NeuroImage最新文献

Translational magnetic resonance imaging of the rodent brain provides invaluable information for preclinical drug development. However, the automated segmentation of such images for quantitative analyses is limited compared to human brain imaging mainly due to the inferior anatomical contrast and the resulting less advanced registration and atlasing tools. Here, we investigated the potential of deep learning models for the segmentation of magnetic resonance images of rat brains into an entire set of multiple regions of interest (rather than individual loci), focusing on the development of a robust method that accommodates changes in the input based on differences in animal strain (genotype) and size. Manually generated labels are expensive, so we tested the ability of neural networks to learn brain structures from noisy but inexpensive registration-based labels, allowing very large datasets to be leveraged for training. We compared three distinct model architectures (U-Net, Attention-U-Net and DeepLab) by training them on a dataset of >10,000 magnetic resonance images of rat brains and found that each model was able to segment the entire brain into predefined sets of 29 and 58 regions, respectively, with the Attention U-Net achieving the best performance. The models canceled out unstructured label noise in the imperfect training data to provide smoother and more symmetric segmentations than registration-based labeling, and were more robust when presented with input variations, thus outperforming the noisy ground truth. Our pipeline also includes uncertainty estimation and an explainability mechanism, hence providing features essential for anomaly detection and quality assurance. In summary, our study shows that deep learning models do achieve accurate brain segmentation in high-throughput quantitative preclinical imaging without the need for expensive expert-generated labels.

The role of the cerebellum in phonetic processing has been discovered and widely discussed for decades. However, with the idea that the cerebral representation of phonetic processing is different in tonal language and non-tonal language speakers, whether the cerebellar representation of phonetic processing differs based on language background remains unknown. In the present study, we conducted an activation likelihood estimation (ALE) analysis among 33 functional neuroimaging studies involving 541 healthy adults (213 tonal language speakers and 328 non-tonal language speakers). The aim was to explore the cerebellar representation of phonetic perception and phonetic production in these two language backgrounds. Our results demonstrated the involvement of cerebellum left Crus I, right Crus II, lobules VI, and VIIb in phonetic perception among tonal language speakers, whereas only one focal cluster (right Crus I and Crus II) was demonstrated in non-tonal language speakers. Conjunction analysis revealed overlapping regions located in the right Crus II both in tonal and non-tonal language speakers during phonetic perception. During phonetic production, no significant cluster was detected among tonal language speakers, whereas one focal cluster (within right lobule VI) was detected in non-tonal language speakers. These results highlight the specific cerebellar representation of phonetic processing in tonal and non-tonal languages. Overall, this ALE analysis provides a profound view of the neural mechanism of phonetic processing.

The electroencephalogram (EEG) exhibits characteristics of complexity and strong randomness. Existing deep learning models for EEG typically target specific objectives and datasets, with their scalability constrained by the size of the dataset, resulting in limited perceptual and generalization abilities. In order to obtain more intuitive, concise, and useful representations of brain activity, we constructed a reconstruction-based self-supervised learning model for EEG based on Variational Autoencoder (VAE) with separate frequency bands, termed variational auto-encoder for EEG (VAEEG). VAEEG achieved outstanding reconstruction performance. Furthermore, we validated the efficacy of the latent representations in three clinical tasks concerning pediatric brain development, epileptic seizure, and sleep stage classification. We discovered that certain latent features: 1) correlate with adolescent brain developmental changes; 2) exhibit significant distinctions in the distribution between epileptic seizures and background activity; 3) show significant variations across different sleep cycles. In corresponding downstream fitting or classification tasks, models constructed based on the representations extracted by VAEEG demonstrated superior performance. Our model can extract effective features from complex EEG signals, serving as an early feature extractor for downstream classification tasks. This reduces the amount of data required for downstream tasks, simplifies the complexity of downstream models, and streamlines the training process.

