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Objectives: Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images. Methods: The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics. Results: Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods. Conclusion: Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET. Critical relevance statement: This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images. Key Points: The first transformer-based model was built to explore focal cortical dysplasia lesion segmentation. Integration of global and local features enhances the segmentation performance of lesions. A valuable benchmark for model development and comparative analyses was provided. [ABSTRACT FROM AUTHOR]

Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD. [ABSTRACT FROM AUTHOR]

Background: The thalamus has a central role in the pathophysiology of idiopathic cervical dystonia (iCD); however, the nature of alterations occurring within this structure remain largely elusive. Using a structural magnetic resonance imaging (MRI) approach, we examined whether abnormalities differ across thalamic subregions/nuclei in patients with iCD. Methods: Structural MRI data were collected from 37 patients with iCD and 37 healthy controls (HCs). Automatic parcellation of 25 thalamic nuclei in each hemisphere was performed based on the FreeSurfer program. Differences in thalamic nuclei volumes between groups and their relationships with clinical information were analysed in patients with iCD. Results: Compared to HCs, a significant reduction in thalamic nuclei volume primarily in central medial, centromedian, lateral geniculate, medial geniculate, medial ventral, paracentral, parafascicular, paratenial, and ventromedial nuclei was found in patients with iCD (P < 0.05, false discovery rate corrected). However, no statistically significant correlations were observed between altered thalamic nuclei volumes and clinical characteristics in iCD group. Conclusion: This study highlights the neurobiological mechanisms of iCD related to thalamic volume changes. [ABSTRACT FROM AUTHOR]

Background: Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). Objective: This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. Methods: The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Results: Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Conclusion: Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability. [ABSTRACT FROM AUTHOR]

Background: Although many previous studies reported structural plasticity of the hippocampus and amygdala induced by electroconvulsive therapy (ECT) in major depressive disorder (MDD), yet the exact roles of both areas for antidepressant effects are still controversial. Methods: In the current study, segmentation of amygdala and hippocampal sub-regions was used to investigate the longitudinal changes of volume, the relationship between volume and antidepressant effects, and prediction performances for ECT in MDD patients before and after ECT using two independent datasets. Results: As a result, MDD patients showed selectively and consistently increased volume in the left lateral nucleus, right accessory basal nucleus, bilateral basal nucleus, bilateral corticoamygdaloid transition (CAT), bilateral paralaminar nucleus of the amygdala, and bilateral hippocampus-amygdala transition area (HATA) after ECT in both datasets, whereas marginally significant increase of volume in bilateral granule cell molecular layer of the head of dentate gyrus, the bilateral head of cornu ammonis (CA) 4, and left head of CA 3. Correlation analyses revealed that increased volume of left HATA was significantly associated with antidepressant effects after ECT. Moreover, volumes of HATA in the MDD patients before ECT could be served as potential biomarkers to predict ECT remission with the highest accuracy of 86.95% and 82.92% in two datasets (The predictive models were trained on Dataset 2 and the sensitivity, specificity and accuracy of Dataset 2 were obtained from leave-one-out-cross-validation. Thus, they were not independent and very likely to be inflated). Conclusions: These results not only suggested that ECT could selectively induce structural plasticity of the amygdala and hippocampal sub-regions associated with antidepressant effects of ECT in MDD patients, but also provided potential biomarkers (especially HATA) for effectively and timely interventions for ECT in clinical applications. [ABSTRACT FROM AUTHOR]

Blepharospasm is traditionally thought to be a movement disorder that results from basal ganglia dysfunction. Recently, accumulating morphometric studies have revealed structural alterations outside the basal ganglia, such as in the brainstem, cerebellum and sensorimotor cortex, suggesting that blepharospasm may result from network disorders. However, the temporal and causal relationships between structural alterations and whether there are disease duration-related hierarchical structural changes in these patients remain largely unknown. Structural MRI was performed in 62 patients with blepharospasm, 62 patients with hemifacial spasm and 62 healthy controls to assess the structural alterations using voxel-based morphology and structural covariance networks. The use of the causal structural covariance network, modularity analysis and functional decoding were subsequently performed to map the causal effect of grey matter change pattern, hierarchical topography and functional characterizations of the structural network throughout the disease duration of blepharospasm. Greater grey matter volume in the left and right supplementary motor areas was identified in patients with blepharospasm compared to that in patients with hemifacial spasm and healthy controls, whereas no significant difference was identified between patients with hemifacial spasm and healthy controls. In addition, increased grey matter volume covariance between the right supplementary motor area and right brainstem, left superior frontal gyrus, left supplementary motor area and left paracentral gyrus was found in patients with blepharospasm compared to healthy controls. Further causal structural covariance network, modularity analysis and functional decoding showed that the right supplementary motor area served as a driving core in patients with blepharospasm, extending greater grey matter volume to areas in the cortico-basal ganglia–brainstem motor pathway and cortical regions in the vision–motor integration pathway. Taken together, our results suggest that the right supplementary motor area is an early and important pathologically impaired region in patients with blepharospasm. With a longer duration of blepharospasm, increased grey matter volume extends from the right supplementary motor area to the cortico-basal ganglia motor and visual–motor integration pathways, showing a hierarchy of structural abnormalities in the disease progression of blepharospasm, which provides novel evidence to support the notion that blepharospasm may arise from network disorders and is associated with a wide range of grey matter abnormalities. [ABSTRACT FROM AUTHOR]

