Human Brain Mapping最新文献

Traumatic brain injury (TBI) is considered to initiate cerebrovascular pathology, involving in the development of multiple forms of neurodegeneration. However, it is unknown the relationships between imaging marker of cerebrovascular injury (white matter hyperintensity, WMH), its load on white matter tract and disrupted brain dynamics with cognitive function in mild TBI (mTBI). MRI data and neuropsychological assessments were collected from 85 mTBI patients and 52 healthy controls. Between-group difference was conducted for the tract-specific WMH volumes, white matter integrity, and dynamic brain connectivity (i.e., fractional occupancies [%], dwell times [seconds], and state transitions). Regression analysis was used to examine associations between white matter damage, brain dynamics, and cognitive function. Increased WMH volumes induced by mTBI within the thalamic radiation and corpus callosum were highest among all tract fibers, and related with altered fractional anisotropy (FA) within the same tracts. Clustering identified two brain states, segregated state characterized by the sparse inter-independent component connections, and default mode network (DMN)-centered integrated state with strongly internetwork connections between DMN and other networks. In mTBI, higher WMH loads contributed to the longer dwell time and larger fractional occupancies in DMN-centered integrated state. Every 1 mL increase in WMH volume within the left thalamic radiation was associated with a 47% increase fractional occupancies, and contributed to 65.6 s delay in completion of cognitive processing speed test. Our study provided the first evidence for the structural determinants (i.e., small vessel lesions) that mediate the spatiotemporal brain dynamics to cognitive impairments in mTBI.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly important for the diagnosis of various brain disorders, where structural information plays a crucial role in diseases such as Alzheimer's, while functional imaging is more critical for disorders such as schizophrenia. However, the combination of functional and structural imaging modalities can provide a more comprehensive diagnosis. In this work, we present MultiViT, a novel diagnostic deep learning model that utilizes vision transformers and cross-attention mechanisms to effectively fuse information from 3D gray matter maps derived from structural MRI with functional network connectivity matrices obtained from functional MRI using the ICA algorithm. MultiViT achieves an AUC of 0.833, outperforming both our unimodal and multimodal baselines, enabling more accurate classification and diagnosis of schizophrenia. In addition, using vision transformer's unique attentional maps in combination with cross-attentional mechanisms and brain function information, we identify critical brain regions in 3D gray matter space associated with the characteristics of schizophrenia. Our research not only significantly improves the accuracy of AI-based automated imaging diagnostics for schizophrenia, but also pioneers a rational and advanced data fusion approach by replacing complex, high-dimensional fMRI information with functional network connectivity, integrating it with representative structural data from 3D gray matter images, and further providing interpretative biomarker localization in a 3D structural space.

National and international biobanking efforts led to the collection of large and inclusive imaging genetics datasets that enable examination of the contribution of genetic and environmental factors to human brains in illness and health. High-resolution neuroimaging (~10^4–6 voxels) and genetic (10^6–8 single nucleotide polymorphic [SNP] variants) data are available in statistically powerful (N = 10^3–5) epidemiological and disorder-focused samples. Performing imaging genetics analyses at full resolution afforded in these datasets is a formidable computational task even under the assumption of unrelatedness among the subjects. The computational complexity rises as ~N^2–3 (where N is the sample size), when accounting for relatedness among subjects. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees. These approaches linearize (from N^2–3 to N^~1) computational effort while maintaining fidelity (r ~ 0.95) with the VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches lead to a 10⁴- to 10⁶-fold reduction in computational complexity—making voxel-wise heritability, genetic correlation, and genome-wide association studies (GWAS) analysis practical for large and complex samples such as those provided by the Amish and Human Connectome Projects (N = 406 and 1052 subjects, respectively) and UK Biobank (N = 31,681). These developments are shared in open-source, SOLAR-Eclipse software.

Subcortical volumes are a promising source of biomarkers and features in biosignatures, and automated methods facilitate extracting them in large, phenotypically rich datasets. However, while extensive research has verified that the automated methods produce volumes that are similar to those generated by expert annotation; the consistency of methods with each other is understudied. Using data from the UK Biobank, we compare the estimates of subcortical volumes produced by two popular software suites: FSL and FreeSurfer. Although most subcortical volumes exhibit good to excellent consistency across the methods, the tools produce diverging estimates of amygdalar volume. Through simulation, we show that this poor consistency can lead to conflicting results, where one but not the other tool suggests statistical significance, or where both tools suggest a significant relationship but in opposite directions. Considering these issues, we discuss several ways in which care should be taken when reporting on relationships involving amygdalar volume.

