Listed here are selected scientific publications authored or co-authored by staff scientists at the Center for Neurosciences at The Feinstein Institutes for Medical Research.

Publications are grouped by disease category.

The disease categories are as follows:

Parkinson’s Disease, Selected Publications, 2018–present


  1. Ko JH, Spetsieris PG, Eidelberg D. Network structure and function in Parkinson’s disease. Cereb Cortex, 2018; 28(12):4121-4135
  2. Argyelan M, Herzallah M, Sako W, DeLucia I, Sarpal D, Vo A, Fitzpatrick T, Moustafa AA, Eidelberg D, Gluck M. Dopamine modulates striatal response to reward and punishment in patients with Parkinson’s disease: a pharmacological challenge fMRI study. Neuroreport, 2018; 29(7):532-540
  3. Tomše P, Peng S, Pirtošek Z, Zaletel K, Dhawan V, Eidelberg D, Ma Y, Trošt M. The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson’s disease. Physica Medica: European Journal of Medical Physics, 2018; 52:104-112
  4. Schindlbeck K, Eidelberg D. Network imaging biomarkers: insights and clinical applications in Parkinson’s disease. Lancet Neurol 2018; 17(7):629-640 [Review]
  5. Niethammer M, Tang CC, Vo A, Nguyen N, Spetsieris P, Dhawan V, Ma Y, Small M, Feigin A, During MJ, Kaplitt MG, Eidelberg D. Gene therapy mitigates Parkinson’s disease symptoms by reorganizing functional brain connectivity. Sci Transl Med, 2018; 10(469):eaau0713
  6. Spetsieris PG, Dhawan V, Eidelberg D. Visualizing network connectivity in Parkinson’s disease. In: Proceedings – International Conference on Image Processing, ICIP. IEEE Computer Society. 2018; 724-728
  7. Schindlbeck KA, Eidelberg D. Serotonergic pathology and Braak’s staging hypothesis in Parkinson’s disease. Lancet Neurol 2019;18(8):713-714
  8. Cenci MA, Riggare S, Pahwa R, Eidelberg D, and Hauser RA. Dyskinesia matters. Mov Disord 2020; 35(3): 392-396
  9. Schindlbeck KA, Vo A, Nguyen N, Tang CC, Niethammer M, Dhawan V, Brandt V, Saunders-Pullman R, Bressman SB, Eidelberg D. LRRK2 and GBA variants exert distinct influences on Parkinson’s disease-specific metabolic networks. Cereb Cortex, 2020; 30(5):2867-2878
  10. Schindlbeck KA, Lucas-Jiménez O, Tang CC, Morbelli S, Arnaldi D, Pardini M, Pagani M, Ibarretxe-Bilbao N, Ojeda N, Nobili F, Eidelberg D. Metabolic network abnormalities in drug-naïve Parkinson’s disease. Mov Disord, 2020; 35(4):587-594
  11. Mishra VR, Sreenivasa KR, Yang Z, Zhuang X, Cordes D, Mari Z, Litvan I, Fernandez H, Eidelberg D, Ritter A, Cummings J, Walsh RR. Unique white matter structural connectivity in early stage, drug naïve Parkinson’s disease. Neurology, 2020; 94(8):e774-e784
  12. Rus T, Tomse P, Jensterle L, Lezaic L, Limback Stokin C, Popovic M, Tang CC, Eidelberg D, Pirtosek Z, Trost M. Atypical clinical presentation of pathologically proven Parkinson’s disease: The role of Parkinson’s disease related metabolic pattern. Parkinsonism Relat Disord, 2020; 78:1-3
  13. Tang CC, Holtbernd F, Ma Y, Spetsieris G, Oh A, Fink GR, Timmermann L, Eggers C, Eidelberg D. Hemispheric network expression in Parkinson’s disease: Relationship to dopaminergic asymmetries. J Parkinsons Dis, 2020; 10(4):1737-1749
  14. Greuel A, Trezzi J-P, Glaab E, Ruppert MC, Maier F, Jager C, Hodak Z, Lohmann K, Ma Y, Eidelberg D, Timmermann L, Hiller K, Tittgemeyer M, Drzezga A, Diederich N, Eggers C. GBA variants in Parkinson’s disease: Clinical, metabolomic, and multimodal neuroimaging phenotypes. Mov Disord, 2020; 35(12):2201-2210
  15. Spetsieris PG, Eidelberg D. Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson’s disease. Neuroimage, 2021; 226:117568
  16. Fujita K, Peng S, Ma Y, Tang CC, Hellman M, Feigin A, Eidelberg D, Dhawan V. Blood–brain barrier permeability in Parkinson’s disease patients with and without dyskinesia. J Neurol, 2021; 268(6):2246-2255
  17. Greene PE, Fahn S, Eidelberg D, Bjugstad K, Breeze RE, Freed CR. Persistent dyskinesias in patients with fetal tissue transplantation for Parkinson disease. NPJ Parkinsons Dis. 2021; 7(1):38
  18. Peng S, Tang C, Schindlbeck K, Rydzinski Y, Dhawan V, Spetsieris PG, Ma Y, Eidelberg D. Dynamic 18F-FPCIT PET: Quantification of Parkinson’s disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session. J Nucl Med. 2021; 62(12):1175-1782
  19. Schindlbeck KA, Vo A, Mattis PJ, Villringer K, Marzinzik F, Fiebach JB, Eidelberg D. Cognition-related functional topographies in Parkinson’s disease: Localized loss of the ventral default mode network. Cereb Cortex, 2021; 31(11):5139-5150
  20. Rommal A, Vo A, Schindlbeck KA, Greuel A, Ruppert MC, Eggers C, Eidelberg D. Parkinson’s disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study. Neuroimage Rep, 2021; 1(3):100026
  21. Vo A, Schindlbeck KA, Nguyen N, Rommal A, Spetsieris PG, Tang CC, Choi YY, Niethammer M, Dhawan V, Eidelberg D. Adaptive and pathological connectivity responses in Parkinson’s disease brain networks. Cereb Cortex, 2022 March 25; Epub ahead of print
  22. Dhawan V, Niethammer MH, Lesser ML, Pappas KN, Hellman M, Fitzpatrick TM, Bjelke D, Singh J, Quartarolo LM, Choi YY, Oh A, Eidelberg D, Chaly T. Prospective F-18 FDOPA PET Imaging Study in Human PD. Nucl Med Mol Imaging, 2022; 56(3):147-157
  23. Rus T, Schindlbeck KA, Tang CC, Vo A, Dhawan V, Trošt M, Eidelberg D. Stereotyped relationship between motor and cognitive metabolic networks in Parkinson’s disease. Mov Disord, 2022; in press