Background: Although acupuncture is widely used to improve cognitive and memory in the amnesic mild cognitive impairment (aMCI) patients with impressive effectiveness, its neural mechanism remains largely unclear.Objective: We aimed to explore functional magnetic resonance imaging (fMRI) mechanism of acupuncture for aMCI.Methods: A randomized, controlled, single-blind research was performed. A total of 46 aMCI patients were randomly assigned into verum and sham acupuncture group, who received a total of 24 times treatments (3 times/week, 8 weeks). Clinical evaluation and fMRI scanning were performed at baseline and after treatment for all aMCI patients. The interaction effects and inter-group effects of regional homogeneity (ReHo) were performed using mixed effect models, and the correlations between clinical improvement and neuroimaging changes before and after verum acupuncture treatment were analyzed using Pearson correlations.Results: As a result, interaction effects showed increased ReHo value in left dorsal lateral prefrontal cortex (DLPFC), increased functional connectivity between left DLPFC and left precuneus, and decreased functional connectivity between left DLPFC and left inferior temporal gyrus after verum acupuncture but inversely after sham acupuncture in the aMCI. Condition effects showed increased ReHo in right lingual gyrus, and bilateral post-central gyrus after verum and sham acupuncture in the aMCI. In addition, the changed Montreal Cognitive Assessment scores in verum acupuncture group were significantly correlated with changed ReHo values in left DLPFC.Conclusion: Together, our findings further confirmed that acupuncture could be used as a promising complementary therapy for aMCI by modulating function of left DLPFC to improve cognitive symptoms. [ABSTRACT FROM AUTHOR]

Although previous studies identified a similar topography pattern of structural and functional delineations in human middle temporal gyrus (MTG) using healthy adults, trajectories of MTG sub-regions across lifespan remain largely unknown. Herein, we examined gray matter volume (GMV) and resting-state functional connectivity (RSFC) using datasets from the Nathan Kline Institute (NKI), and aimed to (1) investigate structural and functional trajectories of MTG sub-regions across the lifespan; and (2) assess whether these features can be used as biomarkers to predict individual's chronological age. As a result, GMV of all MTG sub-regions followed U-shaped trajectories with extreme age around the sixth decade. The RSFC between MTG sub-regions and many cortical brain regions showed inversed U-shaped trajectories, whereas RSFC between MTG sub-regions and sub-cortical regions/cerebellum showed U-shaped way, with extreme age about 20 years earlier than those of GMV. Moreover, GMV and RSFC of MTG sub-regions could be served as useful features to predict individual age with high estimation accuracy. Together, these results not only provided novel insights into the dynamic process of structural and functional roles of MTG sub-regions across the lifespan, but also served as useful biomarkers to age prediction. [ABSTRACT FROM AUTHOR]

Background: Advanced structural analyses are increasingly being highly valued to uncover pathophysiological understanding of anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis. Therefore, we aimed to explore whether and how antibody-mediated NMDAR dysfunction affected cortical and sub-cortical brain morphology and their relationship with clinical symptoms. Methods: We performed surface-based morphometry analyses, hippocampal segmentation, and correlational analyses in 24 patients with anti-NMDAR encephalitis after acute disease stage and 30 normal controls (NC) in this case–control study. Results: Patients showed significantly decreased cortical alterations mainly in language network (LN) and default mode network (DMN), as well as decreased gray matter volume in left cornu ammonis 1 (CA1) body of hippocampus. Further correlation analyses showed that the decreased cortical thickness in the right superior frontier gyrus was associated with decreased cognitive scores, the decreased cortical volume in the right pars triangulari and decreased surface area in the right pars operculari were associated with decreased memory scores, whereas decreased gray matter volume in the left CA1 body was significantly correlated with longer time between first symptom and imaging in the patients. Conclusion: These results suggested that cognitive impairments resulted from long-term sequelae of the encephalitis were mainly associated with cortical alterations in LN and DMN and sub-cortical atrophy of left CA1 body, which can be served as effective features to assess disease progression in clinical routine examination. [ABSTRACT FROM AUTHOR]

