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Congenital heart disease (CHD) is the most common congenital malformation and is associated with adverse neurodevelopmental outcomes. The placenta is crucial for healthy fetal development and placental development is altered in pregnancy when the fetus has CHD. This study utilized advanced combined diffusion-relaxation MRI and a data-driven analysis technique to test the hypothesis that placental microstructure and perfusion are altered in CHD-affected pregnancies. 48 participants (36 controls, 12 CHD) underwent 67 MRI scans (50 control, 17 CHD). Significant differences in the weighting of two independent placental and uterine-wall tissue components were identified between the CHD and control groups (both p FDR  < 0.001), with changes most evident after 30 weeks gestation. A significant trend over gestation in weighting for a third independent tissue component was also observed in the CHD cohort (R = 0.50, p FDR  = 0.04), but not in controls. These findings add to existing evidence that placental development is altered in CHD. The results may reflect alterations in placental perfusion or the changes in fetal-placental flow, villous structure and maturation that occur in CHD. Further research is needed to validate and better understand these findings and to understand the relationship between placental development, CHD, and its neurodevelopmental implications., (© 2024. The Author(s).)

Purpose: To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating., Methods: The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments., Results: The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved., Conclusion: The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency., (© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T 1 -T 2 -weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.∗ -weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

BOLD fMRI signal has been used in conjunction with vasodilatory stimulation as a marker of cerebrovascular reactivity (CVR): the relative change in cerebral blood flow (CBF) arising from a unit change in the vasodilatory stimulus. Using numerical simulations, we demonstrate that the variability in the relative BOLD signal change induced by vasodilation is strongly influenced by the variability in deoxyhemoglobin-containing cerebral blood volume (CBV), as this source of variability is likely to be more prominent than that of CVR. It may, therefore, be more appropriate to describe the relative BOLD signal change induced by an isometabolic vasodilation as a proxy of deoxygenated CBV (CBV dHb ) rather than CVR. With this in mind, a new method was implemented to map a marker of CBV dHb , termed BOLD-CBV, based on the normalization of voxel-wise BOLD signal variation by an estimate of the intravascular venous BOLD signal from voxels filled with venous blood. The intravascular venous BOLD signal variation, recorded during repeated breath-holding, was extracted from the superior sagittal sinus in a cohort of 27 healthy volunteers and used as a regressor across the whole brain, yielding maps of BOLD-CBV. In the same cohort, we demonstrated the potential use of BOLD-CBV for the normalization of stimulus-evoked BOLD fMRI by comparing group-level BOLD fMRI responses to a visuomotor learning task with and without the inclusion of voxel-wise vascular covariates of BOLD-CBV and the BOLD signal change per mmHg variation in end-tidal carbon dioxide (BOLD-CVR). The empirical measure of BOLD-CBV accounted for more between-subject variability in the motor task-induced BOLD responses than BOLD-CVR estimated from end-tidal carbon dioxide recordings. The new method can potentially increase the power of group fMRI studies by including a measure of vascular characteristics and has the strong practical advantage of not requiring experimental measurement of end-tidal carbon dioxide, unlike traditional methods to estimate BOLD-CVR. It also more closely represents a specific physiological characteristic of brain vasculature than BOLD-CVR, namely blood volume., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Emma Biondetti reports financial support was provided by European Union, Italian Ministry of Education, University and Research. Richard G. Wise reports financial support was provided by European Union, Wellcome Trust. Michael Germuska reports financial support was provided by Wellcome Trust, UK Engineering and Physical Sciences Research Council. Kevin Murphy reports financial support was provided by Wellcome Trust. Valentina Tomassini reports financial support was provided by Multiple Sclerosis Society UK. Valentina Tomassini reports a relationship with Biogen, Almirall, Lundbeck, Roche, Novartis, Viatris, Alexion that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Specific absorption rate (SAR) relates power absorption to tissue heating, and therefore is used as a safety constraint in magnetic resonance imaging (MRI). This study investigates the implications of initial head positioning on local and whole-head SAR. A virtual body model was simulated at 161 positions inside an eight-channel parallel-transmit (pTx) array. On-axis displacements and rotations of up to 20 mm/degrees and off-axis axial/coronal translations were investigated. Single-channel, radiofrequency (RF) shimming (i.e., single-spoke pTx) and multispoke pTx pulses were designed for seven axial, five coronal and five sagittal slices at each position (the slices were consistent across all positions). Whole-head and local SAR were calculated using safety models consisting of a single (centred) body position, multiple representative positions and all simulated body positions. Positional mismatches between safety models and actual positions cause SAR underestimation. For axial imaging, the actual peak local SAR was up to 4.2-fold higher for both single-channel and 5-spoke pTx, 3.5-fold higher for 3-/4-spoke pTx, and 2-fold higher for RF shimming and 2-spoke pTx, compared with that calculated using the centred body position. For sagittal and coronal imaging, the underestimation of peak local SAR was up to 5.2-fold and 3.8-fold, respectively. Using all body positions to estimate SAR prevented SAR underestimation but yielded up to 11-fold SAR overestimation for RF shimming. Local SAR of single-channel and pTx multispoke pulses showed considerable dependence on the initial patient position. RF shimming yielded much lower sensitivity to positional mismatches for axial imaging but not for sagittal and coronal imaging. This was deemed attributable to the higher degrees-of-freedom of control offered by the investigated coil array for axial imaging. Whole-head SAR is less sensitive to positional mismatches compared with local SAR. Nevertheless, whole-head SAR increased by up to 80% for sagittal imaging. Local and whole-head SAR were observed to be more sensitive to positional mismatches in the axial plane, because of larger variations in coil-tissue proximity. Using all possible body positions in the safety model may become substantially over-conservative and limit imaging performance, especially for the RF shimming mode for axial imaging., (© 2022 The Author. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Purpose: Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time., Methods: We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps., Results: T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts., Conclusion: We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Schemata enhance memory formation for related novel information. This is true even when this information is neutral with respect to schema-driven expectations. This assimilation of novel information into schemata has been attributed to more effective organizational processing that leads to more referential connections with the activated associative schema network. Animal data suggest that systems consolidation of novel assimilated information is also accelerated. In the current study, we used both multivariate and univariate fMRI analyses to provide further support for these proposals and to elucidate the neural underpinning of these processes. Twenty-eight participants (5 male) overlearned fictitious schemata for 7 weeks and then encoded novel related and control facts in the scanner. These facts were retrieved both immediately and 2 weeks later, also in the scanner. Our results conceptually replicate previous findings with respect to enhanced vmPFC-hippocampus coupling during encoding of novel related information and point to a prior knowledge effect that is distinct from situations where novel information is experienced as congruent or incongruent with a schema. Moreover, the combination of both multivariate and univariate results further specified the proposed contributions of the vmPFC, precuneus and angular gyrus network to the more efficient encoding of schema-related information. In addition, our data provide further evidence for more efficient systems consolidation of such novel schema-related and potentially assimilated information. SIGNIFICANCE STATEMENT Our prior knowledge in a certain domain, often termed schema, heavily influences whether and how we form memories for novel information that can be related to them. The results of the current study show how a ventromedial prefrontal-precuneal-angular network contributes to the more efficient encoding of novel related information. Furthermore, the observed increase in prefrontal-hippocampal coupling during this process points to a critical distinction from the previously described mechanisms supporting the encoding of information that is experienced as congruent with schema-driven expectations. In addition, we find further support for the proposal based on animal data that prior knowledge enhances also the consolidation of schema-related information., (Copyright © 2022 the authors.)

