Frontiers in neuroimaging最新文献

Purpose: To investigate the alterations of brain structure and function in brain regions related to ingestive desire in overweight/obese T2DM patients, and the correlation with clinical data.

Subjects: 52 patients with overweight/obese type 2 diabetes mellitus (T2DM group), 62 patients with simple obesity (OB group), and 40 healthy subjects (HC group).

Assessment: By means of gray matter morphometric indices (cortical thickness, surface area, etc.), resting-state functional magnetic resonance indices (ALFF, ReHo, FC) and DTI eigenvalues (AD, MD, etc.).

Statistical tests: Comparisons among the three groups were made using one-way ANOVA, bonferroni post hoc test for two-way comparisons, and spearman for correlation analysis.

Results: Compared with the OB and HC groups, the T2DM group showed a significant reduction in cortical thickness in the bilateral superior frontal gyrus, inferior frontal gyrus orbital region, and the lower part of the right middle frontal gyrus, and the functional connectivity of the prefrontal cortex showed a significant trend of enhancement. Meanwhile, compared with the HC group, the T2DM group showed a significant decrease in FA (fractional anisotropy) values in the midline region of the orbitofrontal cortex bilaterally, and the left inferior frontal gyrus orbital region also showed a significant decrease in FA values, whereas AD (axial diffusivity), MD (mean diffusivity), and RD (radial diffusivity) increased significantly.

Data conclusion: T2DM patients have significant alterations in gray matter structure, brain white matter integrity and brain function, and most of the brain regions with significant differences are in the prefrontal cortex, which confirms that the abnormal desire to ingest in T2DM patients is closely related to the functional alterations of the reward system, and that observing the brain function and structural changes of the reward loop through imaging may help in the early diagnosis and treatment of overweight/obese T2DM patients.

Background: A connection between cerebral white matter hyperintensities and coronary artery disease is widely discussed. Both conditions are more prevalent in the elderly. While white matter hyperintensities are often associated with small vessel disease, atherosclerosis is the primary cause of coronary artery disease.

Methods: We evaluated staging CT scans of the body and staging brain MRIs from patients with newly diagnosed malignant melanoma (without metastasis) between 01/01/2015 and 06/30/2023. CT scans were assessed for coronary artery disease using a modified overall visual assessment. Fazekas scores were used to evaluate the MRI for white matter changes. Additional clinical data were obtained from digital patient records.

Results: We analyzed data from 120 patients (57 females, mean age 68 years, standard deviation 14 years) and found a correlation between coronary artery disease and both age (r = 0.48, α = 0.04) and Fazekas score (periventricular r = 0.46, subcortical and deep white matter r = 0.55). A linear model including age, coronary artery disease, diabetes and arterial hypertension served as a predictor for white matter disease and showed significant correlations. Adding (1) atherosclerosis as well as (2) carotid stenosis to the model resulted in (1) a slight decrease in significance and (2) the unmasking of a potential spurious correlation with carotid stenosis.

Conclusion: There is a significant correlation between white matter hyperintensities and both carotid stenoses and coronary artery disease. This finding is clinically relevant: in patients with white matter hyperintensities and coronary artery disease, carotid stenosis should be ruled out.

Introduction: Tractography is the only available technique for visualizing whitematter pathways within the living brain. Avoiding these pathways during surgical interventions for brain tumors and epilepsy is key to reducing postoperative neurological deficits whilst achieving maximum safe resection. Despite this, the use of intraoperative tractography is not widely adopted in clinical practice, with time required to run analyses often cited as a limitation. This systematic review and meta-analysis aimed to assess the impact of intraoperative tractography on neurosurgical outcomes in both tumor and epilepsy surgeries.

Methods: Conducted in accordance with PRISMA guidelines, five major databases were searched using neurosurgery, tractography, brain tumor, and epilepsy terms. Original primary research studies in English were included. A risk of bias analysis was conducted using the MINORS tool.

Results: The search strategy identified 2,611 papers. Following de-duplication and screening, 26 papers were included in the final analysis. Risk of bias was found to be moderate. Findings suggest that the use of intraoperative tractography has the potential to improve surgical outcomes for patients undergoing tumor and epilepsy surgery. Meta-analysis indicated a good rate of gross total resection, 79%, and only three studies of brain tumors and one study of epilepsy reported worsening of neurological deficits.