Accumulating studies had been performed using magnetic resonance imaging (MRI) to understand the neural mechanism of acupuncture therapy for depression. However, inconsistencies remain due to differences in research designs and MRI analytical methods. Therefore, we aim to summarize the current MRI research and provide useful information for further research by identifying papers published in English and Chinese about MRI studies on acupuncture for depression up to November 2020. A total of 22 studies met the inclusion criteria, including 810 depression patients and 416 health controls (HCs). The applied designs of these studies are mainly random control trial and pre–post designs. The MRI analytical methods are mainly (fractional) amplitude of low-frequency fluctuation (fALFF/ALFF) and functional connectivity (FC), whereas a small subset of studies used voxel-based morphometry (VBM) and diffusion tensor imaging (DTI). The most consistent functional MRI (fMRI) results showed increased N -acetylaspartate/creatine (NAA/Cr) ratios, increased ALFF in the right precuneus, decreased ALFF in the inferior frontal gyrus (IFG), and increased FC of the anterior cingulate cortex (ACC). In contrast, no significant neurological changes were identified in any of the DTI or VBM studies. However, clear, reliable conclusions cannot be drawn due to the use of different designs, analytical methods, seed points selected, types of depression, acupuncture points, and so on. Improved report specifications, well-designed studies, consistent analytical methods, and larger sample sizes will enable the field to better elucidate the underlying mechanisms of acupuncture in depressed patients. [ABSTRACT FROM AUTHOR]

The neural mechanisms of acupuncture are not well-understood. Over the past decades, an increasing number of studies have used MRI to investigate the response of the brain to acupuncture. The current review aims to provide an update on acupuncture therapy in disease. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched from inception to January 31, 2021. Article selection and data extraction were conducted by two review authors. A total of 107 publications about MRI in acupuncture were included, the collective findings of which were as follows: (1) stroke and GB34 (Yanglingquan) are the most studied disease and acupoint. Related studies suggested that the mechanism of acupuncture treatment for stroke may associate with structural and functional plasticity, left and right hemispheres balance, and activation of brain areas related to movement and cognition. GB34 is mainly used in stroke and Parkinson's disease, which mainly activates brain response in the premotor cortex, the supplementary motor area, and the supramarginal gyrus; (2) resting-state functional MRI (rs-fMRI) and functional connectivity (FC) analysis are the most frequently used approaches; (3) estimates of efficacy and brain response to acupuncture depend on the type of sham acupuncture (SA) used for comparison. Brain processing after acupuncture differs between patients and health controls (HC) and occurs mainly in disorder-related areas. Factors that influence the effect of acupuncture include depth of needling, number and locations of acupoints, and deqi and expectation effect, each contributing to the brain response. While studies using MRI have increased understanding of the mechanism underlying the effects of acupuncture, there is scope for development in this field. Due to the small sample sizes, heterogeneous study designs, and analytical methods, the results were inconsistent. Further studies with larger sample sizes, careful experimental design, multimodal neuroimaging techniques, and standardized methods should be conducted to better explain the efficacy and specificity of acupuncture, and to prepare for accurate efficacy prediction in the future. [ABSTRACT FROM AUTHOR]

Although numerous studies have revealed the structural and functional alterations in major depressive disorder (MDD) using unimodal diffusion magnetic resonance imaging (MRI) or functional MRI, however, the potential associations between changed microstructure and corresponding functional activities in the MDD has been largely uninvestigated. Herein, 27 medication-free MDD patients and 54 gender-, age-, and educational level-matched healthy controls (HC) were used to investigate the concurrent alterations of white matter microstructure and functional activities using tract-based spatial statistics (TBSS) analyses, fractional amplitude of low-frequency fluctuation (fALFF), and degree centrality (DC). The TBSS analyses revealed significantly decreased fractional anisotropy (FA) in the superior longitudinal fasciculus (SLF I) in the MDD patients compared to HC. Correlation analyses showed that decreased FA in the SLF I was significantly correlated with fALFF in left pre/postcentral gyrus and binary, weighted DC in right posterior cerebellum. Moreover, the fALFF in left pre/postcentral gyrus significantly reduced in MDD patients while binary and weighted DC in right posterior cerebellum significantly increased in MDD patients. Our results revealed concurrent structural and functional changes in MDD patients suggesting that the underlying structural disruptions are an important indicator of functional abnormalities. [ABSTRACT FROM AUTHOR]

In addition to cognitive impairments, depression symptoms were reported in subcortical vascular mild cognitive impairment. Although hippocampal alterations were associated with cognitive decline in subcortical vascular mild cognitive impairment, the neural mechanism underlying depression symptoms remains unclear. Thus, a cohort of 18 patients with depression symptoms, 17 patients without depression symptoms, and 23 normal controls was used. Functionally, significantly altered resting-state functional connectivity between hippocampal emotional sub-region and right posterior cingulate cortex, between hippocampal cognitive sub-region and right inferior parietal gyrus and between hippocampal perceptual sub-region and left inferior temporal gyrus were identified among three groups. Structurally, significantly altered structural associations between hippocampal emotional sub-region and 6 frontal regions/right pole part of superior temporal gyrus/right inferior occipital gyrus, between hippocampal cognitive sub-region and right orbital part of inferior frontal gyrus /right anterior cingulate cortex, and between hippocampal perceptual and right orbital part of inferior frontal gyrus / left inferior temporal gyrus / left thalamus were identified among the three groups. Further analyses also showed correlations between functional connectivity and depression symptoms and/or cognitive impairments of patients. Together, these results showed different patterns of functional and structural alterations of the hippocampal sub-regions in the subcortical vascular mild cognitive impairment with and without depression, which might be specially associated with the depression symptoms and cognitive impairments in these patients. [ABSTRACT FROM AUTHOR]