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level-dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake-sleep transitions, and investigate local homeostatic sleep processes.

Purpose: Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B 1 + distributions following within-slice motion, which can then be used for tailored pTx pulse redesign., Methods: Using simulated data, conditional generative adversarial networks were trained to predict B 1 + distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B 1 maps. Tailored pTx pulses were designed using B 1 maps at the original position (simulated, no motion) and evaluated using simulated B 1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method., Results: Predicted B 1 + maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps., Conclusion: We propose a framework for predicting B 1 + maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx., (© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: The use of high-performance gradient systems (i.e., high gradient strength and/or high slew rate) for human MRI is limited by physiological effects (including the elicitation of magnetophosphenes and peripheral nerve stimulation (PNS)). These effects, in turn, depend on the interaction between time-varying magnetic fields and the body, and thus on the participant's position with respect to the scanner's isocenter. This study investigated the occurrence of magnetophosphenes and PNS when scanning participants on a high-gradient (300 mT/m) system, for different gradient amplitudes, ramp times, and participant positions., Methods: Using a whole-body 300 mT/m gradient MRI system, a cohort of participants was scanned with the head, heart, and prostate at magnet isocenter and a train of trapezoidal bipolar gradient pulses, with ramp times from 0.88 to 4.20 ms and gradient amplitudes from 60 to 300 mT/m. Reports of magnetophosphenes and incidental reports of PNS were obtained. A questionnaire was used to record any additional subjective effects., Results: Magnetophosphenes were strongly dependent on participant position in the scanner. 87% of participants reported the effect with the heart at isocenter, 33% with the head at isocenter, and only 7% with the prostate at isocenter. PNS was most widely reported by participants for the vertical gradient axis (67% of participants), and was the dominant physiological effect for ramp times below 2 ms., Conclusion: This study evaluates the probability of eliciting magnetophosphenes during whole-body imaging using an ultra-strong gradient MRI system. It provides empirical guidance on the use of high-performance gradient systems for whole-body human MRI., (© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Schizophrenia (SCZ) is associated with structural brain changes, with considerable variation in the extent to which these cortical regions are influenced. We present a novel metric that summarises individual structural variation across the brain, while considering prior effect sizes, established via meta-analysis. We determine individual participant deviation from a within-sample-norm across structural MRI regions of interest (ROIs). For each participant, we weight the normalised deviation of each ROI by the effect size (Cohen's d) of the difference between SCZ/control for the corresponding ROI from the SCZ Enhancing Neuroimaging Genomics through Meta-Analysis working group. We generate a morphometric risk score (MRS) representing the average of these weighted deviations. We investigate if SCZ-MRS is elevated in a SCZ case/control sample (NCASE = 50; NCONTROL = 125), a replication sample (NCASE = 23; NCONTROL = 20) and a sample of asymptomatic young adults with extreme SCZ polygenic risk (NHIGH-SCZ-PRS = 95; NLOW-SCZ-PRS = 94). SCZ cases had higher SCZ-MRS than healthy controls in both samples (Study 1: β = 0.62, P < 0.001; Study 2: β = 0.81, P = 0.018). The high liability SCZ-PRS group also had a higher SCZ-MRS (Study 3: β = 0.29, P = 0.044). Furthermore, the SCZ-MRS was uniquely associated with SCZ status, but not attention-deficit hyperactivity disorder (ADHD), whereas an ADHD-MRS was linked to ADHD status, but not SCZ. This approach provides a promising solution when considering individual heterogeneity in SCZ-related brain alterations by identifying individual's patterns of structural brain-wide alterations., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Brain-imaging research on intentional decision-making often employs a "free-choice" paradigm, in which participants choose among options with identical values or outcomes. Although the medial prefrontal cortex has commonly been associated with choices, there is no consensus on the wider network that underlies diverse intentional decisions and behaviours. Our systematic literature search identified 35 fMRI/PET experiments using various free-choice paradigms, with appropriate control conditions using external instructions. An Activation Likelihood Estimate (ALE) meta-analysis showed that, compared with external instructions, intentional decisions consistently activate the medial and dorsolateral prefrontal cortex, the left insula and the inferior parietal lobule. We then categorized the studies into four different types according to their experimental designs: reactive motor intention, perceptual intention, inhibitory intention, and cognitive intention. We conducted conjunction and contrast meta-analyses to identify consistent and selective spatial convergence of brain activation within each specific category of intentional decision. Finally, we used meta-analytic decoding to probe cognitive processes underlying free choices. Our findings suggest that the neurocognitive process underlying intentional decision incorporates anatomically separated components subserving distinct cognitive and computational roles., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2021. Published by Elsevier Inc.)

