PhD - Biophysics
CINVESTAV and NIH - 1991
MSc. - Biophysics
Polytechnic (CINVESTAV). Mexico - 1982
BSc - Neurophysiology
Universidad Anahuac. Mexico - 1978
Human-based research associated with Neurological disease and aging.
The goal is to find biomarkers that will allow us the early detection of those vulnerable to develop Alzheimer's disease.
The goal is to associate brain dynamics to motor and non-motor signs and symptoms present in PD.
The goal is to differentiate healthy aging to aging associated with neurodegenertion.
Computational Neuroscience and Biophysics
The methodological approach we use is mathematics associated with physics principles. In order to do this, we make use of parallel computing.
Monroe DC, Blumenfeld RS, Keator DB, Solodkin A, Small SL. One season of head-to-ball impact exposure alters functional connectivity in a central autonomic network. Neuroimage. 2020 Aug 28;223:117306. doi: 10.1016/j.neuroimage.2020.117306. Epub ahead of print. PMID: 32861790. - publications
Stefanovski L, Triebkorn P, Spiegler A, Diaz-Cortes MA, Solodkin A, Jirsa V, et al. (2019) Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease. Frontiers in computational neuroscience. 13:54. PMID: 31456676 - publications
Kruggel F, Solodkin A. (2019) Determinants of structural segregation and patterning in the human cortex. Neuroimage. 196:248-60. PMID: 30995518 - publications
Zimmermann J, Perry A, Breakspear M, Schirner M, Sachdev P, Wen W, Kochan NA, Mapstone M, Ritter P, McIntosh AR, Solodkin A. (2018). Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models. Neuroimage Clinical. 19: 240-251. PMID: 30035018 - publications
Falcon MI, Riley JD, Jirsa V, McIntosh AR, Shereen AD, Chen EE, Solodkin A (2015). The Virtual Brain: Modeling biological correlates of recovery after chronic stroke. Front Neurol. 6:228.PMID: 26579071 - publications
Kruggel F, Masaki F, Solodkin A, Alzheimer's Disease Neuroimaging Initiative. (2017). Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study. J Neurosci Methods 278:101-115. PMID: 28057473 - publications
Bezgin G, Solodkin A, Bakker R, Ritter P, McIntosh AR. (2017). Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains". Hum Brain Mapp 38(4):2080-2093.PMID: 28054725 - publications
Falcon MI, Jirsa V, Solodkin A. (2016). A new neuroinformatics approach to personalized medicine in Neurology: The Virtual Brain. Curr Opin Neurol. 29(4):429-36. PMID: 27224088 - publications
Falcon MI, Riley JD, Jirsa V, McIntosh AR, Chen EE, Solodkin A (2016). Functional mechanisms of Recovery after Chronic Stroke: Modeling with The Virtual Brain. eNeuro. 4;3(2). PMID: 27088127 - publications
Early diagnosis of AD with computational biomarker.
Project is done in collaboration with V Jirsa in Marseille and AR McIntosh in Toronto
Brain dynamics associated with Cognitive impairment in PD
Project is done in collaboration with K Chen (Toronto, Canada) and N Phielipp (Irvine. CA)
Brain dynamics associated with healthy and abnormal aging
Project in collaboration with AR McIntosh (Toronto, Canada) and V Jirsa (Marseille, France)
Brain dynamics associated with altered conscious states.
Project generated by Dr Sharma (UTSW) and done in collaboration with G Deco (Barcelona, Spain)
For more than 25 years, my research has focused on the discovery of anatomical and physiological substrates of neurological disease that have a reasonable likelihood of leading to therapeutic interventions. My research has been instrumental in the conceptualization and development of analytical tools, from anatomical neuropathology to computational models associated with mechanisms of disease, Biographical Summary
Following a PhD in Biophysics from the Polytechnic in Mexico City and the NIH, I joined as a postdoctoral fellow the Cognitive Neurology group at the University of Iowa under the direction of Dr. Antonio Damasio, At that time, I received direct training with Dr. Gary van Hoesen in the area of neuroanatomical pathology, with emphasis on dementia. My work concentrated on the brain structures affected by pathological alterations associated to abnormal Tau accumulation in Alzheimer’s disease. During those years, I published a number of papers in prominent journals on the pathological alterations in these structures. One important result was expanding on the notion that the axonal damage by Tau in the perforant pathway connecting entorhinal cortex with hippocampus, resulted in an almost complete disconnection of the latter from the cerebral cortex. This "functional isolation" of the hippocampus explained for the first time, the structural basis for some of the memory and emotional deficits of AD.
Neuropathological studies based on post-mortem autopsies albeit highly relevant, lacked however a direct physiological correlate. Hence, I decided to complete my scientific approach by adding Magnetic Resonance Imaging (MRI) including functional MRI to my studies. This opened the unique opportunity to expand on the neuropathological studies in vivo via the detection of imaging changes associated with disease onset in neurodegenerative diseases. Indeed, one of the first studies merging postmortem neuropathology with imaging approaches was based on the assessment of the integrity of the perforant pathway in older patients with AD or with small cognitive declines. This study, based on Diffusion tensor MR imaging, sought not only to detect the damaged tract in AD patients but more importantly, it sought to detect subtle changes produced by Tau before the clinical diagnosis of AD.
This search for disease biomarkers has continued for many years and took a new and exciting turn with an approach developed in collaboration with AR McIntosh (Toronto), V. Jirsa (Marseille) P. Ritter (Berlin) and G. Deco (Barcelona). We established a neuroinformatics platform TheVirtualBrain (TVB) for the assessment of large scale brain dynamics based on individual structural anatomical connections. That is, TVB uses empirical neuroimaging data to create dynamic models of the human brain. The models contain the anatomical connectivity between parts of the brain and the dynamics of local neural populations. TVB uses structural MRI data to create the custom brain surface, diffusion-weighted MRI data to infer the anatomical connections between brain areas, and then functional MRI data as target to modify the parameters of the model to reproduce the observed functional data. The Neuroinformatics architecture houses a library of models, which catalogues the biophysical parameters that produce different empirical brain states. These biophysical parameters are invisible to brain imaging devices, thus TVB acts as a “computational microscope” that allows the inference of internal states and processes of the system. Determining these parameters, validating them, and applying them as individualized predictive biomarkers, has enormous potential to change acute and chronic neurological care of neurological patients. In addition to this model-based analysis, we have a cadre of model-free computational approaches rooted in biophysics allowing for cross validation of modeling results. The expansion of our computational “toolbox” has come with an expansion of neurological diseases to explore, from spinocerebellar ataxia to Parkinson’s disease and altered conscious states.