By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
News & events

Keep up with the latest insights and events

Subscribe to hear the latest about NordicNeuroLab

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
July 19, 2024

"Crowdstrike IT" Outage public Statement - NordicNeuroLab are not Affected.

Due to a critical outage worldwide on 19.07.2024, we would like to give a public statement to our clients, partners and stakeholders.

January 25, 2024

The release of nordicAktiva v2.0

Stimulus presentation made easy - the journey to nordicAktiva v2.0

November 6, 2023

NordicNeuroLab partner with Gold Standard Phantoms

Bergen, Norway, September 21st 2023. NordicNeuroLab and Gold Standard Phantoms are proud to announce a commercial partnership to enhance the quality of fMRI studies.

December 22, 2022

CEO's Summary of 2022

Bergen, Norway December 20th 2022

January 13, 2022

Introduction to Arrington ViewPoint EyeTracking Software

NordicNeuroLab has formally tested the VisualSystem HD for compatibility with the ViewPoint EyeTracker software by Arrington Research, Inc.

January 10, 2022

NordicNeuroLab Summary of 2021

As 2021 has come to a close it is a good time to look back at our accomplishments over the last year and shed some light on our future plans.


Articles published
by our customers


Ruiz-Tagle,  A., Figueiredo, P., Pinto, J., Vilela, P., Martins, I.P. and Gil-Gouveia, R.,  2023. Working memory during spontaneous migraine attacks: an fMRI  study.Neurological Sciences, pp.1-8.    

Graeff, P., Ruscheweyh, R. and  Flanagin, V.L., 2023. Longitudinal changes in human supraspinal processing  after RIII-feedback training to improve descending pain inhibition.NeuroImage,283, p.120432.    

Popovich, C., Grau, A.S.,  Shih, C.H., Chidiac, N.T., Zhou, A., Wang, X. and Xie, H., 2023. Changes in  fear-associated learning task brain activation over the COVID-19 pandemic  period: a preliminary longitudinal analysis.Frontiers  in Psychiatry,14.    

Dąbkowska-Mika, A., Steiger,  R., Gander, M., Haid-Stecher, N., Fuchs, M., Sevecke, K. and Gizewski, E.R.,  2023. Evaluation of visual food stimuli paradigms on healthy adolescents for  future use in fMRI studies in anorexia nervosa.Journal  of Eating Disorders,11(1), pp.1-10.    

Olkoniemi, H., Hurme, M. and  Railo, H., 2023.

Neurologically healthy humans’ ability to make saccades  toward unseen targets.Neuroscience,513,  pp.111-125.    

Lohia, K., Soans, R.S.,  Saxena, R., Mahajan, K. and Gandhi, T.K., 2023. Distinct rich and diverse  clubs regulate coarse and fine binocular disparity processing: Evidence from  stereoscopic task-based fMRI.bioRxiv, pp.2023-10.    

Chiou, R., Cox, C.R. and  Lambon Ralph, M.A., 2023. Bipartite functional fractionation within the  neural system for social cognition supports the psychological continuity of  self versus other.Cerebral Cortex,33(4),  pp.1277-1299.    

Meyer, N.H., Gauthier, B.,  Stampacchia, S., Boscheron, J., Babo-Rebelo, M., Potheegadoo, J., Herbelin,  B., Lance, F., Alvarez, V., Franc, E. and Esposito, F., 2023. Embodiment in  episodic memory through premotor-hippocampal coupling.bioRxiv, pp.2023-09.    

Moon, H.J., Albert, L.P., De  Falco, E., Tasu, C., Gauthier, B., Park, H. and Blanke, O., 2023. Changes in  spatial self-consciousness elicit grid cell-like representation in entorhinal  cortex.bioRxiv,  pp.2023-07.


Prasad, S. et al. (2020). Study of functional magnetic resonance imaging (fMRI) in children and adolescents with specific learning disorder (dyslexia). Asian Journal of Psychiatry, 50, 101945.

Yao, S. et al. (2020). Sex Effect on Presurgical Language Mapping in Patients With a Brain Tumor. Front. Neurosci., 14. Doi: 10.3389/fnins.2020.00004.

Ciaramidaro, A. et al. (2018). Transdiagnostic deviant facial recognition for implicit negative emotion in autism and schizophrenia. European Neuropsychopharmacology, 28(2), pp. 264–275.

Häusler, Alexander N. et al. (2018). Preferences and beliefs about financial risk taking mediate the association between anterior insula activation and self-reported real-life stock trading. Sci Rep, 8(1) doi: 10.1038/s41598-018-29670-6.

Hsu, C.-T. et al. (2018). How mimicry influences the neural correlates of reward: An fMRI study. Neuropsychologia, 116, pp. 61–67.

Olsen, A. et al. (2018). Preterm birth leads to hyper-reactive cognitive control processing and poor white matter organization in adulthood. NeuroImage, 167, pp. 419–428.

Quintana, D. S. et al. (2018). Low-dose intranasal oxytocin delivered with Breath Powered device modulates pupil diameter and amygdala activity: a randomized controlled pupillometry and fMRI study. Neuropsychopharmacology, 44, pp. 306-131.

Preller, K. H. et al. (2018). Role of the 5-HT2A Receptor in Self- and Other-Initiated Social Interaction in Lysergic Acid Diethylamide-Induced States: A Pharmacological fMRI Study. J. Neurosci., 38(14), pp. 3603–3611.

Schultz, J. et al. (2018). Improving therapy outcome prediction in major depression using multimodal functional neuroimaging: A pilot study. Personalized Medicine in Psychiatry, 11, pp. 7–15.

Baumgartner, R. et al. (2017). Evaluation of an fMRI USPIO-based assay in healthy human volunteers. J. Magn. Reson. Imaging, 46(1), pp. 124–133.