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time-averaged approaches and sophisticated algorithms that can capture the time-varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word 'chronnectome' (integration of the Greek word 'Chronos', which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well-known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi-faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The impact of human values on our choices depends on their nature. Self-Transcendence values motivate us to act for the benefit of others and care for the environment. Self-Enhancement values motivate us to act for our benefit. The present study examines differences in the neural processes underlying these two value domains. Extending our previous research, we used fMRI to explore first of all neural correlates of Self-Transcendence vs Self-Enhancement values, with a particular focus on the putative role of the medial prefrontal cortex (MPFC), which has been linked to a self-transcendent mind-set. Additionally, we investigated the neural basis of Openness to Change vs Conservation values. We asked participants to reflect on and rate values as guiding principles in their lives while undergoing fMRI. Mental processing of Self-Transcendence values was associated with higher brain activity in the dorsomedial (BA9, BA8) and ventromedial (BA10) prefrontal cortices, as compared to Self-Enhancement values. The former involved activation and the latter deactivation of those regions. We did not detect differences in brain activation between Openness to Change vs Conservation values. Self-Transcendence values thus shared brain regions with social processes that have previously been linked to a self-transcendent mind-set, and the "core self" representation.

Positional brain shift (PBS), the sagging of the brain under the effect of gravity, is comparable in magnitude to the margin of error for the success of stereotactic interventions ([Formula: see text] 1 mm). This non-uniform shift due to slight differences in head orientation can lead to a significant discrepancy between the planned and the actual location of surgical targets. Accurate in-vivo measurements of this complex deformation are critical for the design and validation of an appropriate compensation to integrate into neuronavigational systems. PBS arising from prone-to-supine change of head orientation was measured with magnetic resonance imaging on 11 young adults. The full-field displacement was extracted on a voxel-basis via digital volume correlation and analysed in a standard reference space. Results showed the need for target-specific correction of surgical targets, as a significant displacement ranging from 0.52 to 0.77 mm was measured at surgically relevant structures. Strain analysis further revealed local variability in compressibility: anterior regions showed expansion (both volume and shape change), whereas posterior regions showed small compression, mostly dominated by shape change. Finally, analysis of correlation demonstrated the potential for further patient- and intervention-specific adjustments, as intra-cranial breadth and head tilt correlated with PBS reaching statistical significance., (© 2021. The Author(s).)

We provide a rich multi-contrast microstructural MRI dataset acquired on an ultra-strong gradient 3T Connectom MRI scanner comprising 5 repeated sets of MRI microstructural contrasts in 6 healthy human participants. The availability of data sets that support comprehensive simultaneous assessment of test-retest reliability of multiple microstructural contrasts (i.e., those derived from advanced diffusion, multi-component relaxometry and quantitative magnetisation transfer MRI) in the same population is extremely limited. This unique dataset is offered to the imaging community as a test-bed resource for conducting specialised analyses that may assist and inform their current and future research. The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and computed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols. Our data demonstrate high reproducibility of several microstructural MRI measures across scan sessions as shown by intra-class correlation coefficients and coefficients of variation. To illustrate a potential use of the MICRA dataset, we computed sample sizes required to provide sufficient statistical power a priori across different white matter pathways and microstructure measures for different statistical comparisons. We also demonstrate whole brain white matter voxel-wise repeatability in several microstructural maps. The MICRA dataset will be of benefit to researchers wishing to conduct similar reliability tests, power estimations or to evaluate the robustness of their own analysis pipelines., (Copyright © 2020. Published by Elsevier Inc.)

Introduction: We present the reliability of ultra-high field T 2 * MRI at 7T, as part of the UK7T Network's "Travelling Heads" study. T 2 *-weighted MRI images can be processed to produce quantitative susceptibility maps (QSM) and R 2 * maps. These reflect iron and myelin concentrations, which are altered in many pathophysiological processes. The relaxation parameters of human brain tissue are such that R 2 * mapping and QSM show particularly strong gains in contrast-to-noise ratio at ultra-high field (7T) vs clinical field strengths (1.5-3T). We aimed to determine the inter-subject and inter-site reproducibility of QSM and R 2 * mapping at 7T, in readiness for future multi-site clinical studies., Methods: Ten healthy volunteers were scanned with harmonised single- and multi-echo T 2 *-weighted gradient echo pulse sequences. Participants were scanned five times at each "home" site and once at each of four other sites. The five sites had 1× Philips, 2× Siemens Magnetom, and 2× Siemens Terra scanners. QSM and R 2 * maps were computed with the Multi-Scale Dipole Inversion (MSDI) algorithm (https://github.com/fil-physics/Publication-Code). Results were assessed in relevant subcortical and cortical regions of interest (ROIs) defined manually or by the MNI152 standard space., Results and Discussion: Mean susceptibility (χ) and R 2 * values agreed broadly with literature values in all ROIs. The inter-site within-subject standard deviation was 0.001-0.005 ppm (χ) and 0.0005-0.001 ms -1 (R 2 *). For χ this is 2.1-4.8 fold better than 3T reports, and 1.1-3.4 fold better for R 2 *. The median ICC from within- and cross-site R 2 * data was 0.98 and 0.91, respectively. Multi-echo QSM had greater variability vs single-echo QSM especially in areas with large B 0 inhomogeneity such as the inferior frontal cortex. Across sites, R 2 * values were more consistent than QSM in subcortical structures due to differences in B 0 -shimming. On a between-subject level, our measured χ and R 2 * cross-site variance is comparable to within-site variance in the literature, suggesting that it is reasonable to pool data across sites using our harmonised protocol., Conclusion: The harmonized UK7T protocol and pipeline delivers on average a 3-fold improvement in the coefficient of reproducibility for QSM and R 2 * at 7T compared to previous reports of multi-site reproducibility at 3T. These protocols are ready for use in multi-site clinical studies at 7T., (Copyright © 2020. Published by Elsevier Inc.)

