Parcellation of the primary cerebral cortices based on local connectivity profiles

Connectivity-based parcellation using diffusion MRI has been extensively used to parcellate subcortical areas and the association cortex. Connectivity profiles are vital for connectivity-based parcellation. Two categories of connectivity profiles are generally utilized, including global connectivity profiles, in which the connectivity information is from the seed to the whole brain, and long connectivity profiles, in which the connectivity information is from the seed to other brain regions after excluding the seed. However, whether global or long connectivity profiles should be applied in parcellating the primary cortex utilizing connectivity-based parcellation is unclear. Many sources of evidence have indicated that the primary cerebral cortices are composed of structurally and functionally distinct subregions. Because the primary cerebral cortices are rich in local anatomic hierarchical connections and possess high degree of local functional connectivity profiles, we proposed that local connectivity profiles, that is the connectivity information within a seed region of interest, might be used for parcellating the primary cerebral cortices. In this study, the global, long, and local connectivity profiles were separately used to parcellate the bilateral M1, A1, S1, and V1. We found that results using the three profiles were all quite consistent with reported cytoarchitectonic evidence. More importantly, the results using local connectivity profiles showed less inter-subject variability than the results using the other two, a finding which suggests that local connectivity profiles are superior to global and long connectivity profiles for parcellating the primary cerebral cortices. This also implies that, depending on the characteristics of specific areas of the cerebral cortex, different connectivity profiles may need to be adopted to parcellate different areas

Digital twin brain: a bridge between biological intelligence and artificial intelligence

In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare

Common and Specific Functional Activity Features in Schizophrenia, Major Depressive Disorder, and Bipolar Disorder

Objectives: Schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD) are serious mental disorders with distinct diagnostic criteria. They share common clinical and biological features. However, there are still only few studies on the common and specific brain imaging changes associated with the three mental disorders. Therefore, the aim of this study was to identify the common and specific functional activity and connectivity changes in SZ, MDD, and BD.Methods: A total of 271 individuals underwent functional magnetic resonance imaging: SZ (n = 64), MDD (n = 73), BD (n = 41), and healthy controls (n = 93). The symptoms of SZ patients were evaluated by the Positive and Negative Syndrome Scale. The Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) were used to evaluate the symptoms of MDD patients. The BDI, BAI, and Young Mania Rating Scale were used to evaluate the symptoms of MDD and BD patients. In addition, we compared the fALFF and functional connectivity between the three mental disorders and healthy controls using two sample t-tests.Results: Significantly decreased functional activity was found in the right superior frontal gyrus, middle cingulate gyrus, left middle frontal gyrus, and decreased functional connectivity (FC) of the insula was found in SZ, MDD, and BD. Specific fALFF changes, mainly in the ventral lateral pre-frontal cortex, striatum, and thalamus were found for SZ, in the left motor cortex and parietal lobe for MDD, and the dorsal lateral pre-frontal cortex, orbitofrontal cortex, and posterior cingulate cortex in BD.Conclusion: Our findings of common abnormalities in SZ, MDD, and BD provide evidence that salience network abnormality may play a critical role in the pathogenesis of these three mental disorders. Meanwhile, our findings also indicate that specific alterations in SZ, MDD, and BD provide neuroimaging evidence for the differential diagnosis of the three mental disorders

Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia

Schizophrenia is hypothesized to arise from disrupted brain connectivity. This “dysconnectivity hypothesis” has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex (PFC) and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia

Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia

Schizophrenia is hypothesized to arise from disrupted brain connectivity. This "dysconnectivity hypothesis" has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex (PFC) and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia

Robust brain parcellation using sparse representation on resting-state fMRI.

Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation

Relationship between hearing loss and depression symptoms among older adults in China: The mediating role of social isolation and loneliness.

OBJECTIVES: To evaluate whether social isolation and loneliness mediates the relationship between hearing loss and depression symptoms in older adults in China. METHODS: A cross-sectional analysis was conducted of 3769 participants (aged≥60 years) in Shandong province of China. Hearing loss was assessed using Pure-Tone Audiometry test, depression symptoms using 15-item Geriatric Depression Scale, loneliness through UCLA Loneliness Scale and social isolation using Lubben Social Network Scale. Regression and bootstrap analyses were performed to test both direct associations of hearing loss and depression symptoms, and whether the mediating role of social isolation and loneliness. RESULTS: Overall, 44% of older adults had hearing loss, which was generally mild (30%) rather than moderate (10%), severe (3%) or profound (0.6%). Increasing levels of hearing loss was associated with increasing levels of social isolation and depressions. Hearing loss was also associated with loneliness, but here a threshold effect was apparent and no trend for increasing loneliness with increasing hearing loss. Models that included social isolation and loneliness showed an amelioration in the association of hearing loss and depression, although it remained significant at all levels of hearing loss. Overall, 8% of the total effect of hearing loss on depression symptoms was explained by the mediated effect through social isolation and 42% by loneliness. CONCLUSIONS: Psychosocial factors such as social isolation and loneliness might explain the association between hearing loss and depression. Interventions that address older adults' social isolation and loneliness may ameliorate depression in older adults with hearing loss