Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Iturria-Medina, Yasser; Carbonell, Félix; Assadi, Atousa; Adewale, Quadri; Khan, Ahmed Faraz; Baumeister, Tobias R.; Sanchez-Rodriguez, Lazaro M.

doi:10.1038/s42003-021-02133-x

Cited by 21 publications

(35 citation statements)

References 65 publications

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“…The Matlab code for the ESM has been made available as a public software release with an accompanying paper (neuropm-lab.com/software 29 ). All the Python code used to analyze ESM results, perform statistical analysis, and visualize results can be found at https://github.com/llevitis/DIAN_ESM_AmyloidBeta_Project.git .…”

Section: Methodsmentioning

confidence: 99%

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer’s disease

Levitis

Vöglein

Funck

et al. 2022

Brain Communications

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Amyloid-beta (Aβ) deposition is one of the hallmark pathologies in both sporadic Alzheimer’s disease (sAD) and autosomal dominant Alzheimer’s disease (ADAD), the latter of which is caused by mutations in genes involved in Aβ processing. Despite Aβ deposition being a centerpiece to both sAD and ADAD, some differences between these Alzheimer’s disease subtypes have been observed with respect to the spatial pattern of Aβ. Previous work has shown that the spatial pattern of Aβ in individuals spanning the sAD spectrum can be reproduced with high accuracy using an epidemic spreading model which simulates the diffusion of Aβ across neuronal connections and is constrained by individual rates of Aβ production and clearance. However, it has not been investigated whether Aβ deposition in the rarer ADAD can be modeled in the same way, and if so, how congruent the spreading patterns of Aβ across sAD and ADAD are. We leverage the epidemic spreading model as a data-driven approach to probe individual-level variation in the spreading patterns of Aβ across three different large-scale imaging datasets (2 sAD, 1 ADAD). We applied the epidemic spreading model separately to the Alzheimer’s Disease Neuroimaging initiative (N=737), the Open Access Series of Imaging Studies (N=510), and the Dominantly Inherited Alzheimer’s Network (N=249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately, and further identified the most likely subject-specific epicenter of Aβ spread. Using epicenters defined in previous work in sAD, the epidemic spreading model provided moderate prediction of the regional pattern of Aβ deposition across all three datasets. We further find that, while the most likely epicenter for most Aβ-positive subjects overlaps with the default mode network, 13% of ADAD individuals were best characterized by a striatal origin of Aβ spread. These subjects were also distinguished by being younger than ADAD subjects with a default mode network Aβ origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most ADAD patients express Aβ spreading patterns similar to those of sAD, but that there may be a subset of ADAD patients with a separate, striatal phenotype.

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Section: Methodsmentioning

confidence: 99%

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer’s disease

Levitis

Vöglein

Funck

et al. 2022

Brain Communications

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“…In the moderate/severe TBI context, it is critical to consider the distinction between these two DP modeling categories, while empirical approaches can represent powerful predictive tools, they constitute “black-boxes.” This handicap is not common to mechanistic models, which (in part to remain interpretable) often achieve a lower predictive capacity. In TBI applications, in order to maximize the tradeoff between the models' clinical predictability and biological interpretability, it will be essential to combine the advantages of empirical and mechanistic brain disease models ( 182 ). For example, in a recent neurodegeneration study ( 183 ), state-of-the-art machine learning advances for exploring and visualizing high dimensional data ( 184 ) were used to define contrasted disease trajectories and clinically screen the patients.…”

Section: Integrative Neuroinformatics: Future Of Precision Medicine In Tbimentioning

confidence: 99%

“…The TBI research and clinical community could take advantage of the growing number of techniques initially developed for other neurological conditions (Alzheimer's, Parkinson's, depression), which have been tested and validated in large-scale heterogenous datasets. As such, the use of already available Open-Access neuroinformatic tools ( 177 , 178 , 182 , 186 ), integrating a large variety of biological data for a better understanding of individual disease progression and treatment needs, may accelerate the adaptation and improvement of advance computational approaches in the moderate/severe TBI context.…”

Section: Integrative Neuroinformatics: Future Of Precision Medicine In Tbimentioning

confidence: 99%

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury

et al. 2021

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Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the “one-treatment fits all” approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex “-omics” data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.

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“…In neurosciences, these concepts and ideas resulted in the neuroinformatic subfield 4 devoted to developing analytical and computational models for sharing, integrating, and analyzing multimodal neuroscience data. In this context, strategies to integrate neuroimaging data with transcriptomics started to emerge at the gene level (12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Serine/threonine kinase activity associates with brain glucose metabolism changes in Alzheimer’s Disease

Povala

Bastiani

Bellaver

et al. 2022

Preprint

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Positron emission tomography (PET) imaging has greatly improved the diagnosis and monitoring of Alzheimer's disease (AD). The recently developed neuroinformatic field is expanding analytical and computational strategies to study multimodal neuroscience data. One approach is integrating PET imaging and omics to provide new insights into AD pathophysiology. Hippocampal and blood transcriptomic data of cognitively unimpaired (CU) and cognitively impaired (CI) individuals were obtained from Gene Expression Omnibus (GEO) databases and the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used the differentially expressed genes (DEGs) from these datasets to implement a modular dimension reduction approach based on Gene Ontology (GO) and reverse engineering of transcriptional networks centered on transcription factors (TF). GO clusters and regulatory units of TF were selected to undergo integration with [18F]Fluorodeoxyglucose ([18F]FDG)-PET images using voxel-wise linear regression models adjusted for age, gender, years of education, and APOEϵ4 status. The GO semantic similarity resulted in 16 GO clusters enriched with overlapping DEGs in blood and the brain. Voxel-wise analysis revealed a strong association between the cluster related to the regulation of protein serine/threonine kinase activity and the [18F]FDG-PET signal in the brain. The master regulator analysis showed 61 regulatory units of TF significantly enriched with DEGs. The voxel-wise analysis of these regulons showed that zinc-finger-related regulatory units had the closest association with brain glucose metabolism. We identified multiple biological processes and regulatory units of TF associated with [18F]FDG-PET metabolism in the brain of individuals across the aging and AD clinical spectrum. Furthermore, the prominent enrichment of protein serine/threonine kinase activity-related GO cluster and the zinc-finger-related regulatory units highlight the potential gene signatures associated with changes in glucose metabolism due to AD pathology.

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Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Cited by 21 publications

References 65 publications

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer’s disease

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer’s disease

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury

Serine/threonine kinase activity associates with brain glucose metabolism changes in Alzheimer’s Disease

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