Asymmetry in choice patterns across rewarding and punishing contexts has long been observed in behavioural economics. Within existing theories of reinforcement learning, the mechanistic account of these behavioural differences is still debated. We propose that motivational salience—the degree of bottom-up attention attracted by a stimulus with relation to motivational goals—offers a potential mechanism to modulate stimulus value updating and decision policy. In a probabilistic reversal learning task, we identified post-feedback signals from EEG and pupillometry that captured differential activity with respect to rewarding and punishing contexts. We show that the degree of between-context distinction in these signals predicts interindividual asymmetries in decision accuracy. Finally, we contextualise these effects in relation to the neural pathways that are currently centred in theories of reward and punishment learning, demonstrating how the motivational salience network could plausibly fit into a range of existing frameworks.

White matter hyperintensities (WMH), a common feature of cerebral small vessel disease, affect a wide range of cognitive dysfunctions, including spatial neglect. The latter is a disorder of spatial attention and exploration typically after right hemisphere brain damage. To explore the impact of WMH on neglect-related structural disconnections, the present study investigated the indirectly quantified structural disconnectome induced by either stroke lesion alone, WMH alone, or their combination. Furthermore, we compared different measures of structural disconnection—voxel-wise, pairwise, tract-wise, and parcel-wise—to identify neural correlates and predict acute neglect severity. We observed that WMH-derived disconnections alone were not associated with neglect behavior. However, when combined with disconnections derived from individual stroke lesions, pre-stroke WMH contributed to post-stroke neglect severity by affecting right frontal and subcortical substrates, like the middle frontal gyrus, basal ganglia, thalamus, and the fronto-pontine tract. Predictive modeling demonstrated that voxel-wise disconnection data outperformed other measures of structural disconnection, explaining 42% of the total variance; interestingly, the best model used predictors of stroke-based disconnections only. We conclude that prestroke alterations in the white matter microstructure due to WMH contribute to poststroke deficits in spatial attention, likely by impairing the integrity of human attention networks.

Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how removal of input changes brain function. However, an important question yet to be answered is: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. When central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with some portions receiving “preferential” usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. Specifically, we examined functional connectivity between category-selective visual areas and the cortical representation of three areas of the retina: the lesioned area, a preferentially used region of the intact retina, and a non-preferentially used region. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that the long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. In the experiments, we perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state-of-the-art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities—structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI—in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of cross-modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age-related sources replicates across two independent and separately analyzed datasets. The same set also presented age-adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset.

One of the fundamental questions in real-time functional magnetic resonance imaging neurofeedback (rt-fMRI NF) investigations is the definition of a suitable neural target for training. Previously, we applied a meta-analytical approach to define a network-level target for connectivity-based rt-fMRI NF in substance use disorders. The analysis yielded consistent connectivity alterations between the insula and anterior cingulate cortex (ACC) as well as the dorsal striatum and the ACC. In the current investigation, we addressed the feasibility of regulating this network and its functional relevance using connectivity-based neurofeedback. In a double-blind, sham-controlled design, 60 nicotine users were randomly assigned to the experimental or sham control group for one NF training session. The preregistered primary outcome was defined as improved inhibitory control performance after regulation of the target network compared to sham control. Secondary outcomes were (1) neurofeedback-specific changes in functional connectivity of the target network; (2) changes in smoking behavior and impulsivity measures; and (3) changes in resting-state connectivity profiles. Our results indicated no differences in behavioral measures after receiving feedback from the target network compared to the sham feedback. Target network connectivity was increased during regulation blocks compared to rest blocks, however, the experimental and sham groups could regulate to a similar degree. Accordingly, the observed activation patterns may be related to the mental strategies used during regulation attempts irrespective of the group assignment. We discuss several crucial factors regarding the efficacy of a single-session connectivity-based neurofeedback for the target network. This includes high fluctuation in the connectivity values of the target network that may impact controllability of the signal. To our knowledge, this investigation is the first randomized, double-blind controlled real-time fMRI study in nicotine users. This raises the question of whether previously observed effects in nicotine users are specific to the neurofeedback signal or reflect more general self-regulation attempts.