Human Brain Mapping最新文献

Cognitive impairment is considered to be one of the key features of Parkinson's disease (PD), ultimately resulting in PD-related dementia in approximately 80% of patients over the course of the disease. Several distinct cognitive syndromes of PD have been suggested, driven by different neurotransmitter deficiencies and thus requiring different treatment regimes. In this study, we aimed to identify characteristic brain covariance patterns that reveal how cholinergic denervation is related to PD and to cognitive impairment, focusing on four domains, including attention, executive functioning, memory, and visuospatial cognition. We applied scaled sub-profile model principal component analysis to reveal cholinergic-specific disease-related and cognition-related covariance patterns using [¹⁸F]fluoroethoxybenzovesamicol PET imaging. Stepwise logistic regression was applied to predict disease state (PD vs. healthy control). Linear regression models were applied to predict cognitive functioning within the PD group, for each cognitive domain separately. We assessed the performance of the identified patterns with leave-one-out cross validation and performed bootstrapping to assess pattern stability. We included 34 PD patients with various levels of cognitive dysfunction and 10 healthy controls, with similar age, sex, and educational level. The disease-related cholinergic pattern was strongly discriminative (AUC 0.91), and was most prominent in posterior brain regions, with lower tracer uptake in patients compared to controls. We found largely overlapping cholinergic-specific patterns across cognitive domains, with positive correlations between tracer uptake in the opercular cortex, left dorsolateral prefrontal cortex and posterior cingulate gyrus, among other regions, and attention, executive, and visuospatial functioning. Cross validation showed significant correlations between predicted and measured cognition scores, with the exception of memory. We identified a robust structural covariance pattern for the assessment of cholinergic dysfunction related to PD, as well as overlapping cholinergic patterns related to attentional, executive- and visuospatial impairment in PD patients.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non-stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level-dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19–39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component-based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc.

Iron in the brain is essential to neurodevelopmental processes, as it supports neural functions, including processes of oxygen delivery, electron transport, and enzymatic activity. However, the development of brain iron before birth is scarcely understood. By estimating R2* (1/T2*) relaxometry from a sizable sample of fetal multiecho echo-planar imaging (EPI) scans, which is the standard sequence for functional magnetic resonance imaging (fMRI), across gestation, this study investigates age and sex-related changes in iron, across regions and tissue segments. Our findings reveal that brain R2* levels significantly increase throughout gestation spanning many different regions, except the frontal lobe. Furthermore, females exhibit a faster rate of R2* increase compared to males, in both gray matter and white matter. This sex effect is particularly notable within the left insula. This work represents the first MRI examination of iron accumulation and sex differences in developing fetal brains. This is also the first study to establish R2* estimation methodology in fetal multiecho functional MRI.

Language control processes allow for the flexible manipulation and access to context-appropriate verbal representations. Functional magnetic resonance imaging (fMRI) studies have localized the brain regions involved in language control processes usually by comparing high vs. low lexical–semantic control conditions during verbal tasks. Yet, the spectro-temporal dynamics of associated brain processes remain unexplored, preventing a proper understanding of the neural bases of language control mechanisms. To do so, we recorded functional brain activity using magnetoencephalography (MEG) and fMRI, while 30 healthy participants performed a silent verb generation (VGEN) and a picture naming (PN) task upon confrontation with pictures requiring low or high lexical–semantic control processes. fMRI confirmed the association between stronger language control processes and increased left inferior frontal gyrus (IFG) perfusion, while MEG revealed these controlled mechanisms to be associated with a specific sequence of early (< 500 ms) and late (> 500 ms) beta-band (de)synchronization processes within fronto-temporo-parietal areas. Particularly, beta-band modulations of event-related (de)synchronization mechanisms were first observed in the right IFG, followed by bilateral IFG and temporo-parietal brain regions. Altogether, these results suggest that beyond a specific recruitment of inferior frontal brain regions, language control mechanisms rely on a complex temporal sequence of beta-band oscillatory mechanisms over antero-posterior areas.

