matlabbatch{1}.spm.dcm.bms. inference.sess_dcm{1}.dcmmat{ 1,1} = ...
cellstr([session_folder_name '/func/Run01/GLM/DCM_DMN.mat'] );
matlabbatch{1}.spm.dcm.bms. inference.sess_dcm{1}.dcmmat{ 2,1} = ...
cellstr([session_folder_name '/func/Run02/GLM/DCM_DMN.mat'] );
matlabbatch{1}.spm.dcm.bms. inference.sess_dcm{1}.dcmmat{ 3,1} = ...
cellstr([session_folder_name '/func/Run03/GLM/DCM_DMN.mat'] );
matlabbatch{1}.spm.dcm.bms. inference.model_sp = {''};
matlabbatch{1}.spm.dcm.bms. inference.load_f = {''};
matlabbatch{1}.spm.dcm.bms. inference.method = 'RFX';
matlabbatch{1}.spm.dcm.bms. inference.family_level.family_ file = {''};
matlabbatch{1}.spm.dcm.bms. inference.bma.bma_no = 0;
matlabbatch{1}.spm.dcm.bms. inference.verify_id = 0;
spm_jobman('run',matlabbatch);
However, this gives me an error as well (The Model seems to be empty and I cannot fix it, even though all the variables and the structure in the script above seem to be fine).
On a different note, I was wondering whether there is a way of finding out which model is the best one, other than just looking at the graphs generated using the GUI - I realised that this is a question some of the other SPM users in the list have as well. If not, it seems like I have to run the script for each session and each subject and look at the graphs... which does not seem reasonable. If there is a quantitative way of finding the best model for each session in BMS, then I can easily select the DCM.Ep.A for that model and use it in my further analysis. Also, what if the best model is different for each subject, e.g., model 3 for subject 1, model 2 for subject 8? Can they then be used in the analysis?
Many thanks and apologies for the very long email with millions of different questions!
Thanking you in advance,
Amir