Kleiser, R. et al. (2017). An fMRI study of training voluntary smooth circular eye movements. Exp Brain Res, 235(3), pp. 819–831.

Kuniecki, M. et al. (2017). Effects of Scene Properties and Emotional Valence on Brain Activations: A Fixation-Related fMRI Study. Front. Hum. Neurosci., 11. Doi: 10.3389/fnhum.2017.00429.

Mumuni, A. N. & McLean, J. (2017). Dynamic MR Spectroscopy of brain metabolism using a non-conventional spectral averaging scheme. Journal of Neuroscience Methods, 277, pp. 113–121.

Nilsonne, G., Tamm, S., Schwarz, J., Almeida, R., Fischer, H., Kecklund, G., Lekander, M., et al. (2017). Intrinsic brain connectivity after partial sleep deprivation in young and older adults: results from the Stockholm Sleepy Brain study. Sci Rep, 7(1). Doi:10.1038/s41598-017-09744-7.

Wittfoth, D., Preibisch, C., & Lanfermann, H. (2017). Processing of Unattended Emotional Facial Expressions: Correlates of Visual Field Bias in Women. Front. Neurosci., 11. Doi: 10.3389/fnins.2017.00443.

Zhang, B. et al. (2017). Altered task-specific deactivation in the default mode network depends on valence in patients with major depressive disorder. Journal of Affective Disorders, 207, pp. 377–383.

Furlan, M. et al. (2016). An fMRI Investigation of Preparatory Set in the Human Cerebral Cortex and Superior Colliculus for Pro- and Anti-Saccades. PLoS ONE, 11(7), e0158337.

Grosbras, M.-H. (2016). Patterns of Activity in the Human Frontal and Parietal Cortex Differentiate Large and Small Saccades. Front. Integr. Neurosci. doi: 10.3389/fnint.2016.00034

Gupta, S. et al. (2016). BOLD fMRI and DTI in strabismic amblyopes following occlusion therapy. Int Ophthalmol, 36(4), pp. 557–568.

Häusler, Alexander N. et al. (2016). Gain- and Loss-Related Brain Activation Are Associated with Information Search Differences in Risky Gambles: An fMRI and Eye-Tracking Study. eneuro, 3(5), ENEURO.0189-16.2016.

Lee, M. H. et al. (2016). Clinical Resting-state fMRI in the Preoperative Setting. Topics in Magnetic Resonance Imaging, 25(1), pp. 11–18.

Lu, K.-H. et al. (2016). Influences of High-Level Features, Gaze, and Scene Transitions on the Reliability of BOLD Responses to Natural Movie Stimuli. PLoS ONE, 11(8), e0161797.

Preston, C. & Ehrsson, H. H. (2016). Illusory Obesity Triggers Body Dissatisfaction Responses in the Insula and Anterior Cingulate Cortex. Cereb. Cortex, 26(12), pp. 4450–4460.

Wang, C. et al. (2016). Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states. Proc Natl Acad Sci USA, 113(34), pp. 9653–9658.

Ahn, H. et al. (2015). Altered Brain Reactivity to Game Cues After Gaming Experience. Cyberpsychology, Behavior, and Social Networking, 18(8), pp. 474–479.

Enax, L. et al. (2015). Nutrition labels influence value computation of food products in the ventromedial prefrontal cortex. Obesity, 23(4), pp. 786–792.

Hu, Y. et al. (2015). Helping or punishing strangers: neural correlates of altruistic decisions as third-party and of its relation to empathic concern. Front. Behav. Neurosci., 9. Doi: 10.3389/fnbeh.2015.00024.

Kim, D.-Y. et al. (2015). The Inclusion of Functional Connectivity Information into fMRI-based Neurofeedback Improves Its Efficacy in the Reduction of Cigarette Cravings. Journal of Cognitive Neuroscience, 27(8), pp. 1552–1572.

Olsen, A. et al. (2015). Altered Cognitive Control Activations after Moderate-to-Severe Traumatic Brain Injury and Their Relationship to Injury Severity and Everyday-Life Function. Cereb. Cortex, 25(8), pp. 2170–2180.

Ong, J. L. et al. (2015). Co-activated yet disconnected—Neural correlates of eye closures when trying to stay awake. NeuroImage, 118, pp. 553–562.

Fastenrath, M. et al. (2014). Dynamic Modulation of Amygdala-Hippocampal Connectivity by Emotional Arousal. Journal of Neuroscience, 34(42), pp. 13935–13947.

Fransson, P. et al. (2014). Slow fluctuations in eye position and resting-state functional magnetic resonance imaging brain activity during visual fixation. Eur J Neurosci, 40(12), pp. 3828–3835.

Frassle, S. et al. (2014). Binocular Rivalry: Frontal Activity Relates to Introspection and Action But Not to Perception. Journal of Neuroscience, 34(5), pp. 1738–1747.

Hougaard, A. et al. (2014). Interhemispheric differences of fMRI responses to visual stimuli in patients with side-fixed migraine aura. Hum. Brain Mapp., 35(6), pp. 2714–2723.

Gaebler, M. et al. (2014). Behavioural and neural correlates of self-focused emotion regulation in social anxiety disorder. J Psychiatry Neurosci, 39(4), 249–258.

Kong, D. et al. (2014). Sleep deprivation reduces the rate of rapid picture processing. NeuroImage, 91, pp. 169–176.

Pluta, A. et al. (2014). Reduced resting-state brain activity in the default mode network in children with (central) auditory processing disorders. Behavioral and Brain Functions, 10(1), 33. Doi:10.1186/1744-9081-10-33.

Saralegui, I., et al. (2014). Reading networks in children with dyslexia compared to children with ocular motility disturbances revealed by fMRI. Front. Hum. Neurosci., 8. Doi: 10.3389/fnhum.2014.00936.