Background: Voxel-based morphometry (VBM) has been widely used to investigate structural alterations in amnesia mild cognitive impairment (aMCI). However, inconsistent results have hindered our understanding of the exact neuropathology related to aMCI. Objectives: Our aim was to systematically review the literature reporting VBM on aMCI to elucidate consistent gray matter alterations, their functional characterization, and corresponding co-activation patterns. Methods: The PubMed, Web of Science, and EMBASE databases were searched for VBM studies on aMCI published from inception up to June 2020. Peak coordinates were extracted from clusters that showed significant gray matter differences between aMCI patients and healthy controls (HC). Meta-analysis was performed using seed-based d mapping with the permutation of subject images (SDM-PSI), a newly improved meta-analytic method. Functional characterization and task-based co-activation patterns using the BrainMap database were performed on significant clusters to explore their functional roles. Finally, VBM was performed based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to further support the findings. Results: A total of 31 studies with 681 aMCI patients and 837 HC were included in this systematic review. The aMCI group showed significant gray matter atrophy in the left amygdala and right hippocampus, which was consistent with results from the ADNI dataset. Functional characterization revealed that these regions were mainly associated with emotion, cognition, and perception. Further, meta-regression analysis demonstrated that gray matter atrophy in the left inferior frontal gyrus and the left angular gyrus was significantly associated with cognitive impairment in the aMCI group. Conclusions: The findings of gray matter atrophy in the left amygdala and right hippocampus are highly consistent and robust, and not only offer a better understanding of the underlying neuropathology but also provide accurate potential biomarkers for aMCI. [ABSTRACT FROM AUTHOR]

Objectives: To summarize development processes and research hotspots of MRI research on acupuncture and to provide new insights for researchers in future studies. Methods: Publications regarding MRI on acupuncture from inception to 2020 were downloaded from the Web of Science Core Collection. VOSviewer 1.6.15 and CiteSpace V software were used for bibliometric analyses. The main analyses include collaboration analyses between countries/institutions/authors, co-occurrence analysis between keywords, as well as analyses on keyword bursts, citation references, and clusters of references. Results: A total of 829 papers were obtained with a continually increased trend over time. The most productive country and institution in this field were the People's Republic of China (475) and KyungHee University (70), respectively. Evidence-based Complementary and Alternative Medicine (83) was the most productive journal, and Neuroimage (454) was the most co-cited journal. Dhond's et al. (2008) article (co-citation counts: 58) and Napadow's et al. (2005) article (centrality: 0.21) were the most representative and symbolic references, with the highest co-citation number and centrality, respectively. Jie Tian had the highest number of publications (35) and Kathleen K S Hui was the most influential author (280 co-citations). The four hot topics in MRI on acupuncture were acupuncture, fMRI, pain, and stimulation. The three frontier topics were connectivity, modulation, and fMRI. Based on the clustering of co-cited documents, chronic low back pain, sham electro-acupuncture treatment, and clinical research were the main research directions. Conclusion: This study provides an in-depth perspective for MRI research on acupuncture and provides researchers with valuable information to determine the current status, hot spots, and frontier trends of MRI research on acupuncture. [ABSTRACT FROM AUTHOR]

Traditionally, the visual motion area (MT) is considered as a brain region specialized for visual motion perception. However, accumulating evidence showed that MT is also related to various functions, suggesting that it is a complex functional area and different functional subregions might exist in this area. To delineate functional subregions of this area, left and right masks of MT were defined using meta-analysis in the BrainMap database, and coactivation-based parcellation was then performed on these two masks. Two dorsal subregions (Cl1 and Cl2) and one ventral subregion (Cl3) of left MT, as well as two dorsal-anterior subregions (Cl1 and Cl2), one ventral-anterior subregion (Cl3), and an additional posterior subregion (Cl4) of right MT were identified. In addition to vision motion, distinct and specific functions were identified in different subregions characterized by task-dependent functional connectivity mapping and forward/reverse inference on associated functions. These results not only were in accordance with the previous findings of a hemispheric asymmetry of MT, but also strongly confirmed the existence of subregions in this region with distinct and specific functions. Furthermore, our results extend the special role of visual motion perception on this area and might facilitate future cognitive study. [ABSTRACT FROM AUTHOR]