Purpose: This study investigates the implications of all degrees of freedom of within-scan patient head motion on patient safety., Methods: Electromagnetic simulations were performed by displacing and/or rotating a virtual body model inside an 8-channel transmit array to simulate 6 degrees of freedom of motion. Rotations of up to 20° and displacements of up to 20 mm including off-axis axial/coronal translations were investigated, yielding 104 head positions. Quadrature excitation, RF shimming, and multi-spoke parallel-transmit excitation pulses were designed for axial slice-selection at 7T, for seven slices across the head. Variation of whole-head specific absorption rate (SAR) and 10-g averaged local SAR of the designed pulses, as well as the change in the maximum eigenvalue (worst-case pulse) were investigated by comparing off-center positions to the central position., Results: In their respective worst-cases, patient motion increased the eigenvalue-based local SAR by 42%, whole-head SAR by 60%, and the 10-g averaged local SAR by 210%. Local SAR was observed to be more sensitive to displacements along right-left and anterior-posterior directions than displacement in the superior-inferior direction and rotation., Conclusion: This is the first study to investigate the effect of all 6 degrees of freedom of motion on safety of practical pulses. Although the results agree with the literature for overlapping cases, the results demonstrate higher increases (up to 3.1-fold) in local SAR for off-axis displacement in the axial plane, which had received less attention in the literature. This increase in local SAR could potentially affect the local SAR compliance of subjects, unless realistic within-scan patient motion is taken into account during pulse design., (© 2020 International Society for Magnetic Resonance in Medicine.)

Preclinical MRI approaches constitute a key tool to study a wide variety of neurological and psychiatric illnesses, allowing a more direct investigation of the disorder substrate and, at the same time, the possibility of back-translating such findings to human subjects. However, the lack of consensus on the optimal experimental scheme used to acquire the data has led to relatively high heterogeneity in the choice of protocols, which can potentially impact the comparison between results obtained by different groups, even using the same animal model. This is especially true for diffusion-weighted MRI data, where certain experimental choices can impact not only on the accuracy and precision of the extracted biomarkers, but also on their biological meaning. With this in mind, we extensively examined preclinical imaging studies that used diffusion-weighted MRI to investigate neurodegenerative, neurodevelopmental and psychiatric disorders in rodent models. In this review, we discuss the main findings for each preclinical model, with a special focus on the analysis and comparison of the different acquisition strategies used across studies and their impact on the heterogeneity of the findings., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objectives: To use multi-parametric magnetic resonance imaging (MRI) to test the hypothesis that hypertensives would have higher retrograde venous blood flow (RVBF) in the internal jugular veins (IJV) vs. normotensives, and that this would inversely correlate with arterial inflow and gray matter, white matter, and cerebrospinal fluid volumes., Methods: Following local institutional review board approval and written consent, a prospective observational 3-T MRI study of 42 hypertensive patients (53 ± 2 years, BMI 28.2 ± 0.6 kg/m 2 , ambulatory daytime systolic BP 148 ± 2 mmHg, ambulatory daytime diastolic BP 101 ± 2 mmHg) and 35 normotensive patients (48 ± 2 years, BMI 25.2 ± 0.8 kg/m 2 , ambulatory daytime systolic BP 119 ± 3 mmHg, ambulatory daytime diastolic BP 90 ± 2 mmHg) was performed. Phase contrast imaging calculated percentage retrograde venous blood flow (%RVBF), brain segmentation estimated regional brain volumes from 3D T1-weighted images, and pseudo-continuous arterial spin labeling measured regional cerebral blood perfusion. Statistical analysis included two-sample equal variance Student's T tests, two-way analysis of variance with Tukey's post hoc correction, and permutation-based two-group general linear modeling (p < 0.05)., Results: In the left IJV, %RVBF was higher in hypertensives (6.1 ± 1.5%) vs. normotensives (1.1 ± 0.3%, p = 0.003). In hypertensives, there was an inverse relationship of %RVBF (permutation-based general linear modeling) to cerebral blood flow in several brain regions, including the left occipital pole and the cerebellar vermis (p < 0.01). Percentage retrograde flow in the left IJV correlated inversely with the total matter volume (gray plus white matter volume) in hypertensives (r = - 0.49, p = 0.004)., Conclusion: RVBF in the left IJV is greater in hypertensives vs. normotensives and is linked to regional hypoperfusion and brain total matter volume., Key Points: • Hypertensive humans have higher retrograde cerebral venous blood flow, associated with regional brain hypoperfusion and lower tissue volume, compared with controls. • Cerebral retrograde venous blood flow may add further stress to already hypoperfused tissue in hypertensive patients. • The amount of retrograde venous blood flow in hypertensive patients may predict which patients might be at higher risk of developing cerebral pathologies.

Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acquire high-quality multi-contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage-of-features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use., (© 2020 John Wiley & Sons, Ltd.)