Sims, T. B. et al. (2014). Autistic traits modulate frontostriatal connectivity during processing of rewarding faces. Soc Cogn Affect Neurosci, 9(12), pp. 2010–2016.

Gaebler, M. et al. (2013). Heart rate variability and its neural correlates during emotional face processing in social anxiety disorder. Biological Psychology, 94(2), pp. 319–330.

Kambeitz-Ilankovic, L. et al. (2013). Attentional Modulation of Source Attribution in First-Episode Psychosis: A Functional Magnetic Resonance Imaging Study, 39(5), pp. 1027–1036.

Kollndorfer, K. et al. (2013). A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning? Front. Hum. Neurosci., 7. Doi: 10.3389/fnhum.2013.00095.

Olsen, A. et al. (2013). The Functional Topography and Temporal Dynamics of Overlapping and Distinct Brain Activations for Adaptive Task Control and Stable Task-set Maintenance during Performance of an fMRI-adapted Clinical Continuous Performance Test. Journal of Cognitive Neuroscience, 25(6), pp. 903–919.

Ong, J. L. et al. (2013). Now You Hear Me, Now You Don’t: Eyelid Closures as an Indicator of Auditory Task Disengagement, 36(12), pp. 1867–1874.

Romeo, M. et al. (2013). A Functional Magnetic Resonance Imaging Paradigm to Identify Distinct Cortical Areas of Facial Function. Plastic and Reconstructive Surgery, 131(4), pp. 527e–533e.

Fliessbach, Klaus, Phillipps, C. B., Trautner, P., Schnabel, M., Elger, C. E., Falk, A., & Weber, B. (2012). Neural responses to advantageous and disadvantageous inequity. Front. Hum. Neurosci., doi:10.1016/j.neuroimage.2006.06.007.

Jansen, M. et al. (2012). Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data. NeuroImage, 59(1), pp. 261–270.

Malaia, E. et al. (2012). Event segmentation in a visual language: Neural bases of processing American Sign Language predicates. NeuroImage, 59(4), pp. 4094–4101.

Weissman, D. H. & Prado, J. (2012). Heightened activity in a key region of the ventral attention network is linked to reduced activity in a key region of the dorsal attention network during unexpected shifts of covert visual spatial attention. NeuroImage, 61(4), pp. 798–804.

Fuglo, D., Kallenbach, K., Tsakiri, A., Simonsen, H., Sander, B., Hansen, A. E., Rostrup, E., et al. (2011). Retinal atrophy correlates with fMRI response in patients with recovered optic neuritis. Neurology, 77(7), 645–651.

Prado, J. & Weissman, D. H. (2011). Spatial attention influences trial-by-trial relationships between response time and functional connectivity in the visual cortex. NeuroImage, 54(1), pp. 465–473.

Raab, G. et al. (2011). A Neurological Study of Compulsive Buying Behaviour. J Consum Policy, 34(4), pp. 401–413.

Yao, B. et al. (2011). Silent Reading of Direct versus Indirect Speech Activates Voice-selective Areas in the Auditory Cortex. Journal of Cognitive Neuroscience, 23(10), pp. 3146–3152

Brassen, S. et al. (2010). The influence of directed covert attention on emotional face processing. NeuroImage, 50(2), pp. 545–551.

Kalpouzos, G. et al. (2010). Neurocognitive Systems Related to Real-World Prospective Memory. PLoS ONE, 5(10), e13304.

Arzy, S. et al. (2009). Subjective mental time: the functional architecture of projecting the self to past and future. European Journal of Neuroscience, 30(10), pp. 2009–2017.

Ousdal, O. T. et al. (2008). The human amygdala is involved in general behavioral relevance detection: Evidence from an event-related functional magnetic resonance imaging Go-NoGo task. Neuroscience, 156(3), pp. 450–455.

Ranta-aho, P. O. et al. (2008). Estimation of single-trial fMRI BOLD responses using combined EEG and fMRI measurements.

Weber, B. et al. (2007). Neural evidence for Reference-dependence in real-market-transactions. NeuroImage, 35(1), pp. 441–447.

LCD Monitor


Hermann, A. et al. (2020). Multiple extinction contexts modulate the neural correlates of context-dependent extinction learning and retrieval. Neurobiology of Learning and Memory, 168, 107150.

Honbolygo, F. et al. (2020). Expectations about word stress modulate neural activity in speech-sensitive cortical areas. Neuropsychologia, 143, 107467.

Hu, K. et al. (2020). Differential color tuning of the mesolimbic reward system. Scientific Reports, 10, 10223.

Liuzzi, A. G. et al. (2020). General and feature-based semantic representations in the semantic network. Scientific Reports, 10, 8931.

Sasaoka, T. et al. (2020). The shape of a vehicle windshield affects reaction time and brain activity during a target detection task. Frontiers in Human Neuroscience, 14, 183.

Tzvi, E. et al. (2020). Cerebellar – Premotor cortex interactions underlying visuomotor adaptation. NeuroImage, 220, 117142.

Wu, E. X. W. et al. (2020). Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention? NeuroImage, 209, 11635.


Amlien, I. K. et al. (2019). Elaboration benefits source memory encoding through centrality change. Scientific Reports, 9, 3704.

Castelhano, J. et al. (2019). The role of the insula in intuitive expert bug detection in computer code: an fMRI study. Brain Imaging and Behavior, 13, pp. 623-637.

Darcy, N. et al. (2019). Category-selective processing in the two visual pathways as a function of stimulus degradation by noise. NeuroImage, 188, pp. 785-793.