The neural basis of sex-related differences in processing negative emotions remains poorly understood. The amygdala-related fiber pathways serve as the neuroanatomical foundation for emotion processing. However, the precise sex-related variations within these pathways remain largely elusive. Using diffusion magnetic resonance imaging data from 418 healthy individuals, we identified sex differences in white-matter microstructures of the striato-amygdaloid-prefrontal tracts, particularly the amygdala (Amy)-medial prefrontal cortex (mPFC) pathway. These differences were associated with various neurobiological factors, including pain-related negative emotions, pain sensitivity, neurotransmitter receptors, and gene expressions in the human brain. Our findings suggested that the Amy-mPFC pathway may serve as a neuroanatomical foundation for sex-specific negative emotion processing, driven by specific genetic and neurotransmitter profiles. Notably, we also found similar sex differences in this pathway in an infant imaging dataset, hinting at its developmental significance as a precursor to sex differences in adulthood. These findings underscore the importance of the striato-amygdaloid-prefrontal tracts in sex-related differences in processing negative emotions. This may enhance our understanding of sex-specific emotion regulation and potentially inform future research on strategies for preventing and diagnosing emotional regulation disorders across sexes.

Background: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders.

Objective: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs.

Methods: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions.

Results: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination.

Conclusion: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.

Non-invasive determination of amyloid-β peptide (Aβ) and tau deposition are important for early diagnosis and therapeutic intervention for Alzheimer's disease (AD) and non-AD tauopathies. In the present study, we investigated the capacity of a novel radioiodinated compound AD-DRK (^123/125I-AD-DRK) with 50% inhibitory concentrations of 11 nM and 2 nM for Aβ and tau aggregates, respectively, as a single photon emission computed tomography (SPECT) ligand in living brains. In vitro and ex vivo autoradiography with ¹²⁵I-AD-DRK was performed in postmortem human and two transgenic (Tg) mice lines with either fibrillar Aβ or tau accumulation, APP23 and rTg4510 mice. SPECT imaging of ¹²³I-AD-DRK was performed in APP23 mice to investigate the ability of AD-DRK to visualize fibrillar protein deposition in the living brain. In-vitro autoradiogram of ¹²⁵I-AD-DRK showed high specific radioactivity accumulation in the temporal cortex and hippocampus of AD patients and the motor cortex of progressive supranuclear palsy (PSP) patients enriched by Aβ and/or tau aggregates. Ex-vivo autoradiographic images also demonstrated a significant increase in ¹²⁵I-AD-DRK binding in the forebrain of both APP23 and rTg450 mice compared to their corresponding non-Tg littermates. SPECT imaging successfully captured Aβ deposition in the living brain of aged APP23 mice. The present study developed a novel high-contrast SPECT agent for assisting the diagnosis of AD and non-AD tauopathies, likely benefiting from its affinity for both fibrillar Aβ and tau.

The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is crucial for early treatment interventions. However, the heterogeneity of MCI data samples poses a challenge for CN vs. MCI vs. AD multiclass classification, as some samples are closer to AD while others are closer to CN in the feature space. Previous attempts to address this challenge produced inaccurate results, leading most frameworks to break the assessment into binary classification tasks such as AD vs. CN, AD vs. MCI, and CN vs. MCI. Other methods proposed sequential binary classifications such as CN vs. others and dividing others into AD vs. MCI. While those approaches may have yielded encouraging results, the sequential binary classification method makes interpretation and comparison with other frameworks challenging and subjective. Those frameworks exhibited varying accuracy scores for different binary tasks, making it unclear how to compare the model performance with other direct multiclass methods. Therefore, we introduce a classification framework comprising unsupervised ensemble manifold regularized sparse low-rank approximation and regularized multikernel support vector machine (SVM). This framework first extracts a joint feature embedding from MRI and PET neuroimaging features, which were then combined with the Apoe4, Adas11, MPACC digits, and Intracranial volume features using a regularized multikernel SVM. Using that framework, we achieved a state-of-the-art (SOTA) result in a CN vs. MCI vs. AD multiclass classification (mean accuracy: 84.87±6.09, F1 score: 84.83±6.12 vs 67.69). The methods generalize well to binary classification tasks, achieving SOTA results in all but the CN vs. MCI category, which was slightly lower than the best score by just 0.2%.

