Epistemic language in news headlines shapes readers' perceptions of objectivity.

How we reason about objectivity-whether an assertion has a ground truth-has implications for belief formation on wide-ranging topics. For example, if someone perceives climate change to be a matter of subjective opinion similar to the best movie genre, they may consider empirical claims about climate change as mere opinion and irrelevant to their beliefs. Here, we investigate whether the language employed by journalists might influence the perceived objectivity of news claims. Specifically, we ask whether factive verb framing (e.g., "Scientists know climate change is happening") increases perceived objectivity compared to nonfactive framing (e.g., "Scientists believe [...]"). Across eight studies (N = 2,785), participants read news headlines about unique, noncontroversial topics (studies 1a-b, 2a-b) or a familiar, controversial topic (climate change; studies 3a-b, 4a-b) and rated the truth and objectivity of the headlines' claims. Across all eight studies, when claims were presented as beliefs (e.g., "Tortoise breeders believe tortoises are becoming more popular pets"), people consistently judged those claims as more subjective than claims presented as knowledge (e.g., "Tortoise breeders know…"), as well as claims presented as unattributed generics (e.g., "Tortoises are becoming more popular pets"). Surprisingly, verb framing had relatively little, inconsistent influence over participants' judgments of the truth of claims. These results demonstrate how, apart from shaping whether we believe a claim is true or false, epistemic language in media can influence whether we believe a claim has an objective answer at all.

Local similarity and global variability characterize the semantic space of human languages.

How does meaning vary across the world's languages? Scholars recognize the existence of substantial variability within specific domains, ranging from nature and color to kinship. The emergence of large language models enables a systems-level approach that directly characterizes this variability through comparison of word organization across semantic domains. Here, we show that meanings across languages manifest lower variability within semantic domains and greater variability between them, using models trained on both 1) large corpora of native language text comprising Wikipedia articles in 35 languages and also 2) Test of English as a Foreign Language (TOEFL) essays written by 38,500 speakers from the same native languages, which cluster into semantic domains. Concrete meanings vary less across languages than abstract meanings, but all vary with geographical, environmental, and cultural distance. By simultaneously examining local similarity and global difference, we harmonize these findings and provide a description of general principles that govern variability in semantic space across languages. In this way, the structure of a speaker's semantic space influences the comparisons cognitively salient to them, as shaped by their native language, and suggests that even successful bilingual communicators likely think with "semantic accents" driven by associations from their native language while writing English. These findings have dramatic implications for language education, cross-cultural communication, and literal translations, which are impossible not because the objects of reference are uncertain, but because associations, metaphors, and narratives interlink meanings in different, predictable ways from one language to another.

Dissociable Contributions of the Medial Parietal Cortex to Recognition Memory.

Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its distinct functional characteristics during different retrieval tasks. Specifically, while recognition and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct spatial patterns of response within MPC. However, other studies have emphasized alternate MPC functional dissociations in terms of brain network connectivity profiles or stimulus category selectivity. As the unique contributions of MPC to episodic memory remain unclear, adjudicating between these different accounts can provide better consensus regarding MPC function. Therefore, we used a precision-neuroimaging dataset (7T functional magnetic resonance imaging) to examine how MPC regions are differentially engaged during recognition memory and how these task-related dissociations may also reflect distinct connectivity and stimulus category functional profiles. We observed interleaved, though spatially distinct, subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli. In addition, this dissociation was further accentuated by functional subregions displaying distinct profiles of connectivity with the hippocampus during task and rest. Finally, we show that recent observations of dissociable person and place selectivity within the MPC reflect category-specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, by examining precision functional mapping within individuals, these data suggest that previously distinct observations of functional dissociation within MPC conform to a common principle of organization throughout hippocampal-neocortical memory systems.

Distinctive and Complementary Roles of Default Mode Network Subsystems in Semantic Cognition.