Dorum, E. S. et al. (2019). Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking. BioRxiv, doi:

Lolli, V. et al. (2019). MRI-compatible pneumatic stimulator for sensorimotor mapping. Journal of Neuroscience Methods, 313, pp. 29-36.

Schmitt, A. et al. (2019). Effects of low- and high-intensity exercise on emotional face processing: an fMRI face-matching study. Social Cognitive and Affective Neuroscience, 14(6), pp. 657-667.

Varambally, S. et al. (2019). Yoga and schizophrenia – a comprehensive assessment of neuroplasticity. Medicine, 98(43), e17399.

Vidal-Pineiro, D. et al. (2019). Maintained frontal activity underlies high memory function over 8 years in aging. Cerebral Cortex, 29(7), pp. 3111-3123.


De Best, P. B. et al. (2018). How ocular dominance and binocularity are reflected by the population receptive field properties. Visual Neuroscience, 59, pp. 5301-5311.

Duarte, I. C. et al. (2018). The role of prefrontal cortex in a battle of the sexes dilemma involving a conflict between tribal and romantic love. Scientific Reports, 8, 12133.

Juvodden, H. T. et al. (2018). Narcolepsy type 1 patients have abnormal brain activation to neutral-rated movies in humor-paradigm. BioRxiv, doi:

Kiss, M. et al. (2018). Reducing task-based fMRI scanning time using simultaneous multislice echo planar imaging. Neuroradiology, 60, pp. 293-302.

Miranda, A. S. C. Optical properties influence visual cortical functional resolution after cataract surgery and both dissociate from subjectively perceived quality of vision. Optics, Population Receptive Fields and Subjective Vision, 59(0), pp. 986-994.

Mouthon, A. et al. (2018). Age-related differences in cortical and subcortical activities during observation and motor imagery of dynamic postural tasks: An fMRI study. Neural Plasticity, doi:

Orlov, T. & Zohary, E. et al. (2018). Object representations in human visual cortex formed through temporal integration of dynamic partial shape views. The Journal of Neuroscience, 38(3), pp. 659-678.

Schulz, L. et al. (2018). Action affordances and visuo-spatial complexity in motor imagery: An fMRI study. Brain and Cognition, 124, pp. 37-46.

Silva, G. et al. (2018). Oscillatory motor patterning is impaired in neurofibromatosis type 1: a behavioural, EEG and fMRI study. Journal of Neurodevelopmental Disorders, 10, 11.

Smith, R. et al. (2018). Structural and functional brain differences in key opinion journal leaders. Neurology and Neurological Sciences, 1, 1005.

Steffens, M. et al. (2018). Effects of ketamine on brain function during response inhibition. Psychopharmacology, 235, pp. 3559-3571.

Taya, F. et al. (2018). Fronto-parietal subnetworks flexibility compensates for cognitive decline due to mental fatigue. Human Brain Mapping, 39, pp. 3528-3545.


Antolak, A. & Jackson, E. (2019). Development and evaluation of an arterial spin-labeling digital reference object for quality control and comparison of data analysis applications. Physics in Medicine and Biology, 64(2), 02NT01

Bao, S. et al. (2019). Differentiating between glioblastoma and primary CNS lymphoma using combined whole-tumor histogram analysis of the normalized cerebral blood volume and apparent diffusion coefficient. Magn. Reson. Med. Sci., 18, pp. 53-61.

Campos, M. et al. (2019). Perfusion magnetic resonance as a biomarker for Sorafebin-treated advanced hepatocellular carcinoma: A polit study. Portuguese Journal of Gastroenterology, 26, pp. 260-267.

Choi, Y. et al. (2019). Analysis of heterogeneity of peritumoral T2 hyperintensity in patients with pretreatment glioblastoma: Prognostic value of MRI-based radiomics. European Journal of Radiology, 120, 108642.

Kang, Y. et al. (2019). Can we predict intraoperative blood loss in meningioma patients? Application of dynamic susceptibility contrast-enhanced magnetic resonance imaging. Journal of Neuroradiology, doi: 10.1016/j.neurad.2019.10.003. EPub ahead of print.

Kim, S. et al. (2019). Prognostic predictions for patients with glioblastoma after standard treatment: Application of contrast leakage information from DSC-MRI within nonenhancing FLAIR high-signal-intensity lesions. Am. J. Neuroradiol., 40(12), pp. 2052-2058.

Kim, Y. et al. (2019). Altered vascular permeability in migraine-associated brain regions: Evaluation with dynamic contrast enhanced MRI. Radiology, 292(3), pp. 713-720.

Lee, M. et al. (2019). Prediction of IDH1 mutation status in glioblastoma using machine learning technique based on quantitative radiomic data. World Neurosurgery, 125, e.688-696.

Li, C. et al. (2019). Multi-parametric and multi-regional histogram analysis of MRI: Revealing imaging phenotypes of glioblastoma correlated with patient survival. European Radiology, 29, pp. 4718-4729.

Lindgren, A. et al. (2019). Prognostic value of preoperative dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian cancer. European Journal of Radiology, 115, pp. 66-73.

Lindgren, A. et al. (2019). Dynamic contrast-enhanced perfusion parameters in ovarian cancer: Good accuracy in identifying high HIF-1α expression. PLOS One, 14(8), e0221340.

Sowa, P. et al. (2019). Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis. Multiple Sclerosis, 25(5), pp. 687-698.

Anzalone, N. et al. (2018). Brain gliomas: Multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images. Radiology, 287(3), pp. 933-943.

Han, M. et al. (2018). Correlation of human papilloma virus status with quantitative perfusion/diffusion/metabolic imaging parameters in the oral cavity and oropharyngeal squamous cell carcinoma: comparison of primary tumour sites and metastatic lymph nodes. Clinical Radiology, 73(8), pp. 757.e21-757.e27.