As one of the key regions in the "social brain" network, the middle temporal gyrus (MTG) has been widely reported to be associated with autism spectrum disorder (ASD), but there have been contradictory results in terms of whether it shows hyperconnectivity or hypoconnectivity. Delineating roles of MTG at the subregional level may eliminate the observed inconsistencies and provide a new avenue to reveal the neurophysiologic mechanism of ASD. Thus, we first performed connectivity‐based parcellation using the BrainMap database to identify fine‐grained functional topography of the MTG. Then, the MTG subregions were used to investigate differences in the functional connectivity in children and adults with ASD using two data sets from Autism Brain Imaging Data Exchange database. Four distinct subregions in the human left and right MTG were identified, including the anterior MTG (aMTG), middle‐anterior MTG (maMTG), middle‐posterior MTG, and posterior MTG (pMTG). The bilateral pMTG was more vulnerable in both children and adults with ASD than in the typically developing (TD) group, mainly showing hypoconnectivity with different brain regions. In addition, the bilateral aMTG and right maMTG also showed altered functional connectivity in adults with ASD compared to the TD group. Moreover, all these altered MTG subregions were mainly associated with social cognition and language, as revealed by functional characterization. Further correlation analyses also showed trends of association between altered connectivity of the left aMTG and the Autism Diagnostic Observation Schedule scores in adults with ASD. Together, these results suggest a potential objective way to explore sub‐regional differences associated with such disorders. Autism Res 2020, 13: 410–422. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: Four distinct subregions in the human left and right middle temporal gyrus (MTG) were identified, including the anterior MTG (aMTG), middle‐anterior MTG (maMTG), middle‐posterior MTG, and posterior MTG (pMTG). The bilateral pMTG was more vulnerable in both children and adults with autism spectrum disorder (ASD) than in the typically developing (TD) group, mainly showing hypoconnectivity with different brain regions. In addition, the bilateral aMTG and right maMTG also showed altered functional connectivity in adults with ASD compared to the TD group. Moreover, all these altered MTG subregions were mainly associated with social cognition and language, as revealed by functional characterization. Further correlation analyses also showed trends of association between altered connectivity of the left aMTG and the Autism Diagnostic Observation Schedule scores in adults with ASD. [ABSTRACT FROM AUTHOR]

Background: Depressive symptoms were thought to increase the risk of vascular dementia. Previous studies reported widespread white matter damages in the subcortical vascular mild cognitive impairment (svMCI), but little is known about the mechanism of depressive symptoms in svMCI.Objective: In the current study, we aim to explore the white matter microstructural alterations in svMCI with depressive symptoms, and their associations with clinical measurements.Methods: Fifty-eight subjects including 18 svMCI with depression (svMCI+D), 17 svMCI without depression (svMCI-D), and 23 normal controls (NC) were included in the study. Voxel-based analyses were performed on fractional anisotropy (FA) and mean diffusivity (MD).Results: Compared to NC, both svMCI groups showed decreased FA in the bilateral insula and the left precentral gyrus, and increased MD in the cerebellum. Compared to svMCI-D, svMCI+D showed increased FA in left precentral gyrus. Moreover, svMCI+D showed significant correlation between the increased MD in the cerebellum and the Hamilton Depression Rating Scale (HAMD) scores.Conclusion: Our findings of white matter alterations might be associated with executive function and memory performance in the svMCI patients. Moreover, the structural alterations in the cerebellum might underlie the mechanism of depressive symptoms in svMCI patients. [ABSTRACT FROM AUTHOR]

Although the middle temporal gyrus (MTG) has been parcellated into subregions with distinguished anatomical connectivity patterns, whether the structural topography of MTG can inform functional segregations of this area remains largely unknown. Accumulating evidence suggests that the brain's underlying organization and function can be directly and effectively delineated with resting‐state functional connectivity (RSFC) by identifying putative functional boundaries between cortical areas. Here, RSFC profiles were used to explore functional segregations of the MTG and defined four subregions from anterior to posterior in two independent datasets, which showed a similar pattern with MTG parcellation scheme obtained using anatomical connectivity. The functional segregations of MTG were further supported by whole brain RSFC, coactivation, and specific RFSC, and coactivation mapping. Furthermore, the fingerprint with predefined 10 networks and functional characterizations of each subregion using meta‐analysis also identified functional distinction between subregions. The specific connectivity analysis and functional characterization indicated that the bilateral most anterior subregions mainly participated in social cognition and semantic processing; the ventral middle subregions were involved in social cognition in left hemisphere and auditory processing in right hemisphere; the bilateral ventro‐posterior subregions participated in action observation, whereas the left subregion was also involved in semantic processing; both of the dorsal subregions in superior temporal sulcus were involved in language, social cognition, and auditory processing. Taken together, our findings demonstrated MTG sharing similar structural and functional topographies and provide more detailed information about the functional organization of the MTG, which may facilitate future clinical and cognitive research on this area. [ABSTRACT FROM AUTHOR]

Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder (MDD), the mechanism underlying the therapeutic efficacy and side effects of ECT remains poorly understood. Here, we investigated alterations in the cortical morphological measurements including cortical thickness (CT), surface area (SA), and local gyrification index (LGI) in 23 MDD patients before and after ECT. Furthermore, multivariate pattern analysis using linear support vector machine (SVM) was applied to investigate whether the changed morphological measurements can be effective indicators for therapeutic efficacy of ECT. Surface-based morphometry (SBM) analysis found significantly increased vertex-wise and regional cortical thickness (CT) and surface area (SA) in widespread regions, mainly located in the left insula (INS) and left fusiform gyrus, as well as hypergyrification in the left middle temporal gyrus (MTG) in MDD patients after ECT. Partial correlational analyses identified associations between the morphological properties and depressive symptom scores and impaired memory scores. Moreover, SVM result showed that the changed morphological measurements were effective to classify the MDD patients before and after ECT. Our findings suggested that ECT may enhance cortical neuroplasticity to facilitate neurogenesis to remit depressive symptoms and to impair delayed memory. These findings indicated that the cortical morphometry is a good index for therapeutic efficacy of ECT. [ABSTRACT FROM AUTHOR]

Susceptibility-weighted images (SWIs) have recently been confirmed to be more sensitive to acute ischemic stroke than diffusion weighted images in the form of the presence of small veins shortly after the symptom onset. Accurate segmentation of small veins in SWIs is critical for quantitative diagnosis, individual therapy and outcome prediction of acute ischemic stroke. It is challenging to segment veins in SWIs as they exhibit substantial variability and intensity inhomogeneity within a patient and among patients, which may even be hard for experts to delineate manually. A deep convolutional neural network is proposed to segment veins in SWIs with two main contributions: dense connection to concatenate feature maps from preceding layers to enhance network performance, and a hybrid loss function comprising of classification accuracy and global region overlap terms to handle class imbalance. Experiments have been conducted on 10 consecutive patients with acute ischemic stroke using leave-one-out validation, yielding the best Dice coefficient (0.756 ± 0.043) (p < 0.001) as compared with 3 relevant methods. The proposed method could provide a potential tool to quantify veins in SWIs with accuracy to assist decision making especially for thrombolytic therapy. [ABSTRACT FROM AUTHOR]

Although evidence from studies on blind adults indicates that visual deprivation early in life leads to structural and functional disruption and reorganization of the brain, whether young blind people show similar patterns remains unknown. Therefore, this study is aimed at exploring the structural and functional alterations of the brain of early-blind adolescents (EBAs) compared to normal-sighted controls (NSCs) and investigating the effects of residual light perception on brain microstructure and function in EBAs. We obtained magnetic resonance imaging (MRI) data from 23 EBAs (8 with residual light perception (LPs), 15 without light perception (NLPs)) and 21 NSCs (age range 11-19 years old). Whole-brain voxel-based analyses of diffusion tensor imaging metrics and region-of-interest analyses of resting-state functional connectivity (RSFC) were performed to compare patterns of brain microstructure and the corresponding RSFC between the groups. The results showed that structural disruptions of LPs and NLPs were mainly located in the occipital visual pathway. Compared with NLPs, LPs showed increased fractional anisotropy (FA) in the superior frontal gyrus and reduced diffusivity in the caudate nucleus. Moreover, the correlations between FA of the occipital cortices or mean diffusivity of the lingual gyrus and age were consistent with the development trajectory of the brain in NSCs, but inconsistent or even opposite in EBAs. Additionally, we found functional, but not structural, reorganization in NLPs compared with NSCs, suggesting that functional neuroplasticity occurs earlier than structural neuroplasticity in EBAs. Altogether, these findings provided new insights into the mechanisms underlying the neural reorganization of the brain in adolescents with early visual deprivation. [ABSTRACT FROM AUTHOR]

Some studies identified hypo-connectivity, while others showed hyper-connectivity of the insula in the autism spectrum disorders (ASD). These contradictory findings leave open the question of whether and to what extent functional connectivity of the insula is altered and how functional connectivity of the insula is associated with the severity of ASD. A newly emerging insular atlas that comprises multiple functionally differentiated subregions provides a new framework to interpret the functional significance of insular findings and uncover the mechanisms underlying the severity of ASD. Using the new insular atlas, the present study aimed to investigate the distinct functional connectivity of the insular subregions and their associations with ASD severity in a cohort of 49 children with ASD and 33 typically developing (TD) subjects. We found that compared with TD group, the ASD group showed different connectivity patterns in the left ventral agranular insula, right ventral dysgranular and granular insula, and dorsal dysgranular insula, characterized by significant hyper-connectivity and/or hypo-connectivity with special brain regions. Furthermore, both the hypo-connectivity and hyper-connectivity patterns of the insular subregions were significantly associated with the severity of ASD symptoms. Our research demonstrated distinct functional connectivity patterns of the insular subregions and emphasized the importance of the subdivisions within the insula to the potential impact of functional difference in children with ASD. Moreover, these results might help us to better understand the mechanisms underlying the symptoms in children with ASD and might elucidate potential biomarkers for clinical applications. [ABSTRACT FROM AUTHOR]

