The parcellation of cingulate cortex in neonatal period based on resting-state functional MRI.

The human cingulate cortex (CC) is a complex region that is characterized by heterogeneous cytoarchitecture, connectivity, and function, and it is associated with various cognitive functions. The adult CC has been divided into various subregions, and this subdivision is highly consistent with its functional differentiation. However, only a few studies have focused on the function of neonatal CC. The aim of this study was to describe the cingulate segregation and the functional connectivity of each subdivision in full-term neonates (n = 60) based on resting-state functional magnetic resonance imaging. The neonatal CC was divided into three subregions, and each subregion showed specific connectivity patterns. The anterior cingulate cortex was mainly correlated with brain regions related to the salience (affected) network and default mode network (DMN), the midcingulate cortex was related to motor areas, and the posterior cingulate cortex was coupled with DMN. Moreover, we found that the cingulate subregions showed distinct functional profiles with major brain networks, which were defined using independent component analysis, and exhibited functional lateralization. This study provided new insights into the understanding of the functional specialization of neonatal CC, and these findings may have significant clinical implications, especially in predicting neurological disorder.

Brainnetome atlas of preadolescent children based on anatomical connectivity profiles.

During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement.

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

The human mediodorsal thalamus: Organization, connectivity, and function.

The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition.

Structural and functional underpinnings of precentral abnormalities in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of both upper and lower motor neurons. Studies using various magnetic resonance imaging (MRI) analytical approaches have consistently identified significant precentral abnormalities in ALS, whereas their structural and functional underpinnings remain poorly understood. METHODS: Using cortical thickness, fractional anisotropy (FA), and effective connectivity, we performed a multimodal MRI study to examine the structural and functional alterations associated with precentral abnormalities in patients with ALS (n = 60) compared with healthy controls (n = 60). RESULTS: Cortical thickness analysis revealed significant cortical thinning in the right precentral gyrus (PCG), superior frontal gyrus, and superior temporal gyrus in patients with ALS. Tractwise white matter microstructure analyses revealed decreased FA in the tracts connected to the PCG cluster in patients with ALS involving the right corticospinal tract and the middle posterior body of the corpus callosum. Additionally, the cortical thickness of the PCG cluster was found to be positively correlated with FA of the tracts connected to the PCG cluster, suggesting that these two structural features are tightly coupled. Using spectral dynamic causal modelling, effective connectivity analysis among the three regions with cortical thinning revealed decreased self-inhibitory influence in the PCG cluster in patients with ALS, which might be an endophenotypic manifestation of an imbalance in inhibitory and excitatory neurotransmitters in this region. CONCLUSIONS: The present data shed new light on the structural and functional underpinnings of precentral abnormalities in ALS.

Concurrent brain structural and functional alterations in the thalamus of adult survivors of childhood brain tumors: a multimodal MRI study.

Adult survivors of childhood brain tumors often present with cognitive deficits that affect their quality of life. Studying brain structure and function in brain tumor survivors can help understand the underlying mechanisms of their cognitive deficits to improve long-term prognosis of these patients. This study analyzed voxel-based morphometry (VBM) derived from T1-weighted MRI and the amplitude of low-frequency fluctuation (ALFF) from resting-state functional magnetic resonance imaging (rs-fMRI) to examine the structural and functional alterations in 35 brain tumor survivors using 35 matching healthy individuals as controls. Compared with healthy controls, brain tumor survivors had decreased gray matter volumes (GMV) in the thalamus and increased GMV in the superior frontal gyrus. Functionally, brain tumor survivors had lower ALFF values in the inferior temporal gyrus and medial prefrontal area and higher ALFF values in the thalamus. Importantly, we found concurrent but negatively correlated structural and functional alterations in the thalamus based on observed significant differences in GMV and ALFF values. These findings on concurrent brain structural and functional alterations provide new insights towards a better understanding of the cognitive deficits in brain tumor survivors.

Multimodal Connectivity-based Individual Parcellation and Analysis for Humans and Rhesus Monkeys.

Individual brains vary greatly in morphology, connectivity and organization. Individualized brain parcellation is capable of precisely localizing subject-specific functional regions. However, most individualization approaches examined single modality of data and have not generalized to nonhuman primates. The present study proposed a novel multimodal connectivity-based individual parcellation (MCIP) method, which optimizes within-region homogeneity, spatial continuity and similarity to a reference atlas with the fusion of personal functional and anatomical connectivity. Comprehensive evaluation demonstrated that MCIP outperformed state-of-the-art multimodal individualization methods in terms of functional and anatomical homogeneity, predictability of cognitive measures, heritability, reproducibility and generalizability across species. Comparative investigation showed a higher topographic variability in humans than that in macaques. Therefore, MCIP provides improved accurate and reliable mapping of brain functional regions over existing methods at an individual level across species, and could facilitate comparative and translational neuroscience research.

Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates.

Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology.

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Fast and Adaptive Construction of Gyral Morphological Networks Based on Morphometric Features of Cortical Surface.

The organization of cortical folding patterns are related to brain function, cognition and behaviors. Due to the enormous complexity and high inter-subject variability in cortical morphology, it has been a challenging task to effectively and efficiently quantify the gyrification patterns of cerebral cortex. To tackle these issues, the gyral net approach used a graph-based representation of cortical architecture by segmenting the gyral crests from the cortical meshes based on its morphological metrics. However, current morphology-based approaches are very time-consuming and not applicable for large-scale dataset. In this study, we develop a fast and adaptive method to automatically construct the gyral morphological graph within 10 seconds. Our method is robust to low contrast conditions and more computationally efficient, approximately 5 times faster than classical approaches. We evaluated the proposed method on 1081 young adults acquired from the HCP dataset and uncovered significant differences among functional brain networks from the perspective of morphological networks.

BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

Connectional asymmetry of the inferior parietal lobule shapes hemispheric specialization in humans, chimpanzees, and rhesus macaques.

The inferior parietal lobule (IPL) is one of the most expanded cortical regions in humans relative to other primates. It is also among the most structurally and functionally asymmetric regions in the human cerebral cortex. Whether the structural and connectional asymmetries of IPL subdivisions differ across primate species and how this relates to functional asymmetries remain unclear. We identified IPL subregions that exhibited positive allometric in both hemispheres, scaling across rhesus macaque monkeys, chimpanzees, and humans. The patterns of IPL subregions asymmetry were similar in chimpanzees and humans, but no IPL asymmetries were evident in macaques. Among the comparative sample of primates, humans showed the most widespread asymmetric connections in the frontal, parietal, and temporal cortices, constituting leftward asymmetric networks that may provide an anatomical basis for language and tool use. Unique human asymmetric connectivity between the IPL and primary motor cortex might be related to handedness. These findings suggest that structural and connectional asymmetries may underlie hemispheric specialization of the human brain.

Fiber-specific white matter reductions in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of both upper and lower motor neurons. Studies using metrics derived from the diffusion tensor model have documented decreased fractional anisotropy (FA) and increased mean diffusivity in the corticospinal tract (CST) and the corpus callosum (CC) in ALS. These studies, however, only focused on microstructural white matter (WM) changes, while the macrostructural alterations of WM tracts in ALS remain unknown. Moreover, studies conducted based on the diffusion tensor model cannot provide information related to specific fiber bundles and fail to clarify which biological characteristics are changing. Using a novel fixel-based analytical method that can characterize the fiber density (FD) and the fiber-bundle cross-section (FC), this study investigated both microstructural and macrostructural changes in the WM in a large cohort of patients with ALS (N = 60) compared with demographically matched healthy controls (N = 60). Compared with healthy controls, we found decreased FD, FC and fiber density and cross-section (FDC, a combined measure of the FD and FC) values in the bilateral CST and the middle posterior body of the CC in patients with ALS, suggesting not only microstructural but also macrostructural abnormalities in these fiber bundles. Additionally, we found that the mean FD and FDC values in the bilateral CST were positively correlated with the revised ALS Functional Rating Scale, indicating that these two indices may serve as potential markers for assessing the clinical severity of ALS. Thus, these findings provide initial evidence for the existence of microstructural and macrostructural abnormalities of the fiber bundles in ALS.

Prefrontal dysconnectivity links to working memory deficit in first-episode schizophrenia.

Working memory (WM) deficit is a core feature of schizophrenia and is characterized by abnormal functional integration in the prefrontal cortex, including the dorsolateral prefrontal cortex (dLPFC), dorsal anterior cingulate cortex (dACC), and ventrolateral prefrontal cortex (vLPFC). However, the specific mechanism by which the abnormal neuronal circuits that involve these brain regions contribute to this deficit is still unclear. Therefore, this study focused on these regions and sought to answer which abnormal causal relationships in these regions can be linked to impaired WM in schizophrenia. We used spectral dynamic causal modeling to estimate directed (effective) connectivity between these regions based on resting-state functional magnetic resonance imaging data from healthy control (HC) subjects and patients with first-episode schizophrenia (FES). By comparing these effective connections in the controls and patients, we found that the effective connectivity from the dACC to the dLPFC and from the right dLPFC to the left vLPFC was weaker in the FES group than in the HC group. Furthermore, these effective connections displayed a positive correlation with WM performance in the HCs. However, in the FES patients, the effective connectivity from the dACC to the dLPFC was not correlated with WM performance, and the effective connectivity from the right dLPFC to the left vLPFC was negatively correlated with WM performance. These results could be explained by an aberrant top-down mechanism of WM processing and provide new evidence for the dysconnectivity hypothesis of schizophrenia.

Resting-State Coupling between Core Regions within the Central-Executive and Salience Networks Contributes to Working Memory Performance.

Previous studies investigated the distinct roles played by different cognitive regions and suggested that the patterns of connectivity of these regions are associated with working memory (WM). However, the specific causal mechanism through which the neuronal circuits that involve these brain regions contribute to WM is still unclear. Here, in a large sample of healthy young adults, we first identified the core WM regions by linking WM accuracy to resting-state functional connectivity with the bilateral dorsolateral prefrontal cortex (dLPFC; a principal region in the central-executive network, CEN). Then a spectral dynamic causal modeling (spDCM) analysis was performed to quantify the effective connectivity between these regions. Finally, the effective connectivity was correlated with WM accuracy to characterize the relationship between these connections and WM performance. We found that the functional connections between the bilateral dLPFC and the dorsal anterior cingulate cortex (dACC) and between the right dLPFC and the left orbital fronto-insular cortex (FIC) were correlated with WM accuracy. Furthermore, the effective connectivity from the dACC to the bilateral dLPFC and from the right dLPFC to the left FIC could predict individual differences in WM. Because the dACC and FIC are core regions of the salience network (SN), we inferred that the inter- and causal-connectivity between core regions within the CEN and SN is functionally relevant for WM performance. In summary, the current study identified the dLPFC-related resting-state effective connectivity underlying WM and suggests that individual differences in cognitive ability could be characterized by resting-state effective connectivity.