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Dystonia, Selected Publications, 2015–present


  1. Vo A, Sako W, Dewey SL, Eidelberg D, Uluğ AM. 18FDG-microPET and MR DTI findings in Tor1a+/- heterozygous knock-out mice. Neurobiol Dis, 2015; 73:399-406
  2. Vo A, Sako W, Niethammer M, Carbon M, Bressman SB, Ulug AM, Eidelberg D. Thalamocortical connectivity correlates with phenotypic variability in dystonia. Cereb Cortex, 2015; 25(9): 3086-3094
  3. Sako W, Fujita K, Vo A, Rucker JC, Rizzo J-R, Niethammer N, Carbon M, Bressman SB, Uluğ AM, Eidelberg D. The visual perception of natural motion: abnormal task-related neural activity in DYT1 dystonia. Brain, 2015; 138(Pt 12):3598-3609
  4. Fujita K, Eidelberg D. Imbalance of the direct and indirect pathways in focal dystonia: a balanced view. Brain, 2017; 140(12):3075-3077 [Editorial]
  5. Shakkottai VG, Bhatla A, Bhatia K, Dauer W, Dresel C, Niethammer M, Eidelberg D, et al. Current opinions and areas of consensus on the role of the cerebellum in dystonia. Cerebellum, 2017; 16(2):577-594 [Review]
  6. Fujita K, Sako W, Vo A, Bressman SB, Eidelberg D. Disruption of network for visual perception of natural motion in primary dystonia. Hum Brain Mapp, 2018; 39(3):1163-1174
  7. Vo A, Nguyen N, Fujita K, Schindlbeck KA, Rommal A, Bressman SB, Niethammer M, Eidelberg D. Disordered network structure and function in dystonia: Pathological connectivity vs. adaptive responses. [2022; preprint]: https://www.researchsquare.com/article/rs-1858543/v1;  suppl. material: Vo_JNeurosci_OnlineSupplMat