Discussion: Though the evidence supporting its use remains limited, results indicate that intraoperative tractography can be a valuable tool in improving neurosurgical outcomes and reducing the risk of postoperative deficits. Further research is required to determine optimal use in clinical practice.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42023427427, Identifier: CRD42023427427.

Introduction: Associative memory (AM) is the most basic and common memory form. It constitutes the foundation of the declarative memory system, including all semantic and episodic memory processes. However, despite numerous studies, recent and remote memory retrieval processes in AM still need further elucidation.

Methods: Here, we investigated the neurofunctional correlates of recent and remote-related AM retrieval using associative face-name pairs of famous and non-famous individuals in a population of young, healthy adults (N = 23; mean age = 23.39 years). Particular interest was placed on the prominent anterior temporal lobe (ATL) found during recent and remote memory, including the right anterior insular (aIC) cortex and posterior midline region (PMR) previously observed during associative memory retrieval.

Results: The results of the present study revealed significant bilateral activation in the anterior parts of the STG as subdivision of the ATL during recent and remote memory retrieval. In addition, bilateral aIC activation was observed exclusively during recent memory retrieval, while PMR and ventromedial prefrontal cortex (vmPFC) activity was found only during remote memory retrieval.

Discussion: Thus, the present results corroborate the ATL's role as a common hub not only for AM retrieval but also for recent and remote memory processes. In addition, the recent and remote memory retrieval systems also appear to engage distinct neurofunctional networks to enable successful retrieval of contingent face-name pairs.

This study compares volumetric measurements of various brain regions using different magnetic resonance imaging (MRI) modalities and deep learning models, specifically 3T MRI, ultra-low field (ULF) MRI at 64mT, and AI-enhanced ULF MRI using SynthSR and HiLoResGAN. The aim is to evaluate the alignment and agreement among field strengths and ULF MRI with and without AI. Descriptive statistics, paired t-tests, effect size analyses, and regression analyses are employed to assess the relationships and differences between modalities. The results indicate that volumetric measurements derived from 64mT MRI deviate significantly from those obtained using 3T MRI. By leveraging SynthSR and LoHiResGAN models, these deviations are reduced, bringing the volumetric estimates closer to those obtained from 3T MRI, which serves as the reference standard for brain volume quantification. These findings highlight that deep learning models can reduce systematic differences in brain volume measurements across field strengths, providing potential solutions to minimize bias in imaging studies.

Introduction: Fourier base fitting for masked or incomplete structured data holds significant importance, for example in biomedical image data processing. However, data incompleteness destroys the simple unitary form of the Fourier transformation, necessitating the construction and solving of a linear system-a task that can suffer from poor conditioning and be computationally expensive. Despite its importance, suitable methodology addressing this challenge is not readily available.

Methods: In this study, we propose an efficient and fast Fourier base fitting method suitable for handling masked or incomplete structured data. The developed method can be used for processing multi-dimensional data, including smoothing and intra-/extrapolation, even when confronted with missing data.

Results: The developed method was verified using 1D, 2D, and 3D benchmarks. Its application is demonstrated in the reconstruction of noisy and partially unreliable brain pulsation data in the context of the development of a biomarker for non-invasive craniospinal compliance monitoring and neurological disease diagnostics.

Discussion: The study investigated the impact of different analytical and numerical performance improvement measures (e.g., term rearrangement, precomputation of recurring functions, vectorization) on computational complexity and speed. Quantitative evaluations on these benchmarks demonstrated that peak reconstruction errors in masked regions remained acceptable (i.e., below 10 % of the data range for all investigated benchmarks), while the proposed computational optimizations reduced matrix assembly time from 843 s to 11 s in 3D cases, demonstrating a 75-fold speed-up compared to unoptimized implementations. Singular value decomposition (SVD) can optionally be employed as part of the solving-step to provide regularization when needed. However, SVD quickly becomes the performance limiting in terms of computational complexity and resource cost, as the number of considered Fourier modes increases.

