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Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT ), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP ), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2 (3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2 (3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2 (3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects., Competing Interests: Conflicts of Interest Dr. Abdallah has served as a consultant, speaker and/or on advisory boards for FSV7, Lundbeck, Psilocybin Labs, Genentech and Janssen, and editor of Chronic Stress for Sage Publications, Inc.; he has filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Davidson is the founder and president of, and serves on the board of directors for, the non-profit organization Healthy Minds Innovations, Inc. Dr. Jahanshad, Dr. Thompson and Dr. Ching received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the content of this manuscript. Dr. Krystal is a consultant for AbbVie, Inc., Amgen, Astellas Pharma Global Development, Inc., AstraZeneca Pharmaceuticals, Biomedisyn Corporation, Bristol-Myers Squibb, Eli Lilly and Company, Euthymics Bioscience, Inc., Neurovance, Inc., FORUM Pharmaceuticals, Janssen Research & Development, Lundbeck Research USA, Novartis Pharma AG, Otsuka America Pharmaceutical, Inc., Sage Therapeutics, Inc., Sunovion Pharmaceuticals, Inc., and Takeda Industries; is on the Scientific Advisory Board for Lohocla Research Corporation, Mnemosyne Pharmaceuticals, Inc., Naurex, Inc., and Pfizer; is a stockholder in Biohaven Pharmaceuticals; holds stock options in Mnemosyne Pharmaceuticals, Inc.; holds patents for Dopamine and Noradrenergic Reuptake Inhibitors in Treatment of Schizophrenia, US Patent No. 5,447,948 (issued September 5, 1995), and Glutamate Modulating Agents in the Treatment of Mental Disorders, U.S. Patent No. 8,778,979 (issued July 15, 2014); and filed a patent for Intranasal Administration of Ketamine to Treat Depression. U.S. Application No. 14/197,767 (filed on March 5, 2014); US application or Patent Cooperation Treaty international application No. 14/306,382 (filed on June 17, 2014); Filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Schmahl is a consultant for Boehringer Ingelheim International GmbH. Dr. Stein has received research grants and/or consultancy honoraria from Lundbeck and Sun. Dr. Lebois reports unpaid membership on the Scientific Committee for the International Society for the Study of Trauma and Dissociation (ISSTD) and spousal license payment for Vanderbilt IP from Acadia Pharmaceuticals unrelated to the topic of this manuscript. All other authors have no conflicts of interest to declare., (Copyright © 2022. Published by Elsevier Inc.)

After realizing the worlds' first preclinical magnetic particle imaging (MPI) demonstrator, Philips is now realizing the worlds' first whole-body clinical prototype to prove the feasibility of MPI for clinical imaging. After a brief introduction of the basic MPI imaging process, this contribution presents an overview on the determining factors for key properties, i.e., spatial resolution, acquisition speed, sensitivity, and quantitativeness, and how these properties are influenced by scaling up from preclinical to clinical instrumentation. Furthermore, it is discussed how this scale up affects the physiological compatibility of the method as well as hardware parameters such as power requirements for drive field generation, selection and focus field generation, and the design of the receive chain of the MPI device.

Magnetic particle imaging (MPI) is a new medical imaging technique which performs a direct measurement of magnetic nanoparticles, also known as superparamagnetic iron oxide. MPI can acquire quantitative images of the local distribution of the magnetic material with high spatial and temporal resolution. Its sensitivity is well above that of other methods used for the detection and quantification of magnetic materials, for example, magnetic resonance imaging. On the basis of an intravenous injection of magnetic particles, MPI has the potential to play an important role in medical application areas such as cardiovascular, oncology, and also in exploratory fields such as cell labeling and tracking. Here, we present an introduction to the basic function principle of MPI, together with an estimation of the spatial resolution and the detection limit. Furthermore, the above-mentioned medical applications are discussed with respect to an applicability of MPI., (Copyright © 2012 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

An MR-electrophysiology (EP) catheter is presented that provides full diagnostic EP functionality and a high level of radiofrequency safety achieved by custom-designed transmission lines. Highly resistive wires transmit intracardiac electrograms and currents for intracardiac pacing. A transformer cable transmits the localization signal of a tip coil. Specific absorption rate simulations and temperature measurements at 1.5 T demonstrate that a wire resistance > 3 kΩ/m limits dielectric heating to a physiologically irrelevant level. Additional wires do not increase tip specific absorption rate significantly, which is important because some clinical catheters require up to 20 electrodes. It is further demonstrated that radiofrequency-induced and pacing-induced resistive heating of the wires is negligible under clinical conditions. The MR-EP catheters provided uncompromised recording of electrograms and cardiac pacing in combination with a standard EP recorder in MR-guided in vivo EP studies, and the tip coil enabled fast and robust catheter localization. In vivo temperature measurements during such a study did not detect any device-related heating, which confirms the high level of safety of the catheter, whereas unacceptable heating was found with a standard EP catheter. The presented concept for the first time enables catheters with full diagnostic EP functionality and active tracking and at the same time a sufficient level of radiofrequency safety for MRI without specific absorption rate-related limitations., (Copyright © 2010 Wiley-Liss, Inc.)

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.