The default mode network (DMN) typically deactivates to external tasks, yet supports semantic cognition. It comprises medial temporal (MT), core, and frontotemporal (FT) subsystems, but its functional organization is unclear: the requirement for perceptual coupling versus decoupling, input modality (visual/verbal), type of information (social/spatial), and control demands all potentially affect its recruitment. We examined the effect of these factors on activation and deactivation of DMN subsystems during semantic cognition, across four task-based human functional magnetic resonance imaging (fMRI) datasets, and localized these responses in whole-brain state space defined by gradients of intrinsic connectivity. FT showed activation consistent with a central role across domains, tasks, and modalities, although it was most responsive to abstract, verbal tasks; this subsystem uniquely showed more "tuned" states characterized by increases in both activation and deactivation when semantic retrieval demands were higher. MT also activated to both perceptually coupled (scenes) and decoupled (autobiographical memory) tasks and showed stronger responses to picture associations, consistent with a role in scene construction. Core DMN consistently showed deactivation, especially to externally oriented tasks. These diverse contributions of DMN subsystems to semantic cognition were related to their location on intrinsic connectivity gradients: activation was closer to the sensory-motor cortex than deactivation, particularly for FT and MT, while activation for core DMN was distant from both visual cortex and cognitive control. These results reveal distinctive yet complementary DMN responses: MT and FT support different memory-based representations that are accessed externally and internally, while deactivation in core DMN is associated with demanding, external semantic tasks.

Representation of event and object concepts in ventral anterior temporal lobe and angular gyrus.

Semantic knowledge includes understanding of objects and their features and also understanding of the characteristics of events. The hub-and-spoke theory holds that these conceptual representations rely on multiple information sources that are integrated in a central hub in the ventral anterior temporal lobes. The dual-hub theory expands this framework with the claim that the ventral anterior temporal lobe hub is specialized for object representation, while a second hub in angular gyrus is specialized for event representation. To test these ideas, we used representational similarity analysis, univariate and psychophysiological interaction analyses of fMRI data collected while participants processed object and event concepts (e.g. "an apple," "a wedding") presented as images and written words. Representational similarity analysis showed that angular gyrus encoded event concept similarity more than object similarity, although the left angular gyrus also encoded object similarity. Bilateral ventral anterior temporal lobes encoded both object and event concept structure, and left ventral anterior temporal lobe exhibited stronger coding for events. Psychophysiological interaction analysis revealed greater connectivity between left ventral anterior temporal lobe and right pMTG, and between right angular gyrus and bilateral ITG and middle occipital gyrus, for event concepts compared to object concepts. These findings support the specialization of angular gyrus for event semantics, though with some involvement in object coding, but do not support ventral anterior temporal lobe specialization for object concepts.

The neural representation of body part concepts.

Neuropsychological and neuroimaging studies provide evidence for a degree of category-related organization of conceptual knowledge in the brain. Some of this evidence indicates that body part concepts are distinctly represented from other categories; yet, the neural correlates and mechanisms underlying these dissociations are unclear. We expand on the limited prior data by measuring functional magnetic resonance imaging responses induced by body part words and performing a series of analyses investigating the cortical representation of this semantic category. Across voxel-level contrasts, pattern classification, representational similarity analysis, and vertex-wise encoding analyses, we find converging evidence that the posterior middle temporal gyrus, the supramarginal gyrus, and the ventral premotor cortex in the left hemisphere play important roles in the preferential representation of this category compared to other concrete objects.

Narratives imagined in response to instrumental music reveal culture-bounded intersubjectivity.

The scientific literature sometimes considers music an abstract stimulus, devoid of explicit meaning, and at other times considers it a universal language. Here, individuals in three geographically distinct locations spanning two cultures performed a highly unconstrained task: they provided free-response descriptions of stories they imagined while listening to instrumental music. Tools from natural language processing revealed that listeners provide highly similar stories to the same musical excerpts when they share an underlying culture, but when they do not, the generated stories show limited overlap. These results paint a more complex picture of music's power: music can generate remarkably similar stories in listeners' minds, but the degree to which these imagined narratives are shared depends on the degree to which culture is shared across listeners. Thus, music is neither an abstract stimulus nor a universal language but has semantic affordances shaped by culture, requiring more sustained attention from psychology.

Decoding the information structure underlying the neural representation of concepts.

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional "semantic hubs" contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

How Grammar Conveys Meaning: Language-Specific Spatial Encoding Patterns and Cross-Language Commonality in Higher-Order Neural Space.

