A Systematic Characterization of Structural Brain Changes in Schizophrenia

A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia,such as voxel-based morphometry(VBM),tensor-based morphometry(TBM),and projection-based thickness(PBT),is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia.However,such studies are still lacking.Here,we performed VBM,TBM,and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls.We found that,although all methods detected wide-spread structural changes,different methods captured different information-only 10.35%of the grey matter changes in cortex were detected by all three methods,and VBM only detected 11.36%of the white matter changes detected by TBM.Further,pattern classification between patients and controls revealed that combining different measures improved the classification accuracy(81.9%),indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Searching for effective biomarkers is one of the most challenging tasks in the research ？eld of Autism Spectrum Disorder（ASD）. Magnetic resonance imaging（MRI） provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation,connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and largescale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.

A Volumetric and Functional Connectivity MRI Study of Brain Arginine-Vasopressin Pathways in Autistic Children

Dysfunction of brain-derived arginine-vasopressin（AVP） systems may be involved in the etiology of autism spectrum disorder（ASD）. Certain regions such as the hypothalamus, amygdala, and hippocampus are known to contain either AVP neurons or terminals and may play an important role in regulating complex social behaviors.The present study was designed to investigate the concomitant changes in autistic behaviors, circulating AVP levels, and the structure and functional connectivity（FC） of speci？c brain regions in autistic children compared with typically developing children（TDC） aged from 3 to5 years. The results showed：（1） children with ASD had a signi？cantly increased volume in the left amygdala and left hippocampus, and a signi？cantly decreased volume in the bilateral hypothalamus compared to TDC, and these were positively correlated with plasma AVP level.（2） Autistic children had a negative FC between the left amygdala and the bilateral supramarginal gyri compared to TDC. The degree of the negative FC between amygdala and supramarginal gyrus was associated with a higher score on the clinical autism behavior checklist.（3） The degree of negative FC between left amygdala and left supramarginal gyrus was associated with a lowering of the circulating AVP concentration in boys with ASD.（4） Autistic children showed a higher FC between left hippocampus and right subcortical area compared to TDC.（5） The circulating AVP was negatively correlated with the visual and listening response score of the childhood autism rating scale.These results strongly suggest that changes in structure and FC in brain regions containing AVP may be involved in the etiology of autism.

Aberrant single-subject morphological brain networks in first-episode,treatment-naive adolescents with major depressive disorder

Background:Neuroimaging-based connectome studies have indicated that major depressive disorder(MDD)is associated with dis-rupted topological organization of large-scale brain networks.However,the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD.Objective:To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD.Methods:Twenty-five first-episode,treatment-naive adolescents with MDD and 19 healthy controls(HCs)underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests.Single-subject morphological brain networks were constructed separately based on cortical thickness,fractal dimension,gyrification index,and sulcus depth,and topologically characterized by graph-based approaches.Between-group differences were inferred by permutation testing.For significant alterations,partial correla-tions were used to examine their associations with clinical and neuropsychological variables in the patients.Finally,a support vector machine was used to classify the patients from controls.Results:Compared with the HCs,the patients exhibited topological alterations only in cortical thickness-based networks character-ized by higher nodal centralities in parietal(left primary sensory cortex)but lower nodal centralities in temporal(left parabelt complex,right perirhinal ectorhinal cortex,right area PHT and right ventral visual complex)regions.Moreover,decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients.These results were largely reproducible for binary and weighted network analyses.Finally,topological properties of the cortical thickness-based net-works were able to distinguish the MDD adolescents from HCs with 87.6%accuracy.Conclusion:Adolescent MDD is associated with disrupted topological organization of morphological brain networks,and the disrup-tions provide potential biomarkers for diagnosing and monitoring the disease.

Data-driven multimodal fusion:approaches and applications in psychiatric research

In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders.However,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data.This is where data-driven multi-modal fusion techniques come into play.By combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed.In this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component analysis.The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain disorders.After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical translation.Overall,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

Differential temporal neural responses of pain-related regions by acupuncture at acupoint ST36： a magnetoencephalography study

Background Previous neuroimaging studies primarily focused on the spatial distribution of acupuncture needling stimulation. However, a salient feature of acupuncture was its long-lasting effect. This study attempted to detect the spatial-temporal neural responses evoked by acupuncture at an analgesia acupoint ST36 by using magnetoencephalography. To further verify its functional specificity, we also adopted acupuncture at Pericardium 6 and nonacupoint as separated controls.Methods Forty-two college students, all right-handed and acupuncture naive, participated in this study. Every participant received only one acupoint stimulation, resulting in 14 subjects in one group. Both magnetoencephalography data （151-channel whole-head system） and structural functional magnetic resonance imaging data （3D sequence with a voxel size of 1 mm3 for anatomical localization） were collected for each subject. All processing procedures were performed in BrainStorm Toolbox.Results Acupuncture at ST36 showed a significantly time-varied brain activities with different onset time. Our results presented that acupuncture at different acupoints （or comparing with nonacupoint） can specifically induce neural responses in different brain areas-acupuncture at ST36 can specifically induce the neural responses of pain-inhibition areas, while acupuncture at PC6 can specifically induce the activities of the insula and amygdala.Conclusions In the present study, we attempted to detect the temporal neural responses underlying the functional specificity of acupuncture at ST36, using acupoint belonging to different meridians and non-acupoint with efficacy-irreverent as separate controls. The specific neural substrates involving acupuncture at different acupoints may be related to its functional specificity in clinical settings.

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment（a MCI） to Alzheimer＇s disease（AD）. As a part of the medial temporal lobe memory system,the hippocampus is one of the brain regions affected earliest by AD neuropathology,and shows progressive degeneration as a MCI progresses to AD. Currently,no validated biomarkers can precisely predict the conversion from a MCI to AD. Therefore,there is a great need of sensitive tools for the early detection of AD progression. In this review,we summarize the specifi c structural and functional changes in the hippocampus from recent a MCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile,this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of a MCI to AD.