These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

107 related articles for article (PubMed ID: 38657465)

  • 1. Modeling and simulation for prediction of multiple sclerosis progression.
    Prathapan V; Eipert P; Wigger N; Kipp M; Appali R; Schmitt O
    Comput Biol Med; 2024 Jun; 175():108416. PubMed ID: 38657465
    [TBL] [Abstract][Full Text] [Related]  

  • 2. MR g-ratio-weighted connectome analysis in patients with multiple sclerosis.
    Kamagata K; Zalesky A; Yokoyama K; Andica C; Hagiwara A; Shimoji K; Kumamaru KK; Takemura MY; Hoshino Y; Kamiya K; Hori M; Pantelis C; Hattori N; Aoki S
    Sci Rep; 2019 Sep; 9(1):13522. PubMed ID: 31534143
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Reaction-diffusion models in weighted and directed connectomes.
    Schmitt O; Nitzsche C; Eipert P; Prathapan V; Hütt MT; Hilgetag CC
    PLoS Comput Biol; 2022 Oct; 18(10):e1010507. PubMed ID: 36306284
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Thresholding functional connectomes by means of mixture modeling.
    Bielczyk NZ; Walocha F; Ebel PW; Haak KV; Llera A; Buitelaar JK; Glennon JC; Beckmann CF
    Neuroimage; 2018 May; 171():402-414. PubMed ID: 29309896
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups.
    Tozlu C; Jamison K; Gu Z; Gauthier SA; Kuceyeski A
    Neuroimage Clin; 2021; 32():102827. PubMed ID: 34601310
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis.
    Meier JM; van der Burgh HK; Nitert AD; Bede P; de Lange SC; Hardiman O; van den Berg LH; van den Heuvel MP
    Ann Neurol; 2020 May; 87(5):725-738. PubMed ID: 32072667
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Microstructure-Weighted Connectomics in Multiple Sclerosis.
    Bosticardo S; Schiavi S; Schaedelin S; Lu PJ; Barakovic M; Weigel M; Kappos L; Kuhle J; Daducci A; Granziera C
    Brain Connect; 2022 Feb; 12(1):6-17. PubMed ID: 34210167
    [No Abstract]   [Full Text] [Related]  

  • 8. Predicting individual brain functional connectivity using a Bayesian hierarchical model.
    Dai T; Guo Y;
    Neuroimage; 2017 Feb; 147():772-787. PubMed ID: 27915121
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Analysis of brain functional connectivity network in MS patients constructed by modular structure of sparse weights from cognitive task-related fMRI.
    Miri Ashtiani SN; Behnam H; Daliri MR; Hossein-Zadeh GA; Mehrpour M
    Australas Phys Eng Sci Med; 2019 Dec; 42(4):921-938. PubMed ID: 31452057
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis.
    Kuceyeski A; Monohan E; Morris E; Fujimoto K; Vargas W; Gauthier SA
    Neuroimage Clin; 2018; 19():417-424. PubMed ID: 30013921
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Structural disconnection is responsible for increased functional connectivity in multiple sclerosis.
    Patel KR; Tobyne S; Porter D; Bireley JD; Smith V; Klawiter E
    Brain Struct Funct; 2018 Jun; 223(5):2519-2526. PubMed ID: 29453522
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis.
    Shu N; Duan Y; Xia M; Schoonheim MM; Huang J; Ren Z; Sun Z; Ye J; Dong H; Shi FD; Barkhof F; Li K; Liu Y
    Sci Rep; 2016 Jul; 6():29383. PubMed ID: 27403924
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Reduced clinical connectome fingerprinting in multiple sclerosis predicts fatigue severity.
    Cipriano L; Troisi Lopez E; Liparoti M; Minino R; Romano A; Polverino A; Ciaramella F; Ambrosanio M; Bonavita S; Jirsa V; Sorrentino G; Sorrentino P
    Neuroimage Clin; 2023; 39():103464. PubMed ID: 37399676
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Combining multiple connectomes improves predictive modeling of phenotypic measures.
    Gao S; Greene AS; Constable RT; Scheinost D
    Neuroimage; 2019 Nov; 201():116038. PubMed ID: 31336188
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection.
    Van Schependom J; Gielen J; Laton J; D'hooghe MB; De Keyser J; Nagels G
    Neuroimage Clin; 2014; 4():403-10. PubMed ID: 24567912
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.
    Reeve K; On BI; Havla J; Burns J; Gosteli-Peter MA; Alabsawi A; Alayash Z; Götschi A; Seibold H; Mansmann U; Held U
    Cochrane Database Syst Rev; 2023 Sep; 9(9):CD013606. PubMed ID: 37681561
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The missing link: Predicting connectomes from noisy and partially observed tract tracing data.
    Hinne M; Meijers A; Bakker R; Tiesinga PH; Mørup M; van Gerven MA
    PLoS Comput Biol; 2017 Jan; 13(1):e1005374. PubMed ID: 28141820
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization.
    Serra A; Galdi P; Pesce E; Fratello M; Trojsi F; Tedeschi G; Tagliaferri R; Esposito F
    Int J Neural Syst; 2019 Sep; 29(7):1950007. PubMed ID: 30929575
    [TBL] [Abstract][Full Text] [Related]  

  • 19. On the accuracy and computational cost of spiking neuron implementation.
    Valadez-Godínez S; Sossa H; Santiago-Montero R
    Neural Netw; 2020 Feb; 122():196-217. PubMed ID: 31689679
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 6.