Languages come in different forms but have shared meanings to convey. Some meanings are expressed by sentence structure and morphologic inflections rather than content words, such as indicating time frame using tense. This fMRI study investigates whether there is cross-language common representation of grammatical meanings that can be identified from neural signatures in the bilingual human brain. Based on the representations in intersentence neural similarity space, identifying grammatical construction of a sentence in one language by models trained on the other language resulted in reliable accuracy. By contrast, cross-language identification of grammatical construction by spatially matched activation patterns was only marginally accurate. Brain locations representing grammatical meaning in the two languages were interleaved in common regions bilaterally. The locations of voxels representing grammatical features in the second language were more varied across individuals than voxels representing the first language. These findings suggest grammatical meaning is represented by language-specific activation patterns, which is different from lexical semantics. Commonality of grammatical meaning is neurally reflected only in the interstimulus similarity space.SIGNIFICANCE STATEMENT Whether human brain encodes sentence-level meanings beyond content words in different languages similarly has been a long-standing question. We characterize the neural representations of similar grammatical meanings in different languages. Using complementary analytic approaches on fMRI data, we show that the same grammatical meaning is neurally represented as the common pattern of neural distances between sentences. The results suggest the possibility of identifying specific grammatical meaning expressed by different morphologic and syntactic implementations of different languages. The neural realization of grammatical meanings is constrained by the specific language being used, but the relationships between the neural representations of sentences are preserved across languages. These findings have some theoretical implications on a distinction between grammar and lexical meanings.

A Fluid Self-Concept: How the Brain Maintains Coherence and Positivity across an Interconnected Self-Concept While Incorporating Social Feedback.

People experience instances of social feedback as interdependent with potential implications for their entire self-concept. How do people maintain positivity and coherence across the self-concept while updating self-views from feedback? We present a network model describing how the brain represents the semantic dependency relations among traits and uses this information to avoid an overall loss of positivity and coherence. Both male and female human participants received social feedback during a self-evaluation task while undergoing functional magnetic resonance imaging. We modeled self-belief updating by incorporating a reinforcement learning model within the network structure. Participants learned more rapidly from positive than negative feedback and were less likely to change self-views for traits with more dependencies in the network. Further, participants back propagated feedback across network relations while retrieving prior feedback on the basis of network similarity to inform ongoing self-views. Activation in ventromedial prefrontal cortex (vmPFC) reflected the constrained updating process such that positive feedback led to higher activation and negative feedback to less activation for traits with more dependencies. Additionally, vmPFC was associated with the novelty of a trait relative to previously self-evaluated traits in the network, and angular gyrus was associated with greater certainty for self-beliefs given the relevance of prior feedback. We propose that neural computations that selectively enhance or attenuate social feedback and retrieve past relevant experiences to guide ongoing self-evaluations may support an overall positive and coherent self-concept.SIGNIFICANCE STATEMENT We humans experience social feedback throughout our lives, but we do not dispassionately incorporate feedback into our self-concept. The implications of feedback for our entire self-concept plays a role in how we either change or retain our prior self-beliefs. In a neuroimaging study, we find that people are less likely to change their beliefs from feedback when the feedback has broader implications for the self-concept. This resistance to change is reflected in processing in the ventromedial prefrontal cortex, a region that is central to self-referential and social cognition. These results are broadly applicable given the role that maintaining a positive and coherent self-concept plays in promoting mental health and development throughout the lifespan.

Biomedical semantic text summarizer.

BACKGROUND: Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. RESULTS: This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. CONCLUSION: The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies.

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.

Effective type label-based synergistic representation learning for biomedical event trigger detection.

BACKGROUND: Detecting event triggers in biomedical texts, which contain domain knowledge and context-dependent terms, is more challenging than in general-domain texts. Most state-of-the-art models rely mainly on external resources such as linguistic tools and knowledge bases to improve system performance. However, they lack effective mechanisms to obtain semantic clues from label specification and sentence context. Given its success in image classification, label representation learning is a promising approach to enhancing biomedical event trigger detection models by leveraging the rich semantics of pre-defined event type labels. RESULTS: In this paper, we propose the Biomedical Label-based Synergistic representation Learning (BioLSL) model, which effectively utilizes event type labels by learning their correlation with trigger words and enriches the representation contextually. The BioLSL model consists of three modules. Firstly, the Domain-specific Joint Encoding module employs a transformer-based, domain-specific pre-trained architecture to jointly encode input sentences and pre-defined event type labels. Secondly, the Label-based Synergistic Representation Learning module learns the semantic relationships between input texts and event type labels, and generates a Label-Trigger Aware Representation (LTAR) and a Label-Context Aware Representation (LCAR) for enhanced semantic representations. Finally, the Trigger Classification module makes structured predictions, where each label is predicted with respect to its neighbours. We conduct experiments on three benchmark BioNLP datasets, namely MLEE, GE09, and GE11, to evaluate our proposed BioLSL model. Results show that BioLSL has achieved state-of-the-art performance, outperforming the baseline models. CONCLUSIONS: The proposed BioLSL model demonstrates good performance for biomedical event trigger detection without using any external resources. This suggests that label representation learning and context-aware enhancement are promising directions for improving the task. The key enhancement is that BioLSL effectively learns to construct semantic linkages between the event mentions and type labels, which provide the latent information of label-trigger and label-context relationships in biomedical texts. Moreover, additional experiments on BioLSL show that it performs exceptionally well with limited training data under the data-scarce scenarios.