Implicit motor learning involves the acquisition and consolidation of motor skills without conscious awareness, influenced by various factors. Punishment and reward have been identified as significant modulators during training, impacting skill acquisition differently. Additionally, the role of a second declarative task in offline consolidation has been explored, affecting both stabilization and enhancement processes during wake and sleep periods. However, how valanced feedback and learning a secondary declarative task can influence the learning and consolidation of implicit motor learning has not been explored. This study investigates whether receiving monetary feedback during motor sequence learning influences consolidation when declarative knowledge about the task is disrupted by a second word-list task. Participants' skill levels were assessed during training, immediately after training, 15 min post-training (after performing the second task), and 24 h later after night sleep. Concurrently, brain synchrony was measured using electroencephalography (EEG) recording. Results indicate that monetary punishment leads to early enhancement and higher performance after the second task compared to reward and control groups. However, after 24 h, no significant enhancement was observed in any group, with differences between groups diminishing. EEG analysis revealed distinct brain subnetworks across alpha, beta, and unexpectedly delta network which traditionally associated with sleep-dependent consolidation. These findings shed light on the complex interplay between valanced feedback learning, declarative memory disruption, and offline consolidation in implicit motor learning, highlighting the dynamic nature of skill acquisition and retention, offering potential implications for targeted interventions and future research directions.

Apathy is a common neuropsychiatric symptom following stroke, characterized by reduced goal-directed behavior. The reward decision network (RDN), which plays a crucial role in regulating goal-directed behaviors, is closely associated with apathy. However, the relationship between poststroke apathy (PSA) and RDN dysfunction remains unclear due to apathy heterogeneity, the confounding effect of depression and individual variability in lesion impacts. This study aims to dissect the heterogeneity of PSA and explore the link between lesion-induced RDN damage and PSA. We prospectively recruited 207 patients with acute ischemic infarction and 60 demographically matched healthy controls. Participants underwent neuroimaging and longitudinal neuropsychiatric assessments. To characterize PSA heterogeneity, we employed multivariate analysis and clustering algorithms based on whole-brain functional connectivity and clinical assessments to classify patients into different PSA biotypes. We embedded each patient's lesion into a structural connectome atlas to obtain white matter (WM) disconnection maps. On this basis, WM disconnection scores were calculated for each brain region to quantify lesion-induced WM damage. We employed the XGBoost model to predict PSA biotypes based on WM disconnection scores, comparing the performance of models focusing on RDN-specific versus whole-brain WM disconnection. Additionally, we explored WM damage patterns across different biotypes by comparing disconnection scores in critical brain regions. We identified four PSA biotypes with unique clinical trajectories and neurobiological underpinnings. Biotype 4 was characterized by persistent apathy with depressive symptoms. Biotype 2 showed persistent apathy. Biotype 3 was non-apathetic. Biotype 1 exhibited delayed-onset apathy. The XGBoost models, when focused on the RDN-specific WM disconnection, performed significantly better in predicting PSA biotypes compared to the whole-brain WM disconnection model (t(164.66) = 8.871, p < 0.001). Analysis of WM disconnection patterns revealed that Biotype 4 exhibited more extensive RDN damage in crucial regions, Biotype 1 had a unique pattern of damage in the anterior cingulate cortex (t(61) = 1.874, p = 0.032), and Biotype 2 had a unique pattern of damage in the orbitofrontal cortex (t(53)= 1.827, p = 0.036). This study dissected PSA heterogeneity and demonstrated that RDN damage is a critical factor in PSA variability. We found that lesion-induced WM disconnections in anterior cingulate cortex and orbitofrontal cortex can lead to delayed-onset and persistent apathy, respectively. Furthermore, our findings revealed that apathy not only has distinct pathogenic mechanisms, but also shares neurobiological substrates with depression.

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level-dependent (BOLD) signal for 6–8 s, whereas the visual network propagates much quicker. We also show that summaries of network-specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been investigated for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 62 fetal brain scans and extracted a set of 51 distinct white matter tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20%–25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75% and 80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. These results highlight the relevance of these regions to key developmental processes, specifically, the dispersion of projection fibers, the protracted growth of commissural pathways, and the emergence of association tracts that contribute to the formation of complex intersection regions. These developmental interactions lead to a high prevalence of crossing fibers and bottleneck areas, reflecting the intricate organization required for establishing structural and functional connectivity. Additionally, our results highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.