Purpose: Sodium MRI shows great promise as a marker for cerebral metabolic dysfunction in stroke, brain tumor, and neurodegenerative pathologies. However, cerebral blood vessels, whose volume and function are perturbed in these pathologies, have elevated sodium concentrations relative to surrounding tissue. This study aims to assess whether this fluid compartment could bias measurements of tissue sodium using MRI., Methods: Density-weighted and B 1 corrected sodium MRI of the brain was acquired in 9 healthy participants at 4.7T. Veins were identified using co-registered 1 H T 2 ∗ -weighted images and venous partial volume estimates were calculated by down-sampling the finer spatial resolution venous maps from the T 2 ∗ -weighted images to the coarser spatial resolution of the sodium data. Linear regressions of venous partial volume estimates and sodium signal were performed for regions of interest including just gray matter, just white matter, and all brain tissue., Results: Linear regression demonstrated a significant venous sodium contribution above the underlying tissue signal. The apparent venous sodium concentrations derived from regression were 65.8 ± 4.5 mM (all brain tissue), 71.0 ± 7.4 mM (gray matter), and 55.0 ± 4.7 mM (white matter)., Conclusion: Although the partial vein linear regression did not yield the expected sodium concentration in blood (~87 mM), likely the result of point spread function smearing, this regression highlights that blood compartments may bias brain tissue sodium signals across neurological conditions where blood volumes may differ., (© 2019 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Alterations induced by prenatal exposure to nicotine have been observed in experimental (rodent) studies. While numerous developmental outcomes have been associated with prenatal exposure to maternal cigarette smoking (PEMCS) in humans, the possible relation with brain structure is less clear. Here we sought to elucidate the relation between PEMCS and structural properties of human corpus callosum in adolescence and early adulthood in a total of 1,747 youth. We deployed three community-based cohorts of 446 (age 25-27 years, 46% exposed), 934 (age 12-18 years, 47% exposed) and 367 individuals (age 18-21 years, 9% exposed). A mega-analysis revealed lower mean diffusivity in the callosal segments of exposed males. We speculate that prenatal exposure to maternal cigarette smoking disrupts the early programming of callosal structure and increases the relative portion of small-diameter fibres., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: The design and performance of a novel head coil setup for 31 P spectroscopy at ultra-high field strengths (7T) is presented. The described system supports measurements at both the 1 H and 31 P resonance frequencies., Methods: The novel coil consists of 2, actively detunable, coaxial birdcage coils to give homogeneous transmit, combined with a double resonant 30 channel receive array. This allows for anatomical imaging combined with 31 P acquisitions over the whole head, without changing coils or disturbing the subject. A phosphate buffer phantom and 3 healthy volunteers were scanned with a pulse acquire CSI sequence using both the novel array coil and a conventional transceiver birdcage. Four different methods of combining the array channels were compared at 3 different levels of SNR., Results: The novel coil setup delivers significantly increased 31 P SNR in the peripheral regions of the brain, reaching up to factor 8, while maintaining comparable performance relative to the birdcage in the center., Conclusions: The new system offers the potential to acquire whole brain 31 P MRSI with superior signal relative to the standard options., (© 2019 International Society for Magnetic Resonance in Medicine.)

Increasing numbers of 7 T (7 T) magnetic resonance imaging (MRI) scanners are in research and clinical use. 7 T MRI can increase the scanning speed, spatial resolution and contrast-to-noise-ratio of many neuroimaging protocols, but technical challenges in implementation have been addressed in a variety of ways across sites. In order to facilitate multi-centre studies and ensure consistency of findings across sites, it is desirable that 7 T MRI sites implement common high-quality neuroimaging protocols that can accommodate different scanner models and software versions. With the installation of several new 7 T MRI scanners in the United Kingdom, the UK7T Network was established with an aim to create a set of harmonized structural and functional neuroimaging sequences and protocols. The Network currently includes five sites, which use three different scanner platforms, provided by two different vendors. Here we describe the harmonization of functional and anatomical imaging protocols across the three different scanner models, detailing the necessary changes to pulse sequences and reconstruction methods. The harmonized sequences are fully described, along with implementation details. Example datasets acquired from the same subject on all Network scanners are made available. Based on these data, an evaluation of the harmonization is provided. In addition, the implementation and validation of a common system calibration process is described., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Impaired myelination may contribute to Huntington's disease (HD) pathogenesis., Objective: This study assessed differences in white matter (WM) microstructure between HD patients and controls, and tested whether drumming training stimulates WM remodelling in HD. Furthermore, it examined whether training-induced microstructural changes are related to improvements in motor and cognitive function., Methods: Participants undertook two months of drumming exercises. Working memory and executive function were assessed before and post-training. Changes in WM microstructure were investigated with diffusion tensor magnetic resonance imaging (DT-MRI)-based metrics, the restricted diffusion signal fraction (Fr) from the composite hindered and restricted model of diffusion (CHARMED) and the macromolecular proton fraction (MPF) from quantitative magnetization transfer (qMT) imaging. WM pathways linking putamen and supplementary motor areas (SMA-Putamen), and three segments of the corpus callosum (CCI, CCII, CCIII) were studied using deterministic tractography. Baseline MPF differences between patients and controls were assessed with tract-based spatial statistics., Results: MPF was reduced in the mid-section of the CC in HD subjects at baseline, while a significantly greater change in MPF was detected in HD patients relative to controls in the CCII, CCIII, and the right SMA-putamen post-training. Further, although patients improved their drumming and executive function performance, such improvements did not correlate with microstructural changes. Increased MPF suggests training-induced myelin changes in HD., Conclusion: Though only preliminary and based on a small sample size, these results suggest that tailored behavioural stimulation may lead to neural benefits in early HD, that could be exploited for delaying disease progression.