Kong, D. et al. (2018). Quantitative radiomic profiling of glioblastoma represents transcriptomic expression. Oncotarget, 9(5), pp. 6336-6345.

Lee, B. et al. (2018). Clinical value of vascular permeability estimates using dynamic susceptibility contrast MRI: Improved diagnostic performance in distinguishing hypervascular primary CNS lymphoma from glioblastoma. American Journal of Neuroradiology, 39(8), pp. 1415-1422.

Lee, S. et al. (2018). Skeletal muscle phosphatidylcholine and phosphatidylethanolamine respond to exercise and influence insulin sensitivity in men. Nature Scientific Reports, 8, 6531.

Taib, N. et al. (2018). The impact of maximum turning angle on reconstructed fibre tractography and diffusion tensor imaging indices. IEEE-EMBS Conference on Biomedical Engineering and Sciences, pp. 54-58.

Yang, Y. et al. (2018). Optimizing texture retrieving model for multimodal MR image-based support vector machine for classifying glioma. Journal of Magnetic Resonance Imaging, 49(5), pp. 1263-1274.

Boonzaier, N. et al. (2017). Multiparametric MR imaging of diffusion and perfusion in contrast-enhancing and nonenhancing components in patients with glioblastoma. Radiology, 284(1), pp. 180-190.

Cho, H. et al. (2017). Is BCAT1 a new MR imaging-related biomarker for prognosis prediction in IDH1-wildtype glioblastoma patients: Radiogenomics study. Sci. Rep., 7, 17740.

Choi, J. et al. (2017). Intratumoural heterogeneity measured using FDG-PET and MRI is associated with tumour-stroma ratio and clinical outcome in head and neck squamous cell carcinoma. Clinical Radiology, 72, pp. 482-489.

De Lange, C. et al. (2017). Assessment of cerebral perfusion with contrast-enhanced ultrasound during constriction of the neck mimicking malposition of the BD Odon Device: A study in newborn piglets. An International Journal of Obstetrics and Gynaecology, 124(S4), pp. 26-34.

Hussain, N. et al. (2017). Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas. J. Neurosurg., 126, pp. 1220-1226.

Ioannidi, L. et al. (2017). Surface-coil MRI for small peripheral choroidal melanoma: Imaging in a rabbit eye model. Ocul. Oncol. Pathol., 4, pp. 364-369.

Li, C. et al. (2017). Flow versus permeability weighting in estimating the forward volumetric transfer constant (Ktrans) obtained by DCE-MRI with contrast agents of differing molecular sizes. Magnetic Resonance Imaging, 36, pp. 105-111.

Nam, J. et al. (2017). Comparison between the prebolus T1 measurements and the fixed T1 value in dynamic contrast-enhanced MR imaging for the differentiation of true progression from pseudoprogression in glioblastoma treated with concurrent radiation therapy and Temozolomide chemotherapy. American Journal of Neuroradiology, 38, pp. 2243-50.

Nordhoy, W. et al. (2017). Assessing B1+ inhomogeneity with the variable-flip-angle method. ESMRMB, DOI:  10.13140/RG.2.2.30837.68326.

Sowa, P. et al. (2017). Magnetic resonance imaging perfusion is associated with disease severity and activity in multiple sclerosis. Neuroradiology, 59(7), pp. 655-664.

Sowa, P. et al. (2017). Magnetic responance perfusion predicts disease severity in early multiple sclerosis. ECR, DOI: 10.1594/ecr2017/C-2400.

Takamura, T. et al. (2017). Slice-accelerated gradient-echo echo planar imaging dynamic susceptibility contrast-enhanced MRI with blipped CAIPI: effect of increasing temporal resolution. Japanese Journal of Radiology, 36(1), pp. 40-50.

Wu, R. et al. (2017). Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine position emission tomography image improves the diagnostic accuracy of cerebral glioma grading. Japanese Journal of Radiology, 35(10), pp. 613-621.

Yen, Y.-F. et al. (2017). Multimodal imaging of vascularity and drug delivery in GBM patients treated with anti-angiogenesis inhibitor. Proc. Intl. Soc. Mag. Reson. Med., 25, p. 0501.

Zhang, X. et al. (2017). Investigation of machine learning techniques in preoperative glioma grading based on multi-parametric MRI data. Proc. Intl. Soc. Mag. Reson. Med., 25, p. 4556.

Zhang, X. et al. (2017). Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget, 8(29), pp. 47816-47830.

Anwander, H. et al. (2016). Perfusion MRI in hips with metal-on-metal and metal-on-polyethylene total hip arthroplasty. Bone Joint Res, 5, pp. 73-79.

Crisi, G. et al. (2016). Effectiveness of a high relaxivity contrast agent administered at half dose in dynamic susceptibility contrast MRI of brain gliomas. J. Magn. Reson. Imaging, 45(2), pp. 500-506.

Filice, S. & Crisi, G. (2016). Dynamic contrast-enhanced perfusion MRI of high grade brain gliomas obtained with arterial or venous waveform input function. J Neuroimaging, 26, pp. 124-129.

Jung, B. et al. (2016). Comparison of glioblastomas and brain metastases using dynamic contrast-enhanced perfusion MRI. J Neuroimaging, 26(2), pp. 240-246.

Lee, S. et al. (2016). Application of diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging for ganglioglioma in adults: Comparison study with oligodendroglioma. Journal of Neuroradiology, 43(5), pp. 331-8.

Nguyen, T. et al. (2016). Correlation of tumor immunohistochemistry with dynamic contrast-enhanced and DSC-MRI parameters in patients with gliomas. American Journal of Neuroradiology, 37:2217-23.