Myoglobin deficiency impairs maximal oxygen uptake and exercise performance: a lesson from Mb-/- mice.

Myoglobin (Mb) plays an important role at rest and during exercise as a reservoir of oxygen and has been suggested to regulate NO• bioavailability under hypoxic/acidic conditions. However, its ultimate role during exercise is still a subject of debate. We aimed to study the effect of Mb deficiency on maximal oxygen uptake ( V ̇ O 2 max ${\dot V_{{{\mathrm{O}}_2}\max }}$ ) and exercise performance in myoglobin knockout mice (Mb-/- ) when compared to control mice (Mb+/+ ). Furthermore, we also studied NO• bioavailability, assessed as nitrite (NO2 - ) and nitrate (NO3 - ) in the heart, locomotory muscle and in plasma, at rest and during exercise at exhaustion both in Mb-/- and in Mb+/+ mice. The mice performed maximal running incremental exercise on a treadmill with whole-body gas exchange measurements. The Mb-/- mice had lower body mass, heart and hind limb muscle mass (P < 0.001). Mb-/- mice had significantly reduced maximal running performance (P < 0.001). V ̇ O 2 max ${\dot V_{{{\mathrm{O}}_2}\max }}$ expressed in ml min-1 in Mb-/ - mice was 37% lower than in Mb+/+ mice (P < 0.001) and 13% lower when expressed in ml min-1  kg body mass-1 (P = 0.001). Additionally, Mb-/- mice had significantly lower plasma, heart and locomotory muscle NO2 - levels at rest. During exercise NO2 - increased significantly in the heart and locomotory muscles of Mb-/- and Mb+/+ mice, whereas no significant changes in NO2 - were found in plasma. Our study showed that, contrary to recent suggestions, Mb deficiency significantly impairs V ̇ O 2 max ${\dot V_{{{\mathrm{O}}_2}\max }}$ and maximal running performance in mice. KEY POINTS: Myoglobin knockout mice (Mb-/- ) possess lower maximal oxygen uptake ( V ̇ O 2 max ${\dot V_{{{\mathrm{O}}_2}\max }}$ ) and poorer maximal running performance than control mice (Mb+/+ ). Respiratory exchange ratio values at high running velocities in Mb-/- mice are higher than in control mice suggesting a shift in substrate utilization towards glucose metabolism in Mb-/- mice at the same running velocities. Lack of myoglobin lowers basal systemic and muscle NO• bioavailability, but does not affect exercise-induced NO2 - changes in plasma, heart and locomotory muscles. The present study demonstrates that myoglobin is of vital importance for V ̇ O 2 max ${\dot V_{{{\mathrm{O}}_2}\max }}$ and maximal running performance as well as explains why previous studies have failed to prove such a role of myoglobin when using the Mb-/- mouse model.

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

BACKGROUND: Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. RESULTS: In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. CONCLUSION: Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models.

To explain how the human brain represents and organizes meaning, many theoretical and computational language models have been proposed over the years, varying in their underlying computational principles and in the language samples based on which they are built. However, how well they capture the neural encoding of lexical semantics remains elusive. We used representational similarity analysis (RSA) to evaluate to what extent three models of different types explained neural responses elicited by word stimuli: an External corpus-based word2vec model, an Internal free word association model, and a Hybrid ConceptNet model. Semantic networks were constructed using word relations computed in the three models and experimental stimuli were selected through a community detection procedure. The similarity patterns between language models and neural responses were compared at the community, exemplar, and word node levels to probe the potential hierarchical semantic structure. We found that semantic relations computed with the Internal model provided the closest approximation to the patterns of neural activation, whereas the External model did not capture neural responses as well. Compared with the exemplar and the node levels, community-level RSA demonstrated the broadest involvement of brain regions, engaging areas critical for semantic processing, including the angular gyrus, superior frontal gyrus and a large portion of the anterior temporal lobe. The findings highlight the multidimensional semantic organization in the brain which is better captured by Internal models sensitive to multiple modalities such as word association compared with External models trained on text corpora.