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm 2 ). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Cerebral Autoregulation (CA), defined as the ability of the cerebral vasculature to maintain stable levels of blood flow despite changes in systemic blood pressure, is a critical factor in neurophysiological health. Magnetic resonance imaging (MRI) is a powerful technique for investigating cerebrovascular function, offering high spatial resolution and wide fields of view (FOV), yet it is relatively underutilized as a tool for assessment of CA. The aim of this study was to demonstrate the potential of using MRI to measure changes in cerebrovascular resistance in response to lower body negative pressure (LBNP). A Pulsed Arterial Spin Labeling (PASL) approach with short inversion times (TI) was used to estimate cerebral arterial blood volume (CBV a ) in eight healthy subjects at baseline and -40mmHg LBNP. We estimated group mean CBV a values of 3.13 ± 1.00 and 2.70 ± 0.38 for baseline and lbnp respectively, which were the result of a differential change in CBV a during -40mmHg LBNP that was dependent on baseline CBV a . These data suggest that the PASL CBV a estimates are sensitive to the complex cerebrovascular response that occurs during the moderate orthostatic challenge delivered by LBNP, which we speculatively propose may involve differential changes in vascular tone within different segments of the arterial vasculature. These novel data provide invaluable insight into the mechanisms that regulate perfusion of the brain, and establishes the use of MRI as a tool for studying CA in more detail., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Functional magnetic resonance imaging (fMRI) is an essential workhorse of modern neuroscience, providing valuable insight into the functional organisation of the brain. The physiological mechanisms underlying the blood oxygenation level dependent (BOLD) effect are complex and preclude a straightforward interpretation of the signal. However, by employing appropriate calibration of the BOLD signal, quantitative measurements can be made of important physiological parameters including the absolute rate of cerebral metabolic oxygen consumption or oxygen metabolism (CMRO 2 ) and oxygen extraction (OEF). The ability to map such fundamental parameters has the potential to greatly expand the utility of fMRI and to broaden its scope of application in clinical research and clinical practice. In this review article we discuss some of the practical issues related to the calibrated-fMRI approach to the measurement of CMRO 2 . We give an overview of the necessary precautions to ensure high quality data acquisition, and explore some of the pitfalls and challenges that must be considered as it is applied and interpreted in a widening array of diseases and research questions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Acute cerebral hypoxia causes rapid calcium shifts leading to neuronal damage and death. Calcium channel antagonists improve outcomes in some clinical conditions, but mechanisms remain unclear. In 18 healthy participants we: (i) quantified with multiparametric MRI the effect of hypoxia on the thalamus, a region particularly sensitive to hypoxia, and on the whole brain in general; (ii) investigated how calcium channel antagonism with the drug nimodipine affects the brain response to hypoxia. Hypoxia resulted in a significant decrease in apparent diffusion coefficient (ADC), a measure particularly sensitive to cell swelling, in a widespread network of regions across the brain, and the thalamus in particular. In hypoxia, nimodipine significantly increased ADC in the same brain regions, normalizing ADC towards normoxia baseline. There was positive correlation between blood nimodipine levels and ADC change. In the thalamus, there was a significant decrease in the amplitude of low frequency fluctuations (ALFF) in resting state functional MRI and an apparent increase of grey matter volume in hypoxia, with the ALFF partially normalized towards normoxia baseline with nimodipine. This study provides further evidence that the brain response to acute hypoxia is mediated by calcium, and importantly that manipulation of intracellular calcium flux following hypoxia may reduce cerebral cytotoxic oedema.

fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors., (Copyright © 2018 Elsevier Inc. All rights reserved.)

There is increasing interest in exploring the use of functional MRI neurofeedback (fMRI-NF) as a therapeutic technique for a range of neurological conditions such as stroke and Parkinson's disease (PD). One main therapeutic potential of fMRI-NF is to enhance volitional control of damaged or dysfunctional neural nodes and networks via a closed-loop feedback model using mental imagery as the catalyst of self-regulation. The choice of target node/network and direction of regulation (increase or decrease activity) are central design considerations in fMRI-NF studies. Whilst it remains unclear whether the primary motor cortex (M1) can be activated during motor imagery, the supplementary motor area (SMA) has been robustly activated during motor imagery. Such differences in the regulation potential between primary and supplementary motor cortex are important because these areas can be differentially affected by a stroke or PD, and the choice of fMRI-NF target and grade of self-regulation of activity likely have substantial influence on the clinical effects and cost effectiveness of NF-based interventions. In this study we therefore investigated firstly whether healthy subjects would be able to achieve self-regulation of the hand-representation areas of M1 and the SMA using fMRI-NF training. There was a significant decrease in M1 neural activity during fMRI-NF, whereas SMA neural activity was increased, albeit not with the predicated graded effect. This study has important implications for fMRI-NF protocols that employ motor imagery to modulate activity in specific target regions of the brain and to determine how they may be tailored for neurorehabilitation., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Dual-calibrated fMRI is a multi-parametric technique that allows for the quantification of the resting oxygen extraction fraction (OEF), the absolute rate of cerebral metabolic oxygen consumption (CMRO 2 ), cerebral vascular reactivity (CVR) and baseline perfusion (CBF). It combines measurements of arterial spin labelling (ASL) and blood oxygenation level dependent (BOLD) signal changes during hypercapnic and hyperoxic gas challenges. Here we propose an extension to this methodology that permits the simultaneous quantification of the effective oxygen diffusivity of the capillary network (D C ). The effective oxygen diffusivity has the scope to be an informative biomarker and useful adjunct to CMRO 2 , potentially providing a non-invasive metric of microvascular health, which is known to be disturbed in a range of neurological diseases. We demonstrate the new method in a cohort of healthy volunteers (n = 19) both at rest and during visual stimulation. The effective oxygen diffusivity was found to be highly correlated with CMRO 2 during rest and activation, consistent with previous PET observations of a strong correlation between metabolic oxygen demand and effective diffusivity. The increase in effective diffusivity during functional activation was found to be consistent with previously reported increases in capillary blood volume, supporting the notion that measured oxygen diffusivity is sensitive to microvascular physiology., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Functional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to feedback from emotion-regulating regions. We tested in a single-blind, randomized, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = -0.415 [95% CI -4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18). Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative.