Ryu, J. et al. (2016). Characteristics of quantitative perfusion parameters on dynamic contrast-enhanced MRI in mammographically occult breast cancer. Journal of Applied Clinical Medical Physics, 17(5), pp. 377-390.

Santarosa, C. et al. (2016). Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: Preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis. European Journal of Radiology, 85, pp. 1147-1156.

Suh, H. et al. (2016). Imaging predictors for atherosclerosis-related intracranial large artery occlusions in acute anterior circulation stroke. Journal of Stroke, 18(3), pp. 352-354.

Verma, R. et al. (2016). Characterization of enhancing MS lesions by dynamic texture parameter analysis of dynamic susceptibility perfusion imaging. BioMed Research International, Article ID 9578139.

Wang, S. et al. (2016). Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI. American Journal of Neuroradiology, 37, pp. 28-36.

Zhang, L. et al. (2016). VEGFR-1 targeted DNAzyme via transcatheter arterial delivery influences tumor vasculature assessed through dynamic contrast-enhanced magnetic resonance imaging. Oncology Reports, 36, pp. 1339-1344.

Alcaide-Leon, P. et al. (2015). Pixel-by-pixel comparison of volume transfer constant and estimates of cerebral blood volume from dynamic contrast-enhanced and dynamic susceptibility contrast-enhanced MR imaging in high-grade gliomas. AJNR Am J Neuroradiol., 36, pp. 871-76.

Arevalo Perez, J. et al. (2015). Dynamic contrast-enhanced perfusion MRI and diffusion-weighted imaging in grading of gliomas. J Neuroimaging, 25(5), pp. 792-798.

Baco, E. et al. (2015). Magnetic resonance imaging-transrectal ultrasound image-fusion biopsies accurately characterize the index tumor: Correlation with step-sectioned radical prostatectomy specimens in 135 patients. European Urology, 67(4), pp. 787-794.

Baco, E. et al. (2015). Predictive value of magnetic resonance imaging determined tumor contact length for extracapsular extension of prostate cancer. Journal of Urology, 193, pp. 466-472.

De Cobelli, F. et al. (2015). New insights in abdominal pain in paroxysmal nocturnal hemoglobinuria (PNH): A MRI study. PLOS One, 10(4), e0122832.

Flach, P. et al. (2015). Deep into the fibers! Postmortem diffusion tensor imaging in forensic radiology. Am. J. Forensic Med. Pathol. 36(3), pp. 153-161.

Gupta, S. et al. (2015). BOLD fMRI and DTI in strabismic amblyopes following occlusion therapy. Int. Opthalmol., 36(4), pp. 557-568.

Han, M. et al. (2015). The correlations between MRI perfusion, diffusion parameters, and 18F-FDG PET metabolic parameters in primary head and neck cancer. Medicine, 94(47), e2141.

Heck, A. et al. (2015). Quantitative analyses of T2-weighted MRI as a potential marker for response to somatostatin analogs in newly diagnosed acromegaly. Endocrine, 52(2), pp. 333-43.

Kim, S. et al. (2015). Measurement of apparent diffusion coefficient values from diffusion-weighted MRI: A comparison of manual and semiautomatic segmentation methods. Investigative Magnetic Resonance Imaging, 19, pp. 88-98.

Lee, S. et al. (2015). Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. Journal of Neurooncology, 121, pp. 141-150.

Lu, S. et al. (2015). Histogram analysis of apparent diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions. American Journal of Neuroradiology, 204, pp. 827-834.

Nguyen, T. et al. (2015). Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas. American Journal of Neuroradiology, 36, pp. 63-69.

Nguyen, T. et al. (2015). Comparison of the diagnostic accuracy of DSC- and dynamic contrast-enhanced MRI in the preoperative grading of astrocytomas. American Journal of Neuroradiology, 36, pp. 2017-22.

Shim, W. et al. (2015). Comparison of apparent diffusion coefficient and intravoxel incoherent motion for differentiating among glioblastoma, metastasis, and lymphoma focusing on diffusion-related parameter. PLOS One, 10(7), e0134761.

Sowa, P. et al. (2015). Reduced perfusion in white matter lesions in multiple sclerosis. European Journal of Radiology, 84, pp. 2605-2612.

Yang, L. et al. (2015). EBV-LMP1 targeted DNAzyme enhances radiosensitivity by inhibiting tumor angiogenesis via the JNKs/HIF-1 pathway in masopharyngeal carcinoma. Oncotarget, 6(8), pp. 5804-5817.

Adler, J. et al. (2014). Inter-scanner reliability of normative values for dynamic susceptibility brain perfusion MRI. Proc. ECR 2014, DOI: 10.1594/ecr2014/C-0823.

Almeida-Freitas, D. et al. (2014). Assessment of irradiated brain metastases using dynamic contrast-enhanced magnetic resonance imaging. Neuroradiology, 56, pp. 437-443.

Aukland, S. et al. (2014). Ventricular dilatation in ex-prematures: only confined to the occipital region? MRI-based normative standards for 19-year-ols ex-prematures without major handicaps. Acta Radiologica, 55(4), pp. 470-477.

Cha, J. et al. (2014). Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. Am. J. Neuroradiol., 35, pp. 1309-17.

Eckerbom, P. et al. (2014). Evaluation of regional renal blood flow with non-invasive ASL measurements compared with Gadolinium in patients before renal nephrectomy. Proc. ECR 2014, DOI: 10.1594/ecr2014/C-2264.

Esposito, A. et al. (2014). Liver perfusion changes occurring during pancreatic islet engraftment: A dynamic contrast-enhanced magnetic resonance study. American Journal of Transplantation, 14, pp. 202-209.

Jain, R. et al. (2014). Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: Focus on the nonenhancing component of the tumor. Radiology, 272(2), pp. 484-493.