B 1 + inhomogeneity mitigation for diffusion weighted MRI at 7T using TR-FOCI pulses.

PURPOSE: The purpose of this study is to improve the image quality of diffusion-weighted images obtained with a single RF transmit channel 7 T MRI setup using time-resampled frequency-offset corrected inversion (TR-FOCI) pulses to refocus the spins in a twice-refocused spin-echo readout scheme. METHODS: We replaced the conventional Shinnar-Le Roux-pulses in the twice refocused diffusion sequence with TR-FOCI pulses. The slice profiles were evaluated in simulation and experimentally in phantoms. The image quality was evaluated in vivo comparing the Shinnar-Le Roux and TR-FOCI implementation using a b value of 0 and of 1000 s/mm2. RESULTS: The b0 and diffusion-weighted images acquired using the modified sequence improved the image quality across the whole brain. A region of interest-based analysis showed an SNR increase of 113% and 66% for the nondiffusion-weighted (b0) and the diffusion-weighted (b = 1000 s/mm2) images in the temporal lobes, respectively. Investigation of all slices showed that the adiabatic pulses mitigated B 1 + $$ {B}_1^{+} $$ inhomogeneity globally using a conventional single-channel transmission setup. CONCLUSION: The TR-FOCI pulse can be used in a twice-refocused spin-echo diffusion pulse sequence to mitigate the impact of B 1 + $$ {B}_1^{+} $$ inhomogeneity on the signal intensity across the brain at 7 T. However, further work is needed to address SAR limitations.

The effect of and correction for through-slice dephasing on 2D gradient-echo double angle B 1 + mapping.

PURPOSE: To show that B 0 $$ {\mathrm{B}}_0 $$ variations through slice and slice profile effects are two major confounders affecting 2D dual angle B 1 + $$ {\mathrm{B}}_1^{+} $$ maps using gradient-echo signals and thus need to be corrected to obtain accurate B 1 + $$ {\mathrm{B}}_1^{+} $$ maps. METHODS: The 2D gradient-echo transverse complex signal was Bloch-simulated and integrated across the slice dimension including nonlinear variations in B 0 $$ {\mathrm{B}}_0 $$ inhomogeneities through slice. A nonlinear least squares fit was used to find the B 1 + $$ {\mathrm{B}}_1^{+} $$ factor corresponding to the best match between the two gradient-echo signals experimental ratio and the Bloch-simulated ratio. The correction was validated in phantom and in vivo at 3T. RESULTS: For our RF excitation pulse, the error in the B 1 + $$ {\mathrm{B}}_1^{+} $$ factor scales by approximately 3.8% for every 10 Hz/cm variation in B 0 $$ {\mathrm{B}}_0 $$ along the slice direction. Higher accuracy phantom B 1 + $$ {\mathrm{B}}_1^{+} $$ maps were obtained after applying the proposed correction; the root mean square B 1 + $$ {\mathrm{B}}_1^{+} $$ error relative to the gold standard B 1 + $$ {\mathrm{B}}_1^{+} $$ decreased from 6.4% to 2.6%. In vivo whole-liver T 1 $$ {\mathrm{T}}_1 $$ maps using the corrected B 1 + $$ {\mathrm{B}}_1^{+} $$ map registered a significant decrease in T 1 $$ {\mathrm{T}}_1 $$ gradient through slice. CONCLUSION: B 0 $$ {\mathrm{B}}_0 $$ inhomogeneities varying through slice were seen to have an impact on the accuracy of 2D double angle B 1 + $$ {\mathrm{B}}_1^{+} $$ maps using gradient-echo sequences. Consideration of this confounder is crucial for research relying on accurate knowledge of the true excitation flip angles, as is the case of T 1 $$ {\mathrm{T}}_1 $$ mapping using a spoiled gradient recalled echo sequence.