Purpose: To compare the accuracy of a Kinect-Optical navigation system with an electromagnetic (EM) navigation system for percutaneous liver needle intervention. Materials and methods: Five beagles with nine artificial tumors were used for validation. The Veran IG4 EM navigation system and a custom-made Kinect-Optical navigation system were used. Needle insertions into each tumor were conducted with these two guidance methods. The target positioning error (TPE) and the time cost of the puncture procedures were evaluated. Results: A total of 18 needle insertions were performed to evaluate the navigation accuracy of the two guidance approaches. The targeting error was 6.78 ± 3.22 mm and 8.72 ± 3.5 mm for the Kinect-Optical navigation system and the EM navigation system, respectively. There is no statistically significant difference in the TPE between the Kinect-Optical navigation system and the EM navigation system ( p = 0.229). The processing time with the Kinect-Optical system (10 min) is similar to that of the Veran IG4 system (12 min). Conclusions: The accuracy of the Kinect-Optical navigation system is comparable to that of the EM navigation system. [ABSTRACT FROM AUTHOR]

Background: To investigate the anterior lens capsule and epithelium thickness (defined as anterior lens capsular complex: ALCC) in normal Chinese subjects using spectral-domian optical coherence tomography (SD-OCT) and examine the factors that may influence the ALCC, such as age, gender, pupil diameter (PD) and signal strength index (SSI).Methods: A prospective observational case series. One-hundred-thirty-four normal subjects (134 eyes) were included. The ALCCs were determined manually via SD-OCT. Using the pupil center as a reference position, the central ALCC (CALCC), nasal 1-mm ALCC (NALCC), temporal 1-mm ALCC (TALCC) and PD were measured manually.Results: The mean CALCC, NALCC and TALCC were 33 ± 6 μm, 36 ± 7 μm and 34 ± 6 μm, respectively. The NALCC was significantly thicker than the CALCC (P < .001) and TALCC (P < .001). Moreover, CALCC was significantly thinner than TALCC (P = 0.013). Age was positively correlated with the CALCC (r = 0.292, P < .001), NALCC (r = 0.400, P < .001) and TALCC (r = 0.521, P < .001). PD, gender and SSI were not significantly correlated with the three ALCC parameters.Conclusions: The SD-OCT can be used to demonstrate the ALCC thickness, and age is positively correlated with the ALCC in the central, nasal and temporal sides. [ABSTRACT FROM AUTHOR]

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods--3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration--are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-theart automatic methods based on ASM. [ABSTRACT FROM AUTHOR]

Cerebral glioma is one of the most aggressive space-occupying diseases, which will exhibit midline shift (MLS) due to mass effect. MLS has been used as an important feature for evaluating the pathological severity and patients' survival possibility. Automatic quantification of MLS is challenging due to deformation, complex shape and complex grayscale distribution. An automatic method is proposed and validated to estimate MLS in patients with gliomas diagnosed using magnetic resonance imaging (MRI). The deformed midline is approximated by combining mechanical model and local symmetry. An enhanced Voigt model which takes into account the size and spatial information of lesion is devised to predict the deformed midline. A composite local symmetry combining local intensity symmetry and local intensity gradient symmetry is proposed to refine the predicted midline within a local window whose size is determined according to the pinhole camera model. To enhance the MLS accuracy, the axial slice with maximum MSL from each volumetric data has been interpolated from a spatial resolution of 1 mm to 0.33 mm. The proposed method has been validated on 30 publicly available clinical head MRI scans presenting with MLS. It delineates the deformed midline with maximum MLS and yields a mean difference of 0.61 ± 0.27 mm, and average maximum difference of 1.89 ± 1.18 mm from the ground truth. Experiments show that the proposed method will yield better accuracy with the geometric center of pathology being the geometric center of tumor and the pathological region being the whole lesion. It has also been shown that the proposed composite local symmetry achieves significantly higher accuracy than the traditional local intensity symmetry and the local intensity gradient symmetry. To the best of our knowledge, for delineation of deformed midline, this is the first report on both quantification of gliomas and from MRI, which hopefully will provide valuable information for diagnosis and therapy. The study suggests that the size of the whole lesion and the location of tumor (instead of edema or the sum of edema and tumor) are more appropriate to determine the extent of deformation. Composite local symmetry is recommended to represent the local symmetry around the deformed midline. The proposed method could be potentially used to quantify the severity of patients with cerebral gliomas and other brain pathology, as well as to approximate midsagittal surface for brain quantification. [ABSTRACT FROM AUTHOR]