Kim, D. et al. (2014). Differentiation of true recurrence from delayed radiation therapy-related changes in primary brain tumors using diffusion-weighted imaging, dynamic susceptibility contrast perfusion imaging, and susceptibility weighted imaging. Journal of the Korean Society of Magnetic Resonance in Medicine, 18(2), pp. 120-132.

Kumar, A. et al. (2014). The role of neorunavigation-guided functional MRI and diffusion tensor tractography along with cortical stimulation in patients with eloquent cortex lesions. British Journal of Neurosurgery,  28(2), pp. 226-33.

Liao, W-H. et al. (2014). DCE-MRI assessment of the effect of Epstein-Barr virus-encoded latent membrane protein-1 targeted DNAzyme on tumor vasculature in patients with nasopharyngeal carcinomas. BMC Cancer, 14, pp. 835-843.

Orsingher, L. et al. (2014). Differences in dynamic susceptibility contrast MR perfusion maps generated by different methods implemented in commercial software. J. Comput. Assist. Tomogr., 38, pp. 647-654.

Pezzetti, G. et al. (2014). Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) of small bowel and MR flow analysis of mesenteric vessels in patients with Paroxistic Nocturnal Haemoglobinuria (PNH) with and without abdominal pain. European Society of Radiology, DOI: 10.1594/ecr2014/B-0191.

Rud, E. et al. (2014). Detection of the index tumour and tumour volume in prostate cancer using T2-weighted and diffusion-weighted magnetic resonance imaging (MRI) alone. BJU International, 114, pp. E32-E42.

Saha, A. et al. (2014). Magnetic resonance perfusion characteristics of hypervascular renal and hypovascular prostate spinal metastases: Clinical utilities and implications. Spine, 39(24), pp. E1433-E1440.

Vardal, J. et al. (2014). Correction of B0-distortions in echo-planar-imaging-based perfusion-weighted MRI. Journal of Magnetic Resonance Imaging, 39, pp. 722-728.

Burge, M. et al. (2013). Phase I, pharmacokinetic and pharmacodynamic evaluation of CYT997, an orally-bioavailable cytotoxic and vascular-disrupting agent. Invest. New Drugs, 31, pp.126-135.

Choi, Y. et al. (2013). Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging. Acta Radiologica, 54, pp. 448-454.

Chu, H. et al. (2013). Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant Temozolomide: Comparison study of standard and high b-value diffusion-weighted imaging. Radiology, 269(3), 831-840.

Haldorsen, I. et al. (2013). Dynamic contrast-enhanced MRI in endometrial carcinoma identifies patients at increased risk of recurrence. European Radiology, 23(10), pp. 2916-25.

Ravn, S. et al. (2013). Differences n supratentorial white matter diffusion after radiotherapy – new biomarker of normal brain tissue damage. Acta Oncologica, 52, pp. 1314-1319.

Shen, L. et al. (2013). Antoangiogenic and antitumoral effects mediated by a Vascular Endothelial Growth Factor Feceptor 1 (VEGFR-1)-targeted DNAzyme. Molecular Medicine, 19, pp. 377-386.

Suh, C. et al. (2013). Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging. American Journal of Neuroradiology, 34, pp. 2278-2286.

Welch, W. et al. (eds.) (2013). Oxygen Transport to Tissue XXXIV, Advances in Experimental Medicine and Biology, 765, pp. 55-58.

Gundersen, H. et al. (2012). Alcohol-induced changes in cerebral blood flow and cerebral blood volume in social drinkers. Alcohol and alcoholism, 48(2), pp. 160-165.

Jain, R. et al. (2012). Genomic mapping and survival prediction in glioblastoma: Role of tumor blood volume versus molecular sub-classification – A TCGA glioma phenotype research group project. Proc. Intl. Soc. Mag. Reson. Med., 20, p. 3199.

Johansson, L. et al. (2012). Gadobenate dimeglumine (MultiHance) in MR angiography: an in-vitro phantom comparison with gadopentetate dimeglumine (Magnevist) at different concentrations. Acta Radiologica, 53, pp. 1112-17.

Musolino, P. et al. (2012). Hypoperfusion predicts lesion progression in cerebral X-linked adrenoleukodystrophy. Brain, 135, pp. 2676-2683.

Nguyen, T. et al. (2012). Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas. American Journal of Neuroradiology, 33, pp. 1539-45.

Frokjaer, J. et al. (2011). Altered brain microstructure assessed by diffusion tensor imaging in patients with chronic pancreatitis. Gut, 60, pp. 1554-1562.

Garzon, B. et al. (2011). Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction. Acta Radiologica, 52, pp. 1052-1060.

Server, A. et al. (2011). Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis. Neuroradiology, 53, pp. 319-330.

Server, A. et al. (2011). Measurements of diagnostic examination performance and correlation analysis using microvascular leakage, cerebral blood volume, and blood flow derived from 3T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in glial tumor grading. Neuroradiology, 53, pp. 435-447.

Shen, Q. et al. (2011). Spatiotemporal characteristics of postischemic hyperperfusion with respect to changes in T1, T2, diffusion, angiography, and blood-brain barrier permeability. Journal of Cerebral Blood Flow & Metabolism, 31, pp. 2076-2085.

Toh, C.-H. et al. (2011). Differentiation of brain abscesses from necrotic glioblastomas and cystic metastatic brain tumors with diffusion tensor imaging. American Journal of Neuroradiology, 32, pp 1646-51.

Elsinger, C. et al. (2010). nordic fMRI Solution – Products for enhancing the development of a functional imaging clinical practice. European Neurological Review, 5(1), pp. 100-102.

Moen, G. et al. (2010). Cerebral diffusion an perfusion deficits in North Sea divers. Acta Radiologica, 51(9), pp. 1050-1058.