Background: Excessive iron accumulation in the brain has been implicated in Parkinson's disease (PD). However, the patterns and probable sequences of iron accumulation across the PD brain remain largely unknown. This study aimed to explore the sequence of iron accumulation across the PD brain using R₂* mapping and a relaxometry covariance network (RCN) approach.

Methods: R₂* quantification maps were obtained from PD patients (n = 34) and healthy controls (n = 25). RCN was configured on R₂* maps to identify covariance differences in iron levels between the two groups. Regions with excessive iron accumulation and large covariance changes in PD patients compared to controls were defined as propagators of iron. In the PD group, causal RCN analysis was performed on the R₂* maps sequenced according to disease duration to investigate the dynamics of iron accumulations from the propagators. The associations between individual connections of the RCN and clinical information were analyzed in PD patients.

Results: The left substantia nigra pars reticulata (SNpr), left substantia nigra pars compacta (SNpc), and lobule VII of the vermis (VER7) were identified as primary regions for iron accumulation and propagation (propagator). As the disease duration increased, iron accumulation in these three propagators demonstrated positive causal effects on the bilateral pallidum, bilateral gyrus rectus, right middle frontal gyrus, and medial and anterior orbitofrontal cortex (OFC). Furthermore, individual connections of VER7 with the left gyrus rectus and anterior OFC were positively associated with disease duration.

Conclusions: Our results indicate that the aberrant iron accumulation in PD involves several regions, mainly starts from the SN and cerebellum and extends to the pallidum and cortices. These findings provide preliminary information on sequences of iron accumulation in PD, which may advance our understanding of the disease.

Neuroimaging studies have identified functional and structural brain circuits that support reading. However, much less is known about how reading-related functional dynamics are constrained by white matter structure. Network control theory proposes that cortical brain dynamics are linearly determined by the white matter connectome, using control energy to evaluate the difficulty of the transition from one cognitive state to another. Here we apply this approach to linking brain dynamics with reading ability and disability in school-age children. A total of 51 children ages 8.25 -14.6 years performed an in-scanner rhyming task in visual and auditory modalities, with orthographic (spelling) and phonological (rhyming) similarity manipulated across trials. White matter structure and fMRI activation were used conjointly to compute the control energy of the reading network in each condition relative to a null fixation state. We then tested differences of control energy across trial types, finding higher control energy during non-word reading than word reading, and during incongruent trials than congruent trials. ROI analyses further showed a dissociation between control energy of the left fusiform and superior temporal gyrus depending on stimulus modality, with higher control energy for visual modalities in fusiform and higher control energy for auditory modalities in STG. Together, this study highlights that control theory can explain variations on cognitive demands in higher-level abilities such as reading, beyond what can be inferred from either functional or structural MRI measures alone.

Bilingual individuals manage multiple languages that align in conceptual meaning but differ in forms and structures. While prior research has established foundational insights into the neural mechanisms in bilingual processing, the extent to which the first (L1) and second language (L2) systems overlap or diverge across different linguistic components remains unclear. This study probed the neural underpinnings of syntactic and semantic processing for L1 and L2 in Chinese-English bilinguals (N = 44) who performed sentence comprehension tasks and an N-back working memory task during functional MRI scanning. We observed that the increased activation for L2 processing was within the verbal working memory network, suggesting a greater cognitive demand for processing L2. Crucially, we looked for brain regions showing adaptation to the repetition of semantic information and syntactic structure, and found more robust adaptation effects in L1 in the middle and superior temporal cortical areas. The differential adaptation effects between L1 and L2 were more pronounced for the semantic condition. Multivariate pattern analysis further revealed distinct neural sensitivities to syntactic and semantic representations between L1 and L2 across frontotemporal language regions. Our findings suggest that while L1 and L2 engage similar neural systems, finer representation analyses uncover distinct neural patterns for both semantic and syntactic aspects in the two languages. This study advances our understanding of neural representations involved in different language components in bilingual individuals.