Percutaneous coronary intervention (PCI) is a minimally invasive surgery procedure that is widely used in the treatment of coronary artery disease. This procedure requires interventional cardiologists to have high proficiency and therefore demands an extensive training period in order to ensure successful surgical outcome. In this paper, a realistic and real-time interactive simulator for training PCI procedure is presented. A set of new approaches for core simulation components is devised and integrated into the simulator. Trainees can interact with the virtual simulation environment with real instruments and essential maneuvers encountered in real PCI procedure. Although presently targeted at PCI, our simulator could be easily extended to mimic the necessities of any vascular interventional radiology procedures by updating vascular anatomy. Preliminary validation of the proposed physical model of instruments is conducted on vascular phantom to demonstrate its performance and effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: Hemorrhage within the brain space (HWBS) involves the brain parenchyma and ventricle systems, and is associated with high morbidity and mortality. Computed tomography (CT) head scans are the recommended modality for diagnosis and treatment for HWBS. However, HWBS detection may be difficult when the hemorrhage is inconspicuous, while quantification is hard as hemorrhage can have very variable intensity that overlaps with normal brain tissue. An algorithm is proposed to detect and quantify HWBS. Methods: Adaptive thresholding and case-based reasoning (CBR) were applied to HWBS in four steps: preprocessing to extract the brain, adaptive thresholding based on local contrast with varied window sizes to derive candidate HWBS regions, case representation to represent each candidate HWBS region by parameters on context as well as intensity and geometrical characteristics, and classification of HWBS by taking each candidate HWBS region as a case and applying CBR. Additionally, case base indexing and weights optimization were used to increase retrieval speed and improve performances. Refinement of each recognized HWBS was performed for quantifying HWBS. Results: Validation on 426 clinical CT data indicates that the proposed algorithm achieved a detection rate of 94.4 % and recall of 79.2 % for detecting HWBS regions. Visually, the HWBS regions calculated from adaptive thresholding plus refinement agreed well with expert delineation. For 10 representative data with small to large hemorrhage, the algorithm quantitatively yielded a segmentation accuracy of $$0.950\pm 0.015$$ . Case base indexing increased the retrieval speed by 41.1 times at the expense of decreasing detection rate of 0.5 % and recall of 2.6 %. Genetic algorithm optimization enhanced the detection rate and recall to, respectively, 94.9 and 83.5 %. Conclusions: We developed and tested an algorithm that combined adaptive thresholding and CBR for detecting and quantifying HWBS. Experiments showed that adaptive thresholding could provide suitable candidates, while CBR was able to identify HWBS regions. The proposed method has potential as a new tool for accurately detecting and quantifying HWBS. [ABSTRACT FROM AUTHOR]

The article presents a study which proposed a technique for the automatic detection of subarachnoid hemorrhage (SAH) on clinical non-contrast head computed tomography (CT) scans. Also cited are the proposed two steps in detecting SAH, including the identification of abnormality in the approximated subarachnoid space (SAS) through support vector machine (SVM) based pattern classification, and the components of the method including image preprocessing, brain segmentation, and feature extraction.

Most existing image-based approaches neglect the difference in radiological symmetry between the human brain and head. Thus, it is important to analyze and quantify the spatial relationship between the brain symmetry plane (BSP) and the head symmetry plane (HSP) on radiological images. The HSP and BSP were calculated through maximizing local symmetry within the head or cerebrum followed by outlier removal. The HSPs and BSPs for 145 diversified MRI datasets (80 normal, 23 pathological, and 42 synthesized) were extracted and compared. The average angular and distance deviations between the HSP and BSP were 0.49° and 1.65 mm, respectively. These deviations are dependent upon ethnicity and gender, being: (1) (0.56°, 1.85 mm) and (0.42°, 0.91 mm) for Caucasians and Asians, respectively; and (2) (0.33°, 1.17 mm) and (0.51°, 1.58 mm) for males and females, respectively. The HSP is generally different from the BSP on MR images. Statistically, they can be used interchangeably if accuracy of (0.49°, 1.65 mm) is acceptable. The BSP is preferred for a high accuracy Talairach transformation and localization of the anterior and posterior commissures. Either BSP or HSP can be used for medium accuracy Talairach transform. The HSP is preferred for detecting intracranial pathology. [ABSTRACT FROM AUTHOR]

The Talairach transformation is the most prevalent way to normalize brains and is hindered by, among others things, a lack of automatic determination of cortical landmarks. An algorithm to locate the modified Talairach cortical landmarks in three steps is proposed: determination of the three planes containing the landmarks; segmentation of the planes based on range-constrained thresholding and morphologic operations; and local refinement of the segmentation to locate the landmarks. The algorithm has been validated against 62 T1-weighted and SPGR MR diversified data sets. For each data set, it takes less than 2 s on a Pentium 4 to extract all six landmarks. The average landmark location errors are below 0.9 mm. The algorithm is robust due to incorporation of anatomic knowledge. A low computational cost results from processing of three 2D images and employing only simple operations like thresholding, basic morphologic operations, and distance transform. Magn Reson Med 53:970-976, 2005. © 2005 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]