Improved gradient echo magnitude- and phase-based mapping of T 2 $$ {\mathrm{T}}_2 $$ using multiple RF spoiling increments at 3T and 7T.

PURPOSE: The transverse relaxation time T 2 $$ {}_2 $$ holds significant relevance in clinical applications and research studies. Conventional T 2 $$ {}_2 $$ mapping approaches rely on spin-echo sequences, which require lengthy acquisition times and involve high radiofrequency (RF) power deposition. An alternative gradient echo (GRE) phase-based T 2 $$ {}_2 $$ mapping method, utilizing steady-state acquisitions at one small RF spoil phase increment, was recently demonstrated. Here, a modified magnitude- and phase-based T 2 $$ {}_2 $$ mapping approach is proposed, which improves T 2 $$ {\mathrm{T}}_2 $$ estimations by simultaneous fitting of T 1 $$ {\mathrm{T}}_1 $$ and signal amplitude ( A ∝ P D $$ A\propto PD $$ ) at three or more RF spoiling phase increments, instead of assuming a fixed T 1 $$ {\mathrm{T}}_1 $$ value. METHODS: The feasibility of the magnitude-phase-based method was assessed by simulations, in phantom and in vivo experiments using skipped-CAIPI three-dimensional-echo-planar imaging (3D-EPI) for rapid GRE imaging. T 2 $$ {\mathrm{T}}_2 $$ , T 1 $$ {\mathrm{T}}_1 $$ and PD estimations obtained by our method were compared to T 2 $$ {\mathrm{T}}_2 $$ of the phase-based method and T 1 $$ {\mathrm{T}}_1 $$ and PD of spoiled GRE-based multi-parameter mapping using a multi-echo version of the same sequence. RESULTS: The agreement of the proposed T 2 $$ {\mathrm{T}}_2 $$ with ground truth and reference T 2 $$ {\mathrm{T}}_2 $$ values was higher than that of phase-based T 2 $$ {\mathrm{T}}_2 $$ in simulations and in phantom data. While phase-based T 2 $$ {\mathrm{T}}_2 $$ overestimation increases with actual T 2 $$ {\mathrm{T}}_2 $$ and T 1 $$ {\mathrm{T}}_1 $$ , the proposed method is accurate over a large range of physiologically meaningful T 2 $$ {\mathrm{T}}_2 $$ and T 1 $$ {\mathrm{T}}_1 $$ values. At the same time, precision is improved. In vivo results were in line with these observations. CONCLUSION: Accurate magnitude-phase-based T 2 $$ {}_2 $$ mapping is feasible in less than 5 min scan time for 1 mm nominal isotropic whole-head coverage at 3T and 7T.

Triple-tuned birdcage and single-tuned dipole array for quadri-nuclear head MRI at 7 T.

PURPOSE: The purpose of this work was to design and build a coil for quadri-nuclear MRI of the human brain at 7 T. METHODS: We built a transmit/receive triple-tuned (45.6 MHz for 2 $$ {}^2 $$ H, 78.6 MHz for 23 $$ {}^{23} $$ Na, and 120.3 MHz for 31 $$ {}^{31} $$ P) quadrature four-rod birdcage that was geometrically interleaved with a transmit/receive four-channel dipole array (297.2 MHz for 1 $$ {}^1 $$ H). The birdcage rods contained passive, two-pole resonant circuits that emulated capacitors required for single-tuning at three frequencies. The birdcage assembly also included triple-tuned matching networks, baluns, and transmit/receive switches. We assessed the performance of the coil with quality factor (Q) and signal-to-noise ratio (SNR) measurements, and performed in vivo multinuclear MRI and MR spectroscopic imaging (MRSI). RESULTS: Q measurements showed that the triple-tuned birdcage efficiency was within 33% of that of single-tuned baseline birdcages at all three frequencies. The quadri-tuned coil SNR was 78%, 59%, 44%, and 48% lower than that of single or dual-tuned reference coils for 1 $$ {}^1 $$ H, 2 $$ {}^2 $$ H, 23 $$ {}^{23} $$ Na, and 31 $$ {}^{31} $$ P, respectively. Quadri-nuclear MRI and MRSI was demonstrated in brain in vivo in about 30 min. CONCLUSION: While the SNR of the quadruple tuned coil was significantly lower than dual- and single-tuned reference coils, it represents a step toward truly simultaneous quadri-nuclear measurements.