Publications

J. Torok, P.D. Maia, C. Anand, and A. Raj,   A meta-analysis of mouse models of  tauopathy reveals the cellular underpinnings of selective regional vulnerability,  under review. 

N. Fernando, M. Robison, and P.D. Maia,   Analysis of goal, feedback and reward on sustained attention via machine learning,  under review.

K. Aqel, Z. Wang, Y.B. Peng, and P.D. Maia,   Reconstructing rodent brain signals during euthanasia with Eigensystem Realization Algorithm (ERA),  Scientific Reports (2024), 14: 12261, pp. 1-11, Springer Nature.    link  

C. Anand,  P. D. Maia ,  J. Torok , C. Mesias, and  A. Raj,   The effects of microglia on tauopathy progression can be quantified using Nexopathy in silico (Nexis) models,  Scientific Reports (2022), 12:21170, pp. 1-14, Springer Nature.    link 

S. Pandya, P.D. Maia, B. Freeze, R.A.L. Menke, K. Tallbot, M.R. Turner, A. Raj,    Modeling seeding and neuroanatomical spread of pathology in amyotrophic lateral sclerosis,  NeuroImage (2022), 251, pp. 1-12.    link 

* P. D. Maia , S. Pandya, B. Freeze, J. Torok, A. Gupta, Y. Zeighami, and A. Raj,   Origins of atrophy in Parkinson linked to early onset and local transcription patterns,  Brain Communications (2020).    link

B. Freeze , P. D. Maia , S. Pandya , and A. Raj ,   Network control of pathology in sporadic Creutzfeldt-Jakob disease,  Brain Communications (2020).   link

A. A. Nguyen , P. D. Maia , X. Gao, P.F. Damasceno, and A. Raj,  Dynamical role of pivotal brain regions in Parkinson symptomatology uncovered with deep learning,  Brain Sciences (2020), 10,73.    link

J. Torok , P. D. Maia , F. Powell, S. Pandya, and  A. Raj,  A method for inferring regional origins of neurodegeneration,  BRAIN (2018), 141:3,  pp. 863-876.   link 

J. Crodelle and  P. D. Maia,   A computational model for pain processing in the dorsal horn following axonal damage to receptor fibers,  Brain Sciences (2021),  11:4, 505;    link   

C. Delahunt , P. D. Maia  and  J. N. Kutz,   Built to last: functional and structural mechanisms in the moth olfactory network that mitigate effects of neural injury,  Brain Sciences (2021), 11:4, 462.    arXiv  &  link 

B. Lusch , J. Weholt , P. D. Maia  and  J. N. Kutz,  Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks,  Brain and Cognition (2018),   123, pp. 154-164.    arXiv  &  link

M. Weber , P. D. Maia  and  J. N. Kutz,  Estimating memory deterioration rates following neurodegeneration and traumatic brain injuries in a Hopfield Network Model,  Frontiers in Neuroscience (2017),  11:623, pp. 1-8.   arXiv  &  link

M. Morrison , P. D. Maia  and  J. N. Kutz,  Preventing neurodegenerative memory loss in Hopfield neuronal networks using cerebral organoids or external microelectronics,  Computational and Mathematical Methods in Medicine (2017), pp: 1-13.   link   

* P. D. Maia  and  J. N. Kutz,  Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injury and neurodegenerative diseases,  Journal of  Computational Neuroscience (2017), 42:3, pp. 323-347.   link 

J. Kunert , P. D. Maia  and  J. N. Kutz,  Functionality and robustness of injured connectomic dynamics in C. elegans: linking behavioral deficits to neural circuit damage,  PLOS Computational Biology (2017), 13:1, pp. 1-22.   link  

 S. Rudy , P. D. Maia  and  J. N. Kutz,  Cognitive and behavioral deficits arising from neurodegeneration and traumatic brain injury: a model for the underlying role of focal axonal swellings in neuronal networks with plasticity,  Journal of Systems and Integrative Neuroscience  (2016).   link

J. Torok , P. D. Maia , P. Verma , C. Mesias , and A. Raj,   Emergence of directional bias in tau deposition from axonal transport dynamics,  PLOS Computational Biology (2021), 17:7.    link 

P. D. Maia , A. Raj  and  J. N. Kutz,  Slow-gamma frequencies are optimally guarded against neurodegenerative diseases and traumatic brain injury: consequences for neural encoding and working memory,  Journal of Computational Neuroscience  (2019), 47, pp. 1-16.   link

 P. D. Maia ,  M. A. Hemphill , B. Zehnder , C. Zhang , K. K. Parker  and  J. N. Kutz,  Diagnostic tools for evaluating the impact of focal axonal swellings arising in neurodegenerative diseases and/or traumatic brain injury,  Journal of  Neuroscience Methods  (2015),  16:253,  pp. 233-243.   link

* P. D. Maia  and  J. N. Kutz,  Compromised axonal functionality after neurodegeneration, concussion and/or traumatic brain injury,  Journal of Computational Neuroscience  (2014),  37:2,  pp. 317-332.  link

P. D. Maia  and  J. N. Kutz,  Identifying critical regions for spike propagation in axon segments,  Journal of Computational Neuroscience  (2014),  36:2,  pp. 141-155.  link

J. Torok , C. Mesias , P. D. Maia , E. Markley,  and A. Raj,   Matrix inversion and subset selections (MISS): a novel pipeline for quantitative mapping of diverse cell types across the murine brain,  PNAS (2022),  119 (14).  link 

C. Silva,  P.D. Maia, L. Stolerman, V. Rolla, and L. Velho,   Predicting Dengue outbreaks in Brazil with manifold learning on climate data,  Expert Systems with Applications (2022).  link  

L. Stolerman , P. D. Maia  and  J. N. Kutz,  Forecasting dengue fever in Brazil: an assessment of climate conditions ,  PLOS ONE (2019),  pp.  1-16.  arXiv  & link

B. Lusch , P. D. Maia  and  J. N. Kutz,  Inferring connectivity in networked dynamical systems: Challenges using granger causality,  Physical Review E (2016),  94:3,  pp. 1-14.  link 

M. Doumic , P. D. Maia  and  J. P. Zubelli,  On the calibration of a size-structured population model from experimental data,  Acta Biotheoretica  (2010),  50:4,  pp. 405-413.  link 

J. Torok,   P. D. Maia,  P. Verma,  C. Mesias,  and A. Raj,    Axonal transport dynamics explains directional bias in tau deposition ,  Alzheimer's Association International Conference  (2021).   link 

C. Anand,  P. D. Maia ,  J. Torok , C. Mesias, and  A. Raj,    The effect of microglial genes on network diffusion of pathology in mouse models of tauopathy , Alzheimer's Association International Conference  (2021).    link 

P. Damasceno , R. La Joie,  P. D. Maia , A. Visani,  L. Iaccarino,  A. Strom,  L. Edwards,  M. Tempini, W. Jagust, B. Miller, G. Rabinovici, and  A. Raj,   Colocalization of atrophy and tau improves AI classification of Alzheimer phenotypical variants,  Alzheimer's & Dementia (2020).   link  

P. D. Maia  and  J. N. Kutz,  Axonal injuries and consequences to neuronal computation,  BMS Neuroscience  (2013),  pp. 14.   link