To facilitate high spatial-temporal resolution fMRI (≦1mm³) at more broadly available field strengths (3T) and to better understand the neural underpinnings of joy, we used SE-based generalized Slice Dithered Enhanced Resolution (gSLIDER). This sequence increases SNR efficiency utilizing sub-voxel shifts along the slice direction. To improve the effective temporal resolution of gSLIDER, we utilized the temporal information within individual gSLIDER RF encodings to develop gSLIDER with Sliding Window Accelerated Temporal resolution (gSLIDER-SWAT). We first validated gSLIDER-SWAT using a classic hemifield checkerboard paradigm, demonstrating robust activation in primary visual cortex even with stimulus frequency increased to the Nyquist frequency of gSLIDER (i.e., TR = block duration). gSLIDER provided ~2× gain in tSNR over traditional SE-EPI. GLM and ICA results suggest improved signal detection with gSLIDER-SWAT's nominal 5-fold higher temporal resolution that was not seen with simple temporal interpolation. Next, we applied gSLIDER-SWAT to investigate the neural networks underlying joy using naturalistic video stimuli. Regions significantly activated during joy included the left amygdala, specifically the basolateral subnuclei, and rostral anterior cingulate, both part of the salience network; the hippocampus, involved in memory; the striatum, part of the reward circuit; prefrontal cortex, part of the executive network and involved in emotion processing and regulation [bilateral mPFC/BA10/11, left MFG (BA46)]; and throughout visual cortex. This proof of concept study demonstrates the feasibility of measuring the networks underlying joy at high resolutions at 3T with gSLIDER-SWAT, and highlights the importance of continued innovation of imaging techniques beyond the limits of standard GE fMRI.

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along three dimensions: (1) parcellation schemes (JHU, AAL, BRO, and AICHA atlases), (2) neuroimaging modalities (resting-state functional connectivity, structural connectivity, mean diffusivity, fractional anisotropy, and lesion location) and (3) ML methods (Random Forest, Support Vector Regression, Decision Tree, K Nearest Neighbors, and Gradient Boosting). The best results were obtained by combining the JHU atlas, lesion location, and the Random Forest model. This combination yielded moderate to high correlations with the two different behavioral scores. Key regions identified included several perisylvian areas and pathways within the language network. This work complements existing LSM methods with new tools for improving the prediction of language outcomes in stroke survivors.

Introduction: Low-intensity focused ultrasound (LIFU) is a form of neuromodulation that offers increased depth of penetrance and improved spatial resolution over other non-invasive techniques, allowing for modulation of otherwise inaccessible subcortical structures that are implicated in neuropsychiatric pathologies. The amygdala is a target of great interest due to its involvement in numerous psychiatric conditions. While prior works have found that LIFU sonication of the amygdala can alter resting-state neural activation, only a few studies have investigated whether LIFU can selectively modulate the amygdala during task-based fMRI.

Methods: We aimed to address these gaps in literature in a cohort of 10 healthy individuals. We utilized the well-validated Emotional Face Assessment Task (EFAT), which is designed to robustly engage the amygdala. We selected the fusiform gyrus and the thalamus as our non-target regional comparison measures due to their roles in facial and emotional processing. In succession, participants completed a pre-LIFU baseline fMRI, received 10-min of LIFU neuromodulation, and then repeated the baseline fMRI. To test our hypothesis, we conducted paired-samples t-tests assessing changes in amygdala, fusiform gyrus, and thalamic activation from pre to post scan.

Results: We found that there was a significant decrease in left (t(9) = 2.286; p = 0.024) and right (t(9) = 2.240; p = 0.026) amygdala activation from pre-to-post sonication.

Discussion: Meanwhile, there were no differences in activation of the left or right fusiform gyrus or thalamus. Our results indicate that LIFU of the amygdala acutely dampens amygdala reactivity during active socio-emotional processing.

Introduction: While risk factors have been identified for numerous psychiatric disorders, many individuals exposed to these risk factors do not develop psychopathology. A growing neuroimaging literature has sought to find structural and functional brain features that confer psychological resilience against developing psychiatric disorders.

Methods: We conducted a systematic review and meta-analysis of neuroimaging studies associated with psychological resilience. Searches of Pubmed, Embase, Web of Science and PsychInfo yielded 2,658 potentially relevant articles published 2000-2021. Of these, we identified 154 human neuroimaging articles which provided anatomical coordinates of regions promoting resilience against psychiatric disorders including PTSD (44% of articles), schizophrenia (18%), major depressive disorder (14%) and bipolar disorder (12%).

Results: Meta-analysis conducted in GingerALE identified three regions as promoting psychological resilience across disorders (cluster-level FWE p < 0.05): left amygdala, right amygdala, and anterior cingulate.

Discussion: We additionally introduce a novel framework for conducting systematic reviews and meta-analyses that is compliant with best practices of Open Science: our publicly viewable systematic review was curated and annotated using the open-source reference manager Zotero, with customizable Python scripts for extracting curated data for meta-analyses. Our methodological pipeline not only permits independent replication of our findings but also supports customization for future neuroimaging meta-analyses.