NeuroImage最新文献

The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.

The concept of structural reserve in stroke reorganization assumes that the relevance of the contralesional hemisphere strongly depends on the brain tissue spared by the lesion in the affected hemisphere. Recent studies, however, have indicated that the contralesional hemisphere's impact exhibits region-specific variability with concurrently existing maladaptive and supportive influences. This challenges traditional views, necessitating a nuanced investigation of contralesional motor areas and their interaction with ipsilesional networks.

Our study focused on the functional role of contralesional key motor areas and lesion-induced connectome disruption early after stroke.

Online TMS data of twenty-five stroke patients was analyzed to disentangle interindividual differences in the functional roles of contralesional primary motor cortex (M1), dorsal premotor cortex (dPMC), and anterior interparietal sulcus (aIPS) for motor function. Connectome-based lesion symptom mapping and corticospinal tract lesion quantification were used to investigate how TMS effects depend on ipsilesional structural network properties.

At group and individual levels, TMS interference with contralesional M1 and aIPS but not dPMC led to improved performance early after stroke. At the connectome level, a more disturbing role of contralesional M1 was related to a more severe disruption of the structural integrity of ipsilesional M1 in the affected motor network. In contrast, a detrimental influence of contralesional aIPS was linked to less disruption of the ipsilesional M1 connectivity.

Our findings indicate that contralesional areas distinctively interfere with motor performance early after stroke depending on ipsilesional structural integrity, extending the concept of structural reserve to regional specificity in recovery of function.

In previous studies, the magnetic lead field theorem in the quasi-static approximation was derived and used for the development of a method for the forward problem of MEG. It was applied and tested on a single-shell model of the human head and the question whether one shell is adequate enough for the calculation of the magnetic field is the main reason for this study. This forward method is based on the fundamental concept that one can calculate the lead field for MEG by decomposing it into two parts: the lead field of an arbitrary volume conductor that is already known and the gradient of basis functions that have to be harmonic, here derived from spherical harmonics. The problem then is reduced to evaluating the coefficients found in the basis functions. In this research we aim to improve the accuracy of the forward model, hence improving the localization accuracy in inverse methods by introducing a more detailed realistic head model. We here generalize the algorithm developed for a single-shell volume conductor to a three-shell volume conductor representing the brain, the skull and the skin with homogenous and isotropic conductivities in realistic ratios. The expansion to three shells could be tested as the three-shell algorithm is approaching the single-shell with high accuracy in special cases where three-shell solutions can also be calculated using a single-shell solution, especially for higher levels of expansion. The deviation in the calculation of the lead field is also evaluated when using three shells with realistic conductivities. The magnetic field turned out to differ to an important measurable extend in particular for deeper sources, making the three-shell algorithm substantially more accurate for these dipole locations.

The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

Natural Braille reading presents significant challenges to the brain networks of late blind individuals, yet its underlying neural mechanisms remain largely unexplored. Using natural Braille texts in behavioral assessments and functional MRI, we sought to pinpoint the neural pathway and information flow crucial for Braille reading performance in late blind individuals. In the resting state, we discovered a unique neural connection between the higher-order ‘visual’ cortex, the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in late blind individuals, but not in sighted controls. The left-lateralized LOC-IFC connectivity was correlated with individual Braille reading proficiency. Prolonged Braille reading practice led to increased strength of this connectivity. During a natural Braille reading task, bidirectional information flow between the LOC and the IFC was positively modulated, with a predominantly stronger top-down modulation from the IFC to the LOC. This stronger top-down modulation contributed to higher Braille reading proficiency. We thus proposed a two-predictor multiple regression model to predict individual Braille reading proficiency, incorporating both static connectivity and dynamic top-down communication between the LOC-IFC link. This work highlights the dual contributions of the occipito-frontal neural pathway and top-down cognitive strategy to superior natural Braille reading performance, offering guidance for training late blind individuals.

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a powerful technique that can map the metabolic profile in the brain non-invasively. Extracranial lipid contamination and insufficient B₀ homogeneity however hampers robustness, and as a result has hindered widespread use of MRSI in clinical and research settings. Over the last six years we have developed highly effective extracranial lipid suppression methods with a second order gradient insert (ECLIPSE) utilizing inner volume selection (IVS) and outer volume suppression (OVS) methods. While ECLIPSE provides > 100-fold in lipid suppression with modest radio frequency (RF) power requirements and immunity to B₁⁺ field variations, axial coverage is reduced for non-elliptical head shapes. In this work we detail the design, construction, and utility of MC-ECLIPSE, a pulsed second order gradient coil with Z2 and X2Y2 fields, combined with a 54-channel multi-coil (MC) array. The MC-ECLIPSE platform allows arbitrary region of interest (ROI) shaped OVS for full-axial slice coverage, in addition to MC-based B₀ field shimming, for robust human brain proton MRSI.

In vivo experiments demonstrate that MC-ECLIPSE allows axial brain coverage of 92–95 % is achieved following arbitrary ROI shaped OVS for various head shapes. The standard deviation (SD) of the residual B₀ field following SH2 and MC shimming were 25 ± 9 Hz and 18 ± 8 Hz over a 5 cm slab, and 18 ± 5 Hz and 14 ± 6 Hz over a 1.5 cm slab, respectively. These results demonstrate that B₀ magnetic field shimming with the MC array supersedes second order harmonic capabilities available on standard MRI systems for both restricted and large ROIs. Furthermore, MC based B₀ shimming provides comparable shimming performance to an unrestricted SH5 shim set for both restricted, and 5-cm slab shim challenges.

Phantom experiments demonstrate the high level of localization performance achievable with MC-ECLIPSE, with ROI edge chemical shift displacements ranging from 1–3 mm with a median value of 2 mm, and transition width metrics ranging from 1–2.5 mm throughout the ROI edge. Furthermore, MC based B₀ shimming is comparable to performance following a full set of unrestricted spherical harmonic fields up to order 5. Short echo time MRSI and GABA-edited MRSI acquisitions in the human brain following MC-shimming and arbitrary ROI shaping demonstrate full-axial slice coverage and extracranial lipid artifact free spectra. MC-ECLIPSE allows full-axial coverage and robust MRSI acquisitions, while allowing interrogation of cortical tissue proximal to the skull, which has significant value in a wide range of neurological and psychiatric conditions.