D. G. Koenigsberg

D. G. Koenigsberg

Psychiatry Resident at Nuvance. Graduate of Albert Einstein College of Medicine. Researcher in deep learning applications for neuropathology at Icahn School of Medicine at Mount Sinai. Post bacc at Columbia University. Previously software engineer at Triplelift in computer vision and scalable data infrastructure. BA in Mathematics, minor in physics, from Brandeis University.

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publications

Search for Twisted Polarization in Arcsecond Scale Quasar Jets

Published in Brandeis, 2013

The quasar 3C345 displays a seemingly singular 35 degree twist in the polarization of its kiloparsec-scale jet. Because of the vast number of quasars known, it seems unlikely that 3C345 is alone in this respect.

Recommended citation: Chevalier, A. et al (2013). The Search for Twisted Polarization in Arcsecond-scale Quasar Jets (Doctoral dissertation, Brandeis University). http://dkoen.github.io/files/AstrophysicsPoster.pdf

Screening peripheral biopsies for alpha-synuclein pathology using deep machine learning

Published in Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 2020

This work is first to demonstrate the practical utility of an CNN for screening for LTS, which can further translate into a practical screening tool facilitating the antemortem diagnosis of PD. We further plan to develop neural networks for accurate and precise detection and quantification of LTS in antemortem SMG biopsies. This, altogether, would offer a screening, confirmatory, prognostic, and quantitative tool for clinical assessment of early PD.

Recommended citation: Signaevski, M., Prastawa, M., Tabish, N., Marami, B., Koenigsberg, D., Bryce, C., Chahine, L., Mollenhauer, B., Mosovsky, S., Riley, L., Dave, K.D., Eberling, J., Coffey, C., Adler, C., Serrano, G.E., III, C.W., Koll, J., Fernandez, G., Zeineh, J., Cordon-Cardo, C., Beach, T.G. and Crary, J.F. (2020), Screening peripheral biopsies for alpha-synuclein pathology using deep machine learning. Alzheimer's Dement., 16: e047358. https://doi.org/10.1002/alz.047358 https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz.047358

Antemortem detection of Parkinson’s disease pathology in peripheral biopsies using artificial intelligence

Published in Acta Neuropathologica Communications, 2022

The diagnosis of Parkinson’s disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmortem assessment remains the gold standard for a definitive diagnosis. While it is well recognized that PD manifests pathologically in the central nervous system with aggregation of α-synuclein as Lewy bodies and neurites, similar Lewy-type synucleinopathy (LTS) is additionally found in the peripheral nervous system that may be useful as an antemortem biomarker. We have previously found that detection of LTS in submandibular gland (SMG) biopsies is sensitive and specific for advanced PD; however, the sensitivity is suboptimal especially for early-stage disease. Further, visual microscopic assessment of biopsies by a neuropathologist to identify LTS is impractical for large-scale adoption. Here, we trained and validated a convolutional neural network (CNN) for detection of LTS on 283 digital whole slide images (WSI) from 95 unique SMG biopsies. A total of 8,450 LTS and 35,066 background objects were annotated following an inter-rater reliability study with Fleiss Kappa = 0.72. We used transfer learning to train a CNN model to classify image patches (151 × 151 pixels at 20× magnification) with and without the presence of LTS objects. The trained CNN model showed the following performance on image patches: sensitivity: 0.99, specificity: 0.99, precision: 0.81, accuracy: 0.99, and F-1 score: 0.89. We further tested the trained network on 1230 naïve WSI from the same cohort of research subjects comprising 42 PD patients and 14 controls. Logistic regression models trained on features engineered from the CNN predictions on the WSI resulted in sensitivity: 0.71, specificity: 0.65, precision: 0.86, accuracy: 0.69, and F-1 score: 0.76 in predicting clinical PD status, and 0.64 accuracy in predicting PD stage, outperforming expert neuropathologist LTS density scoring in terms of sensitivity but not specificity. These findings demonstrate the practical utility of a CNN detector in screening for LTS, which can translate into a computational tool to facilitate the antemortem tissue-based diagnosis of PD in clinical settings.

Recommended citation: Signaevsky, M., Marami, B., Prastawa, M. et al. Antemortem detection of Parkinson’s disease pathology in peripheral biopsies using artificial intelligence. acta neuropathol commun 10, 21 (2022). https://doi.org/10.1186/s40478-022-01318-7

Interpretable deep learning of myelin histopathology in age-related cognitive impairment

Published in Acta Neuropathologica Communications, 2022

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aβ) plays a major role in Alzheimer’s type age-related cognitive impairment, in addition to other etiopathologies such as Aβ-independent hyperphosphorylated tau, cerebrovascular disease, and myelin damage, which also warrant further investigation. Classical methods, even in the setting of the gold standard of postmortem brain assessment, involve semi-quantitative ordinal staging systems that often correlate poorly with clinical outcomes, due to imperfect cognitive measurements and preconceived notions regarding the neuropathologic features that should be chosen for study. Improved approaches are needed to identify histopathological changes correlated with cognition in an unbiased way. We used a weakly supervised multiple instance learning algorithm on whole slide images of human brain autopsy tissue sections from a group of elderly donors to predict the presence or absence of cognitive impairment (n = 367 with cognitive impairment, n = 349 without). Attention analysis allowed us to pinpoint the underlying subregional architecture and cellular features that the models used for the prediction in both brain regions studied, the medial temporal lobe and frontal cortex. Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with a modest accuracy that was significantly greater than chance. Attention-based interpretation studies of the features most associated with cognitive impairment in the top performing models suggest that they identified myelin pallor in the white matter. Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive impairment that can be translated to the study of other neurobiological disorders.