Ulmer, S. et al. (2010). Intraoperative DSC-MRI (iDSC-MRI): Feasibility and clinical application. Proc. Intl. Soc. Mag. Reson. Med., 18, p.4111.

Brix, O. et al. (2009). Whether depositing fat or losing weight, fish maintain a balance. Proc. R. Soc. B, 276, pp. 3777-3782.

Brix, O. et al. (2009). Chemical shift based MR imaging and gas chromatography for quantification and localization of fat in Atlantic mackerel. Journal of Experimental Marine Biology and Ecology, 376, pp. 68-75.

Chang, T. et al. (2009). Change in cerebral perfusion after carotid angioplasty with stenting is related to cerebral vasoreactivity: A study using dynamic susceptibility-weighted contrast-enhanced MR imaging and functional MR imaging with a breath-holding paradigm. Am. J. Neuroradiol., 30, pp. 1330-36.

Ulmer, S. et al. (2009). Intraoperative dynamic susceptibility contrast weighted magnetic resonance imaging (iDSC-MRI) – Technical considerations and feasibility. NeuroImage, 45, pp. 38-43.

Koral, K. et al. (2008). Imaging characteristics of atypical Teratoid-Rhaboid tumor in children compared with medulloblastoma. American Journal of Radiology, 199, pp. 809-814.

Toh, C.-H. et al. (2008). Primary cerebral lymphoma and glioblastoma multiforme: Differences in diffusion characteristics evaluated with diffusion tensor imaging. American Journal of Neuroradiology, 29, pp. 471-475


Ermolaev, A. et al. (2020). Cytologic control of the resection margins of hemispheric gliomas and metastases. Voprosy Neirokhirurgii Imeni N.N. Burdenko, 84(1), 33. doi:10.17116/neiro20208401133

Louis, R. et al. (2020). Impact of Neurosurgical Consultation With 360-Degree Virtual Reality Technology on Patient Engagement and Satisfaction. Neurosurgery Open,1(3). doi:10.1093/neuopn/okaa004

Qiu, X. et al. (2020). Perfusion MR prior to radiotherapy is a strong predictor of survival in high-grade gliomas after proton and carbon ion radiotherapy. Annals of Translational Medicine.

Rani, N. et al. (2020). [99mTc]-Bis-Methionine-DTPA Single-Photon Emission Computed Tomography Impacting Glioma Management: A Sensitive Indicator for Postsurgical/Chemoradiotherapy Response Assessment. Cancer Biotherapy and Radiopharmaceuticals. doi:10.1089/cbr.2020.3696

Rigolo, L. et al. (2020). Intraoperative Use of Functional MRI for Surgical Decision Making after Limited or Infeasible Electrocortical Stimulation Mapping. Journal of Neuroimaging, 30(2), 184-191. doi:10.1111/jon.12683

Unadkat, P. et al. (2019). Functional MRI Task Comparison for Language Mapping in Neurosurgical Patients. Journal of Neuroimaging, 29(3), 348-356. doi:10.1111/jon.12597

Benjamin, C. F. et al. (2018). Presurgical language fMRI: Technical practices in epilepsy surgical planning. Human Brain Mapping, 39(10), 4032-4042. doi:10.1002/hbm.24229

Rodrigues, N. et al. (2018). The Emerging Role of Tractography in Deep Brain Stimulation: Basic Principles and Current Applications. Brain Sciences, 8(2), 23. doi:10.3390/brainsci8020023

Gargiulo, P. et al. (2017). New Directions in 3D Medical Modeling: 3D-Printing Anatomy and Functions in Neurosurgical Planning. Journal of Healthcare Engineering,2017, 1-8. doi:10.1155/2017/1439643

Vogt, V. L. et al. (2017). Current standards of neuropsychological assessment in epilepsy surgery centers across Europe. Epilepsia, 58(3), 343-355. doi:10.1111/epi.13646

Larvie, M., & Fischl, B. (2016). Volumetric and fiber-tracing MRI methods for gray and white matter. Handbook of Clinical Neurology Neuroimaging Part I, 39-60. doi:10.1016/b978-0-444-53485-9.00003-9

Hirano, M. et al. (2015). Hypertrophic olivary degeneration resulting from posterior fossa masses and their treatments. Clinical Imaging, 39(5), 787-790. doi:10.1016/j.clinimag.2015.05.015

Hulou, M. M. et al. (2015). Awake right hemisphere brain surgery. Journal of Clinical Neuroscience, 22(12), 1921-1927. doi:10.1016/j.jocn.2015.06.009

Leśniak, M. et al. (2014). Pure alexia after damage to the right fusiform gyrus in a right-handed male. Neurologia I Neurochirurgia Polska, 48(5), 373-377. doi:10.1016/j.pjnns.2014.09.003

Berntsen, E. M., & Håberg, A. K. (2011). Resection of Brain Lesions: Use of Preoperative Functional Magnetic Resonance Imaging and Diffusion Tensor Tractography. Tumors of the Central Nervous System, Volume 4, 227-240. doi:10.1007/978-94-007-1706-0_23


References for our software algorithms​


Skurdal, M. et al. (2015). Voxel-wise perfusion assessment in cerebral white matter with PCASL at 3T; Is it possible and how long does it take? PLOS One, 10(8), e0135596.

Grovik, E. et al. (2014). Split dynamic MRI: Aingle bolus high spatial-temporal resolution and multi contrast evaluation of breast lesions. Journal of Magnetic Resonance Imaging, 39, pp. 673-682.

Bjornerud, A. & Emblem, K. (2010). A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. Journal of Cerebral Blood Flow & Metabolism, 20, pp. 1066-1078.

Kvinnsland, Y. et al. (2009). Multispectral analysis of multimodal images. Acta Oncologica, 48(2), pp. 277-284.