Perceptual learning refers to improved perceptual performance after intensive training and was initially suggested to reflect long-term plasticity in early visual cortex. Recent behavioral and neurophysiological evidence further suggested that the plasticity in brain regions related to decision making could also contribute to the observed training effects. However, how perceptual learning modulates the responses of decision-related regions in the human brain remains largely unknown. In the present study, we combined psychophysics and functional magnetic resonance imaging (fMRI), and adopted a model-based approach to investigate this issue. We trained participants on a motion direction discrimination task and fitted their behavioral data using the linear ballistic accumulator model. The results from model fitting showed that behavioral improvement could be well explained by a specific improvement in sensory information accumulation. A critical model parameter, the drift rate of the information accumulation, was correlated with the fMRI responses derived from three spatial independent components: ventral premotor cortex (PMv), supplementary eye field (SEF), and the fronto-parietal network, including intraparietal sulcus (IPS) and frontal eye field (FEF). In this decision network, we found that the behavioral training effects were accompanied by signal enhancement specific to trained direction in PMv and FEF. Further, we also found direction-specific signal reduction in sensory areas (V3A and MT+), as well as the strengthened effective connectivity from V3A to PMv and from IPS to FEF. These findings provide evidence for the learning-induced decision refinement after perceptual learning and the brain regions that are involved in this process.

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue., (© 2018 Wiley Periodicals, Inc.)

As energy metabolism in the brain is largely oxidative, the measurement of cerebral metabolic rate of oxygen consumption (CMRO 2 ) is a desirable biomarker for quantifying brain activity and tissue viability. Currently, PET techniques based on oxygen isotopes are the gold standard for obtaining whole brain CMRO 2 maps. Among MRI techniques that have been developed as an alternative are dual calibrated fMRI (dcFMRI) methods, which exploit simultaneous measurements of BOLD and ASL signals during a hypercapnic-hyperoxic experiment to modulate brain blood flow and oxygenation. In this study we quantified the repeatability of a dcFMRI approach developed in our lab, evaluating its limits and informing its application in studies aimed at characterising the metabolic state of human brain tissue over time. Our analysis focussed on the estimates of oxygen extraction fraction (OEF), cerebral blood flow (CBF), CBF-related cerebrovascular reactivity (CVR) and CMRO 2 based on a forward model that describes analytically the acquired dual echo GRE signal. Indices of within- and between-session repeatability are calculated from two different datasets both at a bulk grey matter and at a voxel-wise resolution and finally compared with similar indices obtained from previous MRI and PET measurements. Within- and between-session values of intra-subject coefficient of variation (CV intra ) calculated from bulk grey matter estimates 6.7 ± 6.6% (mean ± std.) and 10.5 ± 9.7% for OEF, 6.9 ± 6% and 5.5 ± 4.7% for CBF, 12 ± 9.7% and 12.3 ± 10% for CMRO 2 . Coefficient of variation (CV) and intraclass correlation coefficient (ICC) maps showed the spatial distribution of the repeatability metrics, informing on the feasibility limits of the method. In conclusion, results show an overall consistency of the estimated physiological parameters with literature reports and a satisfactory level of repeatability considering the higher spatial sensitivity compared to other MRI methods, with varied performance depending on the specific parameter under analysis, on the spatial resolution considered and on the study design., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI., New Method: Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme., Results: In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition., Comparison With Existing Method(s): The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature., Conclusions: Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: A core principle of creating a scientific evidence base is that results can be replicated in independent experiments and in health intervention research. The TIDieR (Template for Intervention Description and Replication) checklist has been developed to aid in summarising key items needed when reporting clinical trials and other well designed evaluations of complex interventions in order that findings can be replicated or built on reliably. Neurofeedback (NF) using functional MRI (fMRI) is a multicomponent intervention that should be considered a complex intervention. The TIDieR checklist (with minor modification to increase applicability in this context) was distributed to NF researchers as a survey of current practice in the design and conduct of clinical studies. The aim was to document practice and convergence between research groups, highlighting areas for discussion and providing a basis for recommendations for harmonisation and standardisation., Methods: The TIDieR checklist was interpreted and expanded (21 questions) to make it applicable to neurofeedback research studies. Using the web-based Bristol Online Survey (BOS) tool, the revised checklist was disseminated to researchers in the BRAINTRAIN European research collaborative network (supported by the European Commission) and others in the fMRI-neurofeedback community., Results: There were 16 responses to the survey. Responses were reported under eight main headings which covered the six domains of the TIDieR checklist: What, Why, When, How, Where and Who., Conclusions: This piece of work provides encouraging insight into the ability to be able to map neuroimaging interventions to a structured framework for reporting purposes. Regardless of the considerable variability of design components, all studies could be described in standard terms of diagnostic groups, dose/duration, targeted areas/signals, and psychological strategies and learning models. Recommendations are made which include providing detailed rationale of intervention design in study protocols., (Copyright © 2017 Elsevier Masson SAS. All rights reserved.)