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Movement Disorders, Selected Publications, 2017–present


  1. Ko JH, Lee CS, Eidelberg D. Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations. J Cereb Blood Flow Metab, 2017; 37(2):683-693
  2. Strafella AP, Bohnen NI, Perlmutter JS, Eidelberg D, et al. Molecular imaging to track Parkinson’s disease and atypical parkinsonisms: New imaging frontiers. Mov Disord, 2017; 32(2):181-192 [Cited by Joseph Jankovic and Arjun Tarakad in Faculty of 1000]
  3. Ge J, Wu J, Peng S, Wu P, Wang J, Zhang H, Guan Y, Eidelberg D, Zuo C, Ma Y. Reproducible network and regional topographies of abnormal glucose metabolism associated with progressive supranuclear palsy: multivariate and univariate analyses in American and Chinese patient cohorts. Hum Brain Mapp, 2018; 39(7): 2842-2858
  4. Walsh RR, Krismer F, Galpern WR, Wenning GK, Low PA, Halliday G, Koroshetz WJ, Holton J, Quinn NP, Rascol O, Shaw LM, Eidelberg D, et al. Recommendations of the Global Multiple System Atrophy Research Roadmap Meeting. Neurology 2018; 90(2):74-82 [Review]
  5. Niethammer M, Eidelberg D. Network imaging in parkinsonian and other movement disorders: Network dysfunction and clinical correlates. Int Rev Neurobiol 2019;144:143-184
  6. Rus T, Tomše P, Rus T, Perovnik M, Tang CC, Eidelberg D, Trošt M. Use of positron-emission tomography as a diagnostic and differential diagnostic tool in parkinsonian syndromes. In: Diagnosis and Management in Parkinson’s Disease: The Neuroscience of Parkinson’s Disease, Volume 1, Martin CR and Preedy VR, editors; Elsevier Inc. 2020:313-329
  7. P, Jensterle L, Grmek M, Pirtosek Z, Eidelberg D, Tanc C, Trošt M. Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated – metabolic brain patterns’ based approach. Eur J Nucl Med Mol Imaging, 2020; 47(12):2901-2910
  8. Tomše P, Rus T, Perovnik M, Tang CC, Eidelberg D, Trošt M. Use of positron-emission tomography as a diagnostic and differential diagnostic tool in parkinsonian syndromes. In: Diagnosis and Management in Parkinson’s Disease: The Neuroscience of Parkinson’s Disease, Volume 1, Martin CR and Preedy VR, editors; Elsevier Inc. 2020:313-329
  9. Shen B, Wei S, Ge J, Peng S, Liu F, Li L, Guo S, Wu P, Zuo C, Eidelberg D, Wang J, Ma Y. Reproducible metabolic topographies associated with Multiple System Atrophy: Network and regional analyses in Chinese and American cohorts. Neuroimage Clin, 2020; 28:102416
  10. Schindlbeck KA, Deepak GK, Tang CC, O’Shea SA, Poston KL, Choi YY, Dhawan V, Vonsattel JP, Fahn S, Eidelberg D. Neuropathological correlation supports automated image-based differential diagnosis parkinsonism. Eur J Nucl Med Mol Imaging. 2021; 48(11):3522-3529
  11. Papathoma P-E, Markaki I, Tang CC, Lilja Lindström M, Savitcheva I, Eidelberg D, Svenningsson P. A replication study, systematic review and meta-analysis of automated image-based diagnosis in parkinsonism. Sci Rep, 2022; 12(1):2763
  12. Tomše P, Rebec E, Studen An, Perovnik M, Rus T, Ležaić L, Tang CC, Eidelberg D, Trošt M. Abnormal metabolic covariance patterns associated with multiple system atrophy and progressive supranuclear palsy. Phys Med, 2022; 98:131-138
  13. Peng S, Dhawan V, Eidelberg D, Ma Y. Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders. Bioelectron Med 2021; Mar 30;7(1):4

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Brain Imaging in Neurodegenerative Disorders, Selected Publications, 2017–present