Recommended citation: McKenzie, A.T., Marx, G.A., Koenigsberg, D. et al. Interpretable deep learning of myelin histopathology in age-related cognitive impairment. acta neuropathol commun 10, 131 (2022). https://doi.org/10.1186/s40478-022-01425-5 https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-022-01425-5

Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment

Published in Acta Neuropathologica Communications, 2022

Tauopathies are a category of neurodegenerative diseases characterized by the presence of abnormal tau protein-containing neurofibrillary tangles (NFTs). NFTs are universally observed in aging, occurring with or without the concomitant accumulation of amyloid-beta peptide (Aβ) in plaques that typifies Alzheimer disease (AD), the most common tauopathy. Primary age-related tauopathy (PART) is an Aβ-independent process that affects the medial temporal lobe in both cognitively normal and impaired subjects. Determinants of symptomology in subjects with PART are poorly understood and require clinicopathologic correlation; however, classical approaches to staging tau pathology have limited quantitative reproducibility. As such, there is a critical need for unbiased methods to quantitatively analyze tau pathology on the histological level. Artificial intelligence (AI)-based convolutional neural networks (CNNs) generate highly accurate and precise computer vision assessments of digitized pathology slides, yielding novel histology metrics at scale. Here, we performed a retrospective autopsy study of a large cohort (n = 706) of human post-mortem brain tissues from normal and cognitively impaired elderly individuals with mild or no Aβ plaques (average age of death of 83.1 yr, range 55–110). We utilized a CNN trained to segment NFTs on hippocampus sections immunohistochemically stained with antisera recognizing abnormal hyperphosphorylated tau (p-tau), which yielded metrics of regional NFT counts, NFT positive pixel density, as well as a novel graph-theory based metric measuring the spatial distribution of NFTs. We found that several AI-derived NFT metrics significantly predicted the presence of cognitive impairment in both the hippocampus proper and entorhinal cortex (p < 0.0001). When controlling for age, AI-derived NFT counts still significantly predicted the presence of cognitive impairment (p = 0.04 in the entorhinal cortex; p = 0.04 overall). In contrast, Braak stage did not predict cognitive impairment in either age-adjusted or unadjusted models. These findings support the hypothesis that NFT burden correlates with cognitive impairment in PART. Furthermore, our analysis strongly suggests that AI-derived metrics of tau pathology provide a powerful tool that can deepen our understanding of the role of neurofibrillary degeneration in cognitive impairment.

Recommended citation: Marx, G.A., Koenigsberg, D.G., McKenzie, A.T. et al. Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment. acta neuropathol commun 10, 157 (2022). https://doi.org/10.1186/s40478-022-01457-x https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-022-01457-x

Antemortem detection of Parkinson’s disease pathology in peripheral biopsies using artificial intelligence

Published in Neurology, 2023

Objective: To identify features of histologic brain aging and clinical correlates of brain age acceleration. Background: The discordance between cellular and chronologic aging is useful for understanding diseases in the brain and biology at large. One established method for analyzing the factors that contribute to brain aging is to train machine learning models that predict an individual’s age based on an MRI image of their brain. While this approach has yielded important insights, it is inherently constrained by the information provided by an MRI. However, age-dependent pathologic change has the potential to be assessed at greater detail histologically. Histopathological whole slide images provide more granular information regarding cellular structure, injury, dysfunction, and morphology. Recent technological advances in whole slide image digitization has paved the way for large scale analysis of histologic data via artificially intelligent based computer vision techniques. Design/Methods: We leveraged a large novel collection of uniformly processed digitized human post-mortem brain tissue sections to create a histological brain age estimation model. We further investigated the effect of cognitive impairment and exogenous stress on the model. This was accomplished by developing a context-aware attention-based deep multiple instance learning model on 702 human brain tissues sections (ages 50–110 yr) from the hippocampus stained with Luxol Fast Blue counterstained with hematoxylin and eosin on a brain age estimation task. Results: This model estimated brain age within a mean absolute error of 5.2 years. Learned attention weights corresponded to neuroanatomical regions vulnerable to age-related change. We found that histopathologic brain age acceleration had significant associations with cognitive impairment, MMSE, p-tau burden, chronic traumatic encephalopathy, and cerebrovascular disease. These associations were not found when using epigenetic-based measures of age acceleration. Conclusions: These data indicate that estimated histopathologic brain age can be used as a reliable pathologic correlate to identify factors that contribute to accelerated or decelerated brain aging.

Recommended citation: Gabriel Marx, Andrew McKenzie, Justin Kauffman, Daniel Koenigsberg, Kurt Farrell, John Crary Histopathologic Brain Age Estimation via Deep Multiple Instance Learning (P3-6.008). Neurology Apr 2023, 100 (17 Supplement 2) 3206; DOI: 10.1212/WNL.0000000000203100 https://n.neurology.org/content/100/17_Supplement_2/3206.abstract

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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