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings., Section: Analysis methods., Classifications: Source localization: inverse problem; Source localization: other., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Understanding whether the cognitive profile of a patient indicates mild cognitive impairment (MCI) or performance levels within normality is often a clinical challenge. The use of resting-state functional magnetic resonance imaging (RS-fMRI) and machine learning may represent valid aids in clinical settings for the identification of MCI patients., Methods: Machine-learning models were computed to test the classificatory accuracy of cognitive, volumetric [structural magnetic resonance imaging (sMRI)] and blood oxygen level dependent-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers., Results: The best and most significant classifier was the RS-fMRI+Cognitive mixed classifier (94% accuracy), whereas the worst performing was the sMRI classifier (∼80%). The mixed global (sMRI+RS-fMRI+Cognitive) had a slightly lower accuracy (∼90%), although not statistically different from the mixed RS-fMRI+Cognitive classifier. The most important cognitive features were indices of declarative memory and semantic processing. The crucial volumetric feature was the hippocampus. The RS-fMRI features selected by the algorithms were heavily based on the connectivity of mediotemporal, left temporal, and other neocortical regions., Conclusion: Feature selection was profoundly driven by statistical independence. Some features showed no between-group differences, or showed a trend in either direction. This indicates that clinically relevant brain alterations typical of MCI might be subtle and not inferable from group analysis.

The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single "right" way to process resting state data that reveals the "true" nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the "cleaned" residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

FMRI-based neurofeedback transforms functional brain activation in real-time into sensory stimuli that participants can use to self-regulate brain responses, which can aid the modification of mental states and behavior. Emerging evidence supports the clinical utility of neurofeedback-guided up-regulation of hypoactive networks. In contrast, down-regulation of hyperactive neural circuits appears more difficult to achieve. There are conditions though, in which down-regulation would be clinically useful, including dysfunctional motivational states elicited by salient reward cues, such as food or drug craving. In this proof-of-concept study, 10 healthy females (mean age = 21.40 years, mean BMI = 23.53) who had fasted for 4 h underwent a novel 'motivational neurofeedback' training in which they learned to down-regulate brain activation during exposure to appetitive food pictures. FMRI feedback was given from individually determined target areas and through decreases/increases in food picture size, thus providing salient motivational consequences in terms of cue approach/avoidance. Our preliminary findings suggest that motivational neurofeedback is associated with functionally specific activation decreases in diverse cortical/subcortical regions, including key motivational areas. There was also preliminary evidence for a reduction of hunger after neurofeedback and an association between down-regulation success and the degree of hunger reduction. Decreasing neural cue responses by motivational neurofeedback may provide a useful extension of existing behavioral methods that aim to modulate cue reactivity. Our pilot findings indicate that reduction of neural cue reactivity is not achieved by top-down regulation but arises in a bottom-up manner, possibly through implicit operant shaping of target area activity.

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc., (© 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.)

Recent "representational" accounts suggest a key role for the hippocampus in complex scene perception. Due to limitations in scanner field strength, however, the functional neuroanatomy of hippocampal-dependent scene perception is unknown. Here, we applied 7 T high-resolution functional magnetic resonance imaging (fMRI) alongside a perceptual oddity task, modified from nonhuman primate studies. This task requires subjects to discriminate highly similar scenes, faces, or objects from multiple viewpoints, and has revealed selective impairments during scene discrimination following hippocampal lesions. Region-of-interest analyses identified a preferential response in the subiculum subfield of the hippocampus during scene, but not face or object, discriminations. Notably, this effect was in the anteromedial subiculum and was not modulated by whether scenes were subsequently remembered or forgotten. These results highlight the value of ultra-high-field fMRI in generating more refined, anatomically informed, functional accounts of hippocampal contributions to cognition, and a unique role for the human subiculum in discrimination of complex scenes from different viewpoints. SIGNIFICANCE STATEMENT There is increasing evidence that the human hippocampus supports functions beyond just episodic memory, with human lesion studies suggesting a contribution to the perceptual processing of navigationally relevant, complex scenes. While the hippocampus itself contains several small, functionally distinct subfields, examining the role of these in scene processing has been previously limited by scanner field strength. By applying ultra-high-resolution 7 T fMRI, we delineated the functional contribution of individual hippocampal subfields during a perceptual discrimination task for scenes, faces, and objects. This demonstrated that the discrimination of scenes, relative to faces and objects, recruits the anterior subicular region of the hippocampus, regardless of whether scenes were subsequently remembered or forgotten., (Copyright © 2017 Hodgetts et al.)

Objectives/hypothesis: This magnetic resonance imaging (MRI) study of 26 subjects with nasal congestion was performed to assess in the complete nasal passage both the anatomical effect of the marketed Breathe Right Nasal Strip (BRNS) relative to placebo and the potential adjunctive effect of using a decongestant in combination with the BRNS., Study Design: Randomized, crossover study., Methods: The study consisted of two parts, the first involving application of either the BRNS or the placebo strip in a randomized, crossover design with evaluator blinding, and repeated MRI scanning; and the second a sequential process of decongestant administration, MRI scanning, application of the BRNS, and repeated MRI. The same anatomical MRI protocol was used throughout. Nasal patency was assessed in the whole nasal passage and eight subregions (by inferior-superior, anterior-posterior division). Numerical response scores representing subjective nasal congestion were also obtained., Results: Results demonstrate significant anatomical enlargement with the BRNS relative to placebo (P < .001), as well as an additive effect of using a decongestant in combination with the BRNS; both supported by a strong and significant negative correlation with the subjective nasal response measures of nasal congestion (r = -0.98, P = .002). Furthermore, analysis of the nasal subregions indicates that this adjunctive effect arises from a partially localized action of the complementary products: the BRNS acting primarily anteriorly in the nose and the decongestant mainly posteriorly., Conclusions: The BRNS alone significantly increases nasal patency and alleviates perceived nasal congestion, and additional relief of symptoms can be obtained with simultaneous use of a decongestant., Level of Evidence: 1b. Laryngoscope, 126:2205-2211, 2016., (© 2016 The American Laryngological, Rhinological and Otological Society, Inc.)