  1. Niethammer M, Eidelberg D. Functional Imaging in Huntington’s disease. In: Handbook of Clinical Neurology, 3rd Series, Feigin A and Anderson K, editors; Elsevier, Inc., 2017; 144: 263-287 [Book Chapter]
  2. Nazem A, Tang CC, Spetsieris P, Dresel C, Gordon ML, Dieh-Schmid J, Grimmer T, Yakushev I, Mattis PJ, Ma Y, Dhawan V, Eidelberg D. Quantitative imaging assessment for behavioral variant frontotemporal dementia: A two-center multivariate FDG PET study. Alzheimers Dement (Amst), 2018; 10:583-594
  3. Blazhenets G, Ma Y, Sörensen A, Rücker G, Schiller F, Eidelberg D, Frings L, Meyer PT for the Alzheimer’s Disease Neuroimaging Initiative. Principal component analysis of brain metabolism predicts development of Alzheimer’s dementia. J Nucl Med, 2019; 60(6):837-843
  4. Blazhenets G, Ma Y, Sörensen A, Schiller F, Rücker G, Eidelberg D, Frings L, Meyer PT. Predictive value of 18F-Florbetapir and 18F-FDG PET for conversion from mild cognitive impairment to Alzheimer dementia. J Nucl Med, 2020; 61(4):597-603
  5. Blazhenets G, Frings L, Ma Y, Sorensen A, Eidelberg D, Wiltfang J, Meyer PT, Alzheimer’s Disease Neuroimaging Initiative. Validation of the Alzheimer disease dementia conversion-related pattern as an ATN biomarker of neurodegeneration. Neurology, 2021; 96(6):e1358-e1368
  6. Perovnik M, Tomše P, Jamšek J, Tang C, Eidelberg D, Trošt M. Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer’s disease topography. Neuroimage Clin, 2022; 35:103080
  7. Perovnik M, Tomše P, Jamšek J, Emeršič, A, Tang C, Eidelberg D, Trošt M. Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients. Sci Rep, 2022; 12(1):11752

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Other, Selected Publications, 2017–present


  1. Eidelberg D, Léger JM. Editorial: Introducing the new Co-Editors-in-Chief of Current Opinion in Neurology. Curr Opin Neurol, 2017; 30(3):207
  2. Sidtis JJ, Van Lancker Sidtis D, Dhawan V, Eidelberg D. Switching language modes: Complementary brain patterns for formulaic and propositional language. Brain Connect, 2018; 8(3): 189-196
  3. Zhang N, Gordon ML, Ma Y, Chi B, Gomar JJ, Peng S, Kingsley PB, Eidelberg D, Goldberg TE. The age-related perfusion pattern measured with arterial spin labeling MRI in healthy subjects. Front Aging Neurosci, 2018; 10: article 214
  4. Mackay M, Vo A, Tang CC, Small M, Anderson EW, Ploran EJ, Storbeck J, Bascetta B, Kang S, Aranow C, Sartori C, Watson P, Volpe BT, Diamond B, Eidelberg D. Metabolic and microstructural alterations in the SLE brain correlate with cognitive impairment. JCI Insight, 2019; 4(1):e124002
  5. Ploran E, Tang C, Mackay M, Small M, Anderson E, Storbeck J, Bascetta B, Kang S, Aranow C, Sartori C, Watson P, Volpe B, Diamond B, Eidelberg D. Assessing cognitive impairment in SLE: examining relationships between resting glucose metabolism and anti-NMDAR antibodies with navigational performance. Lupus Sci Med, 2019; 6(1):e000327
  6. Sidtis JJ, Van Lancker Sidtis D, Dhawan V, Tagliati M, Eidelberg D. Stimulation of the subthalamic nucleus changes cortical-subcortical blood flow patterns during speech: A positron emission tomography study. Front Neurol, 2021; 12:684596
  7. Rothman JE, Eidelberg D, Rothman SL, Holford TR, Rothman DL. Analysis of the time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values. PloS One 2021; 16(8):e0253843
  8. Mader S, Brimberg L, Vo A, Strohl JJ, Crawford JM, Bonnin A, Carrion J, Campbell D, Heurta TS, La Bella A, Berlin R, Dewey SL, Hellman M, Eidelberg D, Dujmovic I, Drulovic J, Bennett JL, Volpe BR, Heurta PT, Diamond B. In utero exposure to maternal anti-aquaporin-4 antibodies alters brain vasculature and neural dynamics in male mouse offspring. Sci Transl Med, 2022; 14(641):eabe9726

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