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Parkinson’s disease (PD) can be considered as the dysfunction in segregation and integration of large-scale structural networks in the late stage of disease progression. However, the altered patterns in the early stage have not been extensively investigated, especially the altered structural rich-club patterns, which is proved powerful to detect the altered patterns of structural networks in Alzheimer’s disease and schizophrenia. To this end, we investigated the rich-club organization of the structural networks derived from diffusion tensor imaging (DTI) data in the early stage of PD, and further investigated the relationship between rich-club organization and clinicopathological measures, including motor and non-motor scales and cerebrospinal fluid (CSF) biomarkers. Two datasets were included for validation in this study. The first one included 41 healthy controls (HC) and 64 PD patients from Parkinson’s Disease Progression Marker Initiative (PPMI) dataset, and the second one included 24 HC and 26 PD patients. Results revealed that PD patients in early stage had disrupted rich-club organization, with abnormal connectivity strength between peripheral regions (two-sample t-test between PD and HC: p

In recent years, applying machine learning methods to neurological and psychiatric disorder diagnoses has grasped the interest of many researchers; however, currently available machine learning toolboxes usually require somewhat intermediate programming knowledge. In order to use machine learning methods more quickly and conveniently, we developed an intuitive toolbox named BrainSort. BrainSort used Python as the main programming languages and employed a hospitable Graphical User Interface (GUI). The toolbox is user-friendly for researchers and clinical doctors with little to no prior programming skills. It enables the client to choose from multiple machine learning methods, such as support vector machine (SVM), k-nearest neighbors (k-NN), and convolutional neural network (CNN) for data processing and training. Using BrainSort, doctors and researchers can calculate and visualize the correlation between brain connectome topology parameters and the symptom in question without prolonged programming training, empowering them to use the powerful tool of machine learning in their studies and practices.

Sporotrichosis is an infection caused by the microscopic fungus, Sporothrix schenckii. The disease follows the traumatic inoculation of fungus through injuries involving soil, inhalation of conidia, or zoonotic transmission especially from cat scratches. The objective of the retrospective cohort study was to investigate Th1, Th17, and Treg cell counts and host immunity in patients with lymphocutaneous sporotrichosis. From January 2017 to December 2018, 88 patients, diagnosed with sporotrichosis, were retrospectively reviewed. The patients were divided into acute (≤3 months; n = 46) and non-acute (> 3 months; n = 42) groups based on duration of the disease. We also selected 46 healthy adult participants (control group) for comparison. Th1, Th17, and Treg subsets were tested using flow cytometry (p < 0.05 was considered statistically significant). The Th1 and Th17 cell counts of the acute group were higher than those of the control group (p < 0.05). The Th1 and Th17 cell counts of the non-acute group were lower than those of the control controls (p < 0.05). The longer the duration of disease, the lower the Th1 and Th17 cell counts, however, Treg cell counts were lower in the acute group and higher in the non-acute group, relative to the control group (p < 0.05). An imbalance of Th1, Th17, and Treg cells was found in patients with lymphocutaneous sporotrichosis. The severity and duration of the disease may be affected by the imbalance of Th1, Th17, and Treg cells.

Background There is a need for devices that allow reproducible stimulation of skin areas of humans for investigating somatosensory mapping of the whole-body surface. However, their design is not simple, due to the magnetic field of MRI scanners. Purpose To accurately characterize the mapping of somatosensory presentation of the whole-body surface of subjects during functional (f)MRI scans. Study type Prospective. Population A water phantom and six healthy participants (age 23-27 years; two males) were recruited for the fMRI experiment. Field strength/sequence T1 -weighted magnetization-prepared rapid acquisition gradient-echo, T2 *-weighted gradient echo sequence at 3T. Assessment The stimulation device for somatotopic mapping was composed of three units: an air-generating unit, a control unit, and an execution unit. The fMRI in response to tactile stimulation was measured to characterize somatotopic mapping of the right-side body consisting of hand, arm, and leg in six healthy subjects. Statistical tests Pared-samples t-test for the conditions in SII. Results The pneumatical-mechanical tactile stimulation offered a wide range of stimulation intensities (0-400 g) in each channel. The predetermined physical pressure was successfully reached within ~5 msec and returned to baseline within 5 msec after the end of stimulation. With this tactile device, the digressive rate of the signal-to-noise ratio (SNR) (271.44 without the device, 269.68 with the device) was 0.65% in the magnetic field environment. For the fMRI experiment, the primary somatosensory activation contralateral to the stimulation site was detected in response to spatial task and attentive task. Data conclusion This stimulation device characterized the mapping of somatosensory representation of the whole-body surface in individual participants during fMRI scans. Level of evidence 2. Technical efficacy stage 1. J. Magn. Reson. Imaging 2020;52:1093-1101.

Background: Subjective Cognitive Decline (SCD) is the early preclinical stage of Alzheimer's Disease (AD). Previous study provided an invaluable contribution by showing that a tactile angle discrimination system can be used to distinguish between healthy older individuals and patients with mild cognitive impairment and AD. However, that study paid little attention to the relationship between tactile angle discrimination and SCD. Therefore, a means of differentiating Normal Controls (NCs), elderly subjects with SCD, patients with amnestic Mild Cognitive Impairment (aMCI), and AD is urgently needed. Methods: In the present study, we developed a novel tactile discrimination device that uses angle stimulation applied to the index finger pad to identify very small differences in angle discrimination between the NC (n = 30), SCD (n = 30), aMCI (n = 30), and AD (n = 30) groups. Using a three-alternative forced-choice and staircase method, we analyzed the average accuracy and threshold of angle discrimination. Results: We found that accuracy significantly decreased while thresholds of angle discrimination increased in the groups in the following order: NC, SCD, aMCI, and AD. The area under the receiver operating characteristic curve also indicated that the tactile angle discrimination threshold was better than Mini-Mental State Examination scores in distinguishing NC individuals and SCD patients. Conclusion: These findings emphasize the importance of tactile working memory dysfunction in explaining the cognitive decline in angle discrimination that occurs in SCD to AD patients and offer further insight into the very early detection of subjects with AD.

The apolipoprotein E (APOE) e4 allele is a genetic risk factor for Alzheimer's disease, whereas educational attainments have protective effects against cognitive decline in aging and patients with Alzheimer's disease. We examined the possible effects of years of education and APOE genotype on the topological properties of the functional network in normal aging, mild cognitive impairment and Alzheimer's disease. The years of education showed a significant, negative association with the local efficiency, clustering coefficient and small-worldness of functional networks in APOE e4 noncarriers but not in e4 carriers. These associations were mainly observed in normal aging and were reduced in mild cognitive impairment and Alzheimer's disease. Moreover, regions of the inferior frontal gyrus, temporal pole, and cuneus also showed correlations between education and nodal degree. Our findings demonstrated that the protective effects of education persist in APOE e4 noncarriers but diminish in e4 carriers. In addition, the protective effects of education were attenuated or reduced in the progression of Alzheimer's disease.

Ischemic stroke is associated with various physiological and pathological processes including neuronal apoptosis. Growth-arrest-specific transcript 5 (GAS5), a long non-coding RNA (lncRNA), has been recently reported to affect ischemic stroke-induced neuron apoptosis, while its mechanisms remain largely undefined. Through in silico analysis, GAS5 was predicted to interact with the promoter of MAP4K4. The aim of the present study was therefore to investigate the possible role of GAS5 in the progression of ischemic stroke via regulation of mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4) methylation. The expression of MAP4K4 was found to be lowly expressed in the clinical samples collected from 55 patients. MAP4K4 was suggested to be methylated in an in vitro model of oxygen-glucose deprivation (OGD)-treated mouse primary cortical neurons, while its overexpression could inhibit OGD-induced neuronal apoptosis. A series of dual-luciferase reporter, RIP, RNA pull-down, ChIP MSP, and BSP assays confirmed that GAS5 significantly induced MAP4K4 methylation and downregulated MAP4K4 expression through the recruitment of DNA methyltransferase 3B (DNMT3B). An in vivo ischemic stroke model was developed using middle cerebral artery occlusion (MCAO). Upregulation of GAS5 promoted OGD-induced neuronal apoptosis in the in vitro model and increased cerebral infarction size and neurological score in the in vivo model by reducing MAP4K4 expression. Collectively, the present study highlights that silencing GAS5 may inhibit neuronal apoptosis and improve neurological function in ischemic stroke by suppressing DNMT3B-mediated MAP4K4 methylation, which contributes to better understanding of the pathologies of ischemic stroke and development of novel therapeutic options for this disease.

Supplemental Digital Content is available in the text., Background and Purpose— Ischemic stroke is one of the leading causes of morbidity and mortality worldwide and a major cause of long-term disability. Recently, long noncoding RNAs have been revealed, which are tightly associated with several human diseases. However, the functions of long noncoding RNAs in ischemic stroke still remain largely unknown. In the current study, for the first time, we investigated the role of long noncoding RNA Nespas in ischemic stroke. Methods— We used in vivo models of middle cerebral artery occlusion and in vitro models of oxygen-glucose deprivation to illustrate the effect of long noncoding RNA Nespas on ischemic stroke. Results— We found expression of Nespas was significantly increased in ischemic cerebral tissues and oxygen-glucose deprivation–treated BV2 cells in a time-dependent manner. Silencing of Nespas aggravated middle cerebral artery occlusion operation–induced IR injury and cell death. In addition, proinflammatory cytokine production and NF-κB (nuclear factor-κB) signaling activation were inhibited by Nespas overexpression. TAK1 (transforming growth factor-β–activated kinase 1) was found to directly interact with Nespas, and TAK1 activation was significantly suppressed by Nespas. At last, we found Nespas-inhibited TRIM8 (tripartite motif 8)-induced K63-linked polyubiquitination of TAK1. Conclusions— We showed that Nespas played anti-inflammatory and antiapoptotic roles in cultured microglial cells after oxygen-glucose deprivation stimulation and in mice after ischemic stroke by inhibiting TRIM8-related K63-linked polyubiquitination of TAK1.

Objective.Brain-machine interfaces (BMIs) interpret human intent into machine reactions, and the visual stimulation (VS) paradigm is one of the most widely used of these approaches. Although VS-based BMIs have a relatively high information transfer rate (ITR), it is still difficult for BMIs to control machines in dynamic environments (for example, grabbing a dynamic object or targeting a walking person).Approach.In this study, we utilized a BMI based on augmented reality (AR) VS (AR-VS). The proposed VS was dynamically generated based on machine vision, and human intent was interpreted by a dynamic decision time interval approach. A robot based on the coordination of a task and self-motion system was controlled by the proposed paradigm in a fast and flexible state.Methods.Objects in scenes were first recognized by machine vision and tracked by optical flow. AR-VS was generated based on the objects' parameters. The number and distribution of VS was confirmed by the recognized objects. Electroencephalogram (EEG) features corresponding to VS and human intent were collected by a dry-electrode EEG cap and determined by the filter bank canonical correlation analysis method. Key parameters in the AR-VS, including the effect of VS size, frequency, dynamic object moving speed, ITR and the performance of the BMI-controlled robot, were analyzed.Conclusion and significance.The ITR of the proposed AR-VS paradigm for nine healthy subjects was 36.3 ± 20.1 bits min-1. In the online robot control experiment, brain-controlled hybrid tasks including self-moving and grabbing objects were 64% faster than when using the traditional steady-state visual evoked potential paradigm. The proposed paradigm based on AR-VS could be optimized and adopted in other kinds of VS-based BMIs, such as P300, omitted stimulus potential, and miniature event-related potential paradigms, for better results in dynamic environments.

In recent years, Rapid development had been made by brain computer interfaces(BCI) technology, providing a new way of communication between people and the outside world. Researchers proposed varied BCI based methods to control Robotic arms, exoskeletons, and robotic cars for locomotion. However, most of the contemporary BCI systems were poor in human-computer interaction and user experience. For example, in mind-controlled vehicles, controllers would not know the location of the vehicle in real time scene. On the other hand, traditional BCI, especially visual stimulated(VS) BCI methods lacked efficiency, for most VS-BCIs were driven by selective binary commands such as moving forward or backward. To solve this problem, we first proposed a hybrid BCI strategy for mind controlled vehicle, which involved simultaneous localization/mapping(SLAM) and steady state visual evoked potential(SSVEP), by which users could achieve mind control of a vehicle equipped with lidar. We designed a BCI system based on SLAM-SSVEP paradigm and carried out online experimental verification. The experimental results showed that all participants in the online experiment could achieve effective control of the BCI system based on SLAM-SSVEP paradigm.

Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. Due to the complex etiology and diverse clinical symptoms, it’s difficult to accurately diagnose PD. In this study, we applied multimodal physiological signals, which include electroencephalography (EEG), electrocardiogram (ECG), photoplethysmography (PPG), and respiratory (RA), to classify PD and healthy control (HC) based on a multimodal support vector machine (SVM). Our experiments achieved an accuracy of 96.03%. Besides, we performed statistical analysis on the four types of physiological data of the PD group and the HC group. Results showed that the EEG of non-dementia PD patients had a significant decrease in high-frequency power, and the high-frequency energy distribution of the normalized PPG signal increased compared with HC. The current study suggests that combining the physiological information of multiple models and machine learning methods could improve the diagnosis accuracy of PD disease and be a potentially effective method of clinical diagnosis.

In this paper, we propose a novel unsupervised domain adaptation re-ID framework by fusing feature adversarial learning and self-similarity clustering. Different from most of the existing works which only regard the source domain data as network pretraining data, we use the source domain data both in network pretraining and finetuing stage. Concretely, we construct an feature adversarial learning module to learn domain invariant feature representations. The feature extractor network is optimized in an adversarial training manner through minimizing the discrepancy of feature representations between source and target domains. To further enhance the discriminability of the feature extractor network, we design the self-similarity clustering module to mine the implicit similarity relationships among the unlabeled samples of the target domain. By unsupervised clustering, we can generate pseudo-identity labels for the target domain data, which are then combined with the labeled source data together to train the feature extractor network. Additionally, we present a relabeling algorithm to construct correspondence between two groups of pseudo-identity labels generated by two iterative clusterings. Experimental results validate the effectiveness of our method.

Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer's disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline.

Aims To evaluate whether visual field impairment (VFI) can predict stroke recurrence in patients with vertebral-basilar (VB) stroke. Methods A total of 326 patients were eligible for a VFI evaluation within 1 week of stroke onset. One-year follow-up data were obtained after VB stroke and other vascular events. All predictors were determined using Cox regression models. Results The overall incidence of recurrent VB stroke and transient ischemic attack (TIA) was 29% (n = 92). After multivariate adjustment, severe and moderate VFI were predictors of recurrent VB stroke and TIA. Conclusions VFI is an independent predictor of recurrent VB stroke and TIA.

Humans exhibit a remarkable ability to discriminate variations in object volume based on natural haptic perception. The discrimination thresholds for the haptic volume perception of the whole hand are well known, but the discrimination thresholds for haptic volume perception of fingers and phalanges are still unknown. In the present study, two psychophysical experiments were performed to investigate haptic volume perception in various fingers and phalanges. The configurations of both experiments were completely dependent on haptic volume perception from the fingers and phalanges. The participants were asked to blindly discriminate the volume variation of regular solid objects in a random order by using the distal phalanx, medial phalanx, and proximal phalanx of their index finger, middle finger, ring finger, and little finger. The discrimination threshold of haptic volume perception gradually decreases from the little finger to the index finger as well as from the proximal phalanx to the distal phalanx. Overall, both the shape of the target and the part of the finger in contact with the target significantly influence the precision of haptic perception of volume. This substantial data set provides detailed and compelling perspectives on the haptic system, including for discrimination of the spatial size of objects and for performing more general perceptual processes.

Energy efficiency is an extremely important indicator for exploring energy conservation and consumption reduction. The traditional energy efficiency assessment methods for process industry lack an in-depth thinking on energy utilization of the whole system. Based on the proposed required energy, this paper established an energy efficiency assessment method for process industry. By establishing material and energy flow networks, energy efficiency of typical iron and steel manufacturing process (ISMP) is analyzed and its influencing factors are discussed. The results found that the required energy of coking, sintering, pelletizing, blast furnace iron-making, basic oxygen furnace steel-making and steel rolling are 2626.2 MJ/t-coke, 1122.9 MJ/t-sinter, 992.3 MJ/t-pellet, 9781.5 MJ/t-hot metal, 393.95 MJ/t-molten steel and 445.3 MJ/t-steel products, respectively. The energy efficiency of typical ISMP is 66.9%. The energy efficiency of the ISMP can be effectively improved by adjusting the steel ratio, recovering waste heat and residual energy, and developing interface technologies.

We aimed to investigate whether Interleukin-29 (IL-29) directly affects T follicular helper (Tfh) cell frequency in rheumatoid arthritis (RA), which are both related to RA-specific antibody responses. Here, we explored the effect of IL-29 on Tfh cell production in RA patients using a combination of enzyme-linked immunosorbent assay (ELISA), flow cytometry (FCM), CD4+ T cell culture, western blotting, and reverse transcription–polymerase chain reaction (RT-PCR). We reported that serum IL-29 levels, peripheral blood CD4+CXCR5+ Tfh cell frequency, CD4+CXCR5+CD40L+ Tfh cell frequency, and IL-28 receptor (IL-28Rα) and IL-10 receptor (IL-10R2) levels in peripheral blood Tfh cells were higher in RA patients than in healthy controls (HCs). Serum IL-29 levels were positively correlated with peripheral blood CD4+CXCR5+CD40L+ Tfh cell frequency in RA patients, and both parameters also correlated with anti-cyclic citrullinated peptide (anti-CCP) antibodies. Furthermore, we showed that IL-29 may suppress Tfh cell differentiation in RA patients partly via decreased BCL6 level through reduced STAT3 activity. Taken together, our findings reveal the regulatory effect of IL-29 on Tfh cells, which participate in the pathogenesis of RA and provide new targets for its clinical treatment.

Background: Multiple sclerosis (MS) biomarker identification is important for pathogenesis research and diagnosis in routine clinical practice. Cerebrospinal fluid (CSF) and blood cytokines as potential biomarkers that can inform MS pathogenesis, diagnosis and response to treatment have been assessed in numerous studies. However, there have been no comprehensive meta-analyses to pool cytokine data and to address their diagnostic performance. We systematically reviewed literature with meta-analyses to assess the alteration levels of cytokines and chemokines in MS. Methods: We searched PubMed and Web of Science for articles published between January 1, 1990 and April 30, 2018 for this systematic review and meta-analysis. Data were extracted from 226 included studies encompassing 13,526 MS patients and 8,428 controls. Biomarker performance was rated by a random-effects meta-analysis based on the standard mean difference between cytokine concentration in patients with MS and controls, or patients before and after treatments. Results: Of the 26 CSF cytokines and 37 blood cytokines for potential differentiation between MS patients and controls, the random-effects meta-analysis showed that 13 CSF cytokines and 21 blood cytokines were significantly increased in MS patients in comparison to the controls. Interestingly, TNF-α, CXCL8, IL-15, IL-12p40, and CXCL13 were increased in both blood and CSF of MS patients. For those cytokines analyzed in at least 10 studies, differentiation between case and control was strong for CSF CXCL13, blood IL-2R, and blood IL-23; CSF CXCL8, blood IL-2, and blood IL-17 also performed well in differentiating between MS patients and controls, whereas those of CSF TNF-α and blood TNF-α, CXCL8, IL-12, IFN-γ were moderate. Furthermore, CSF IL-15, CCL19, CCL11, CCL-3, and blood CCL20, IL-12p40, IL-21, IL-17F, IL-22 had large effective sizes when differentiating between MS patients and controls but had a relatively small number of studies (three to seven studies). Conclusion: Our findings clarified the circulating cytokine profile in MS, which provide targets for disease modifying treatments, and suggest that cytokines have the potential to be used as biomarkers for MS.

A new attempt for combining machine learning and node parameters based on graph theory is described in this paper in order to find effective features in the early stage of Parkinson's disease (PD). A classifier based on graph measures provides informative predictions. A classification model using support vector machines (SVM) algorithm was constructed to differentiate PD from healthy controls (HC). Six node parameters all achieved a high classification power with accuracy of 96.97% for betweenness centrality, 99.00% for degree centrality, 97.92% for cluster coefficients, 94.06% for local efficiency and 95.84% for shortest path lengths and 96.74% for nodal efficiency. Left median cingulate and paracingulate gyri and right posterior cingulate gyrus were related to non-motor aspects of experiences of daily living. The left supplementary motor area, right posterior cingulate gyrus and right superior occipital gyrus involved with motor experiences of daily living. Left hippocampus and left insula were associated with motor symptoms. Our results suggest that node parameters based on graph theory have high diagnostic efficiency in distinguish early-stage PD from HC, and machine learning methods may provide a new perspective in PD network patterns.

Schizophrenia patients exhibited a psychological abnormal appearance when they recognized objects related to themselves. This cognitive process is associated with self-referential processing. In this study, the self-referential memory (SRM) task was performed by 18 schizophrenia patients and 18 healthy controls. In the encoding stage of the SRM task, the behavioral experiment data and electroencephalogram (EEG) data were recorded in three experimental conditions (self-referential condition, other-referential condition, and physical condition). For data analysis, the electrophysiological performance of the time-frequency distribution, phase lag index (PLI) strengths, phase synchronization connectivity, and brain-network properties were assessed in schizophrenia patients compared to healthy controls. We found that schizophrenia patients exhibited abnormal alpha oscillation characteristics at the time of 100-300 ms poststimulus during the self-referential condition, which consisted of diminished time-frequency distributions over the prefrontal, parietal, and occipital regions; lower functional connectivity strengths of the PLI in the parietal and occipital areas; higher global efficiency and the lower characteristic path length; and nodal efficiency of local areas (increased nodal efficiency in temporal regions and decreased nodal efficiency in occipital region) for dynamic network topology properties. Furthermore, the evoked power of the alpha band during the self-referential condition was significantly correlated with the SRM bias score in the patients (r = 0.595, p = 0.009). These results provided electrophysiological evidence and supported the hypothesis that an abnormal alpha rhythm might be the principal factor of dysfunctional self-referential processing in schizophrenia patients.

Alzheimer’s disease (AD) is the most common neurodegenerative disease in elderly individuals. We conducted this study to examine whether alterations in the fractional amplitudes of low-frequency fluctuations (fALFF) in the AD spectrum were frequency-dependent and symptom-relevant. A total of 43 patients with subjective cognitive decline (SCD), 52 with amnestic mild cognitive impairment (aMCI), 44 with Alzheimer’s dementia (d-AD) and 55 well-matched controls participated in resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitudes were measured using fALFF within the slow-4 (0.027–0.073 Hz) and slow-5 (0.01–0.027 Hz) bands. Repeated-measures analysis of variance was performed on fALFF within two bands and correlated with neuropsychological test scores. The significant main effects of frequency and group on fALFF differed widely across brain regions. There were more varied areas in the slow-5 band than the slow-4 band. The fALFF associated with primary disease effects was mainly distributed in the parietal lobe. Obvious frequency band and group interaction effects were observed in the left angular gyrus, left calcarine fissure and surrounding cortex, left superior cerebellum, left cuneus and right lingual gyrus. Neuropsychological tests scores were significantly correlated with the fALFF magnitude of the left cuneus and right lingual in the slow-5 band. Our results suggested that the AD continuum had abnormal amplitudes in intrinsic brain activity, and these abnormalities were frequency-dependent and mainly associated with the slow-5 band rather than the slow-4 band. This may guide the frequency choice of future rs-fMRI studies and provide new insights into the neuropathophysiology of AD.

Schizophrenia patients always show cognitive impairment, which is proved to be related to hypo-connectivity or hyper-connectivity. Further, individuals with an ultra-high risk for psychosis also show abnormal functional connectivity-related cognitive impairment, especially in the alpha rhythm. Thus, the identification of functional networks is essential to our understanding of the disorder. We investigated the resting-state functional connectivity of the alpha rhythm measured by electroencephalography (EEG) to reveal the relation between functional network and clinical symptoms. The participants included 28 patients with first-episode schizophrenia (FES), 28 individuals with ultra-high risk for psychosis (UHR), and 28 healthy controls (HC). After the professional clinical symptoms evaluation, all the participants were instructed to keep eyes closed for 3-min resting-state EEG recording. The 3-min EEG data were segmented into artefact-free epochs (the length was 3 s), and the functional connectivity of the alpha phase was estimated using the phase lag index (PLI), which measures the phase differences of EEG signals. The FES and UHR groups displayed increased resting-state PLI connectivity compared with the HC group [F(2,74) = 10.804, p < 0.001]. Significant increases in the global efficiency, the local efficiency, and the path length were found in the FES and UHR groups compared with those of the HC group. FES and UHR showed an increased degree of connectivity compared with HC. The degree of the left occipital lobe area was higher in the UHR group than in the FES group. The hypothesis of disconnection is confirmed. Furthermore, differences between the UHR and FES group were found, which is valuable for producing clinical significance before the onset of schizophrenia.

Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.

Background: Alzheimer’s disease (AD) is a common neurodegenerative disease in which the brain undergoes alterations for decades before symptoms become obvious. Subjective cognitive decline (SCD) have self-complain of persistent decline in cognitive function especially in memory but perform normally on standard neuropsychological tests. SCD with the presence of AD pathology is the transitional stage 2 of Alzheimer’s continuum, earlier than the prodromal stage, mild cognitive impairment (MCI), which seems to be the best target to research AD. In this study, we aimed to detect the transformational patterns of the intrinsic brain activity as the disease burden got heavy. Method: In this study, we enrolled 44 SCD, 55 amnestic MCI (aMCI), 47 AD dementia (d-AD) patients and 57 normal controls (NC) in total. A machine learning classification was utilized to detect identification accuracies between groups by using ALFF, fALFF, and fusing ALFF with fALFF features. Then, we measured the amplitude of the low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) levels in three frequency bands (classic: 0.01–0.1 Hz; slow-5: 0.01–0.027 Hz; and slow-4: 0.027–0.073 Hz) and compared alterations in patients with NC. Results: In the machine learning verification, the identification accuracy of SCD, aMCI, d-AD from NC was higher when fused ALFF and fALFF features (76.44, 81.94, and 91.83%, respectively) than only using ALFF or fALFF features. Several brain regions showed significant differences in ALFF/fALFF within these bands among four groups: brain regions presented decreasing trend of values, including the Cingulum_Mid_R (aal), bilateral inferior cerebellum lobe, bilateral precuneus, and the Cingulum_Ant_R (aal); increasing trend of values were detected in the Hippocampus_L (aal), Frontal_Mid_Orb_R (aal), Frontal_Sup_R (aal) and Paracentral_Lobule_R (aal) as disease progressed. The normalized ALFF/fALFF values of these features were significantly correlated with the neuropsychological test scores. Conclusion: This study revealed gradual disturbances in intrinsic brain activity as the disease progressed: the normal objective performance in SCD may be dependent on compensation; as disease advanced, the cognitive function gradually impaired and decompensated in aMCI, severer in d-AD. Our results indicated that the ALFF and fALFF may help detect the underlying pathological mechanism in AD continuum. Clinical Trial Registration: ClinicalTrials.gov, identifier {"type":"clinical-trial","attrs":{"text":"NCT02353884","term_id":"NCT02353884"}}NCT02353884 and {"type":"clinical-trial","attrs":{"text":"NCT02225964","term_id":"NCT02225964"}}NCT02225964.

Aortic dissection (AD) is a life-threatening cardiovascular disease with a high mortality rate. The accurate and generalized 3-D reconstruction of AD from CT-angiography can effectively assist clinical procedures and surgery plans, however, is clinically unavaliable due to the lacking of efficient tools. In this study, we presented a novel multi-stage segmentation framework for type B AD to extract true lumen (TL), false lumen (FL) and all branches (BR) as different classes. Two cascaded neural networks were used to segment the aortic trunk and branches and to separate the dual lumen, respectively. An aortic straightening method was designed based on the prior vascular anatomy of AD, simplifying the curved aortic shape before the second network. The straightening-based method achieved the mean Dice scores of 0.96, 0.95 and 0.89 for TL, FL, and BR on a multi-center dataset involving 120 patients, outperforming the end-to-end multi-class methods and the multi-stage methods without straightening on the dual-lumen segmentation, even using different network architectures. Both the global volumetric features of the aorta and the local characteristics of the primary tear could be better identified and quantified based on the straightening. Comparing to previous deep learning methods dealing with AD segmentations, the proposed framework presented advantages in segmentation accuracy.

Functional connections are commonly used when exploring the human brain, especially in brain data analysis. However, most of the studies concentrate on traditional statistical analysis. In this paper, we innovatively combined the functional connection with the deep learning algorithms and analysed the matrices after the weight distribution of each layer of the convolutional neural network (CNN) to obtain the connections that play a vital role in the classification. The electroencephalogram (EEG) data used in this paper was acquired through a visual mismatch negativity (MMN) experiment. When dealing with this data, each electrode was regarded as a node in the network, and the phase lag index (PLI) was calculated to construct the functional connection matrices, which were used as inputs for the CNN classification and feature extraction. The matrices after the weight distribution were further analysed by means of graph theory. In this paper, the classification accuracy for deviation and standard stimuli are over 95%, and the theta band achieved the highest accuracy. Through the distributed matrices, we found that there are two regions that obtained larger weights from the convolutional layers, i.e., the temporal lobe and the occipital region. The occipital region is related to our visual experiment, and the temporal lobe region is connected with MMN mechanism. We also considered the strategy of the three-layer CNN according to weight distribution processing.

A new method for classifying the preclinical stages of Alzheimer's disease (AD)and positioning the related brain areas is described in this paper in order to slow the progress of AD. The method is based on multi-voxel pattern analysis (MVPA)., which is used to classify normal control (NC)participants and patients and find the changes in different brain areas with AD progression. In the classification., each voxel's blood oxygen level dependence (BOLD)signal during resting-state functional magnetic resonance imaging (rs-fMRI)was extracted as the basic features. To reduce the amount of features, principal component analysis (PCA)and two-class support vector machine (SVM)were applied to classify 62 NC participants and 162 different stages of patients, which included 47 subjective cognitive decline (SCD)patients, 60 amnestic mild cognitive impairment (aMCI)patients and 55 AD patients respectively. The accuracy of classification reached to 62.71% in SCD., 70.67% in aMCI and 86.36% in AD (all of them were classified with NC participants). Based on the accuracy, we innovatively combined “weight vectors” in SVM with permutation test as discrimination patterns to further investigate the related brain areas. The discriminating areas, including middle cingulum (right), insula (left)., paracentral lobule (right)and middle temporal (left)are responsible for different cognitive functions and could provide a large application of AD biomarkers. Our method and results suggest the potential of real-time diagnosis and cognitive therapy because of no complex feature calculations.

The present study aimed to explore the modulation of frequency bands (alpha, beta, theta) underlying the positive facial expressions classification advantage within different post-stimulus time intervals (100–200 ms, 200–300 ms, 300–400 ms). For this purpose, we recorded electroencephalogram (EEG) activity during an emotion discrimination task for happy, sad and neutral faces. The correlation between the non-phase-locked power of frequency bands and reaction times (RTs) was assessed. The results revealed that beta played a major role in positive classification advantage (PCA) within the 100–200 and 300–400 ms intervals, whereas theta was important within the 200–300 ms interval. We propose that the beta band modulated the neutral and emotional face classification process, and that the theta band modulated for happy and sad face classification.

Alzheimer's disease (AD) has a preclinical phase that can last for decades prior to clinical dementia onset. Subjective cognitive decline (SCD) is regarded as the last preclinical AD stage prior to the development of amnestic mild cognitive decline (aMCI) and AD dementia (d-AD). The analysis of brain structural networks based on diffusion tensor imaging (DTI) has identified the so-called 'rich club', a set of cortical regions highly connected to each other, with other regions referred to as peripheral. It has been reported that rich club architecture is affected by regional atrophy and connectivity, which are reduced in patients with aMCI and d-AD. Methods: We recruited 62 normal controls, 47 SCD patients, 60 aMCI patients and 55 d-AD patients and collected DTI data to analyze rich-club organization. Results: We demonstrated that rich club organization was disrupted, with reduced structural connectivity among rich club nodes, in aMCI and d-AD patients but remained stable in SCD patients. In addition, SCD, aMCI and d-AD patients showed similar patterns of disrupted peripheral regions and reduced connectivity involving these regions, suggesting that peripheral regions might contribute to cognitive decline and that disruptions here could be regarded as an early marker of SCD. This organization could provide the fundamental structural architecture for complex cognitive functions and explain the low prevalence of cognitive problems in SCD patients. Conclusions: These findings reveal a disrupted pattern of the AD connectome that starts in peripheral regions and then hierarchically propagates to rich club regions, when patients show clinical symptoms. This pattern provides evidence that disruptions in rich club organization are a key factor in the progression of AD that can dynamically reflect the progression of AD, thus representing a potential biomarker for early diagnosis.

Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.

In this paper, we constructed a novel stimulator system that can present quantitatively controllable air puffs for the research of the human somatosensory system with functional magnetic imaging (fMRI). The developed stimulator system consists of an adjustable stimulus regulating apparatus, and a control box, which is synchronized with fMRI. The system allows regulation of 1mm aperture position in both horizontal and vertical plane by using a special customized adjuster. In addition, the stimulus regulating apparatus can suit different experimental requirements and easily be fixed to and untied from the digits. This new adjustable pneumatic air-jet stimulator system is portable, easy-to-assemble, and economical. A validation fMRI experiment indicated that stimulate different phalanx were induced in activation of different somatosensory areas. The developed device is expected to be utilized for fMRI studies of the tactile system.

Orientation is one of the important elements of objects that can influence visual processing. In this study, we examined whether changes in orientation could be detected automatically under unattended condition. Visual mismatch negativity (vMMN) was used to analyze this processing. In addition, we investigated the underlying neural oscillatory activity. Non-phase-locked spectral power was used to explore the specific frequency related to unexpected changes in orientation. The experiment consisted of standard (0° arrows) and deviant (90°/270° arrows) stimuli. Compared with standard stimuli, deviant stimuli elicited a larger N170 component (negative wave approximately 170 ms after the stimuli started) and a smaller P2 component (positive wave approximately 200 ms after the stimuli started). Furthermore, vMMN was obtained by subtracting the event-related potential (ERP) waveforms in response to standard stimuli from those elicited in response to deviant stimuli. According to the time-frequency analysis, deviant stimuli elicited enhanced band power compared with standard stimuli in the delta and theta bands. Compared with previous studies, we concluded that theta activity plays an important role in the generation of the vMMN induced by changes in orientation.

Context contributes to accurate and efficient information processing. To reveal the dynamics of the neural mechanisms that underlie the processing of visual contexts during the recognition of color, shape, and 3D structure of objects, we carried out functional magnetic resonance imaging (fMRI) of subjects while judging the contextual validity of the three visual contexts. Our results demonstrated that the modes of effective connectivity in the cortical pathways, as well as the patterns of activation in these pathways, were dynamical depending on the nature of the visual contexts. While the fusiform gyrus, superior parietal lobe, and inferior prefrontal gyrus were activated by the three visual contexts, the temporal and parahippocampal gyrus/Amygdala (PHG/Amg) cortices were activated only by the color context. We further carried out dynamic causal modeling (DCM) analysis and revealed the nature of the effective connectivity involved in the three contextual information processing. DCM showed that there were dynamic connections and collaborations among the brain regions belonging to the previously identified ventral and dorsal visual pathways.

Visual word expertise is typically associated with enhanced ventral occipito-temporal (vOT) cortex activation in response to written words. Previous study utilized a passive viewing task and found that vOT response to written words was significantly stronger in literate compared to the illiterate subjects. However, recent neuroimaging findings have suggested that vOT response properties are highly dependent upon the task demand. Thus, it is unknown whether literate adults would show stronger vOT response to written words compared to illiterate adults during other cognitive tasks, such as perceptual matching. We addressed this issue by comparing vOT activations between literate and illiterate adults during a Chinese character and simple figure matching task. Unlike passive viewing, a perceptual matching task requires active shape comparison, therefore minimizing automatic word processing bias. We found that although the literate group performed better at Chinese character matching task, the two subject groups showed similar strong vOT responses during this task. Overall, the findings indicate that the vOT response to written words is not affected by expertise during a perceptual matching task, suggesting that the association between visual word expertise and vOT response may depend on the task demand.

This study used electroencephalogram measurements to investigate the effects of aging on oscillatory theta modulation during an audiovisual discrimination task. By a wavelet-based time-frequency analysis, age-related theta oscillation response differences were observed within a relatively restricted time range (0-500 ms) over frontal-central regions. Older adults showed stronger theta spectral power during visual and audiovisual stimuli in the left frontal regions; however, young adults showed stronger theta spectral power during auditory and audiovisual stimuli in the central regions. These findings suggest that multisensory oscillatory theta responses differ according to age, which further proves that the left frontal regions play an important role in audiovisual integration.

Supplemental Digital Content is available in the text, Schizophrenia is a complex disorder characterized by marked social dysfunctions, but the neural mechanism underlying this deficit is unknown. To investigate whether face-specific perceptual processes are influenced in schizophrenia patients, both face detection and configural analysis were assessed in normal individuals and schizophrenia patients by recording electroencephalogram (EEG) data. Here, a face processing model was built based on the frequency oscillations, and the evoked power (theta, alpha, and beta bands) and the induced power (gamma bands) were recorded while the subjects passively viewed face and nonface images presented in upright and inverted orientations. The healthy adults showed a significant face-specific effect in the alpha, beta, and gamma bands, and an inversion effect was observed in the gamma band in the occipital lobe and right temporal lobe. Importantly, the schizophrenia patients showed face-specific deficits in the low-frequency beta and gamma bands, and the face inversion effect in the gamma band was absent from the occipital lobe. All these results revealed face-specific processing in patients due to the disorder of high-frequency EEG, providing additional evidence to enrich future studies investigating neural mechanisms and serving as a marked diagnostic basis.

A new portable brain-computer interface (BCI) system for the inner ideas expressing by voice is designed and fabricated in this paper, The dry electrode is used in this system to record ElectroencephaloFaphy (EEG), and it is fabricated using the standard micromachining techniques. Compared with previous designs, our system has features of light weight and convenient carrying. The system is mainly divided into three parts: signal acquisition, signal processing, and output control. The EEG signal is recorded by the dry electrode, and then amplified, processed successively by the application specific integrate circuit (ASIC), and the obtained processed signal can be used to control the external output equipment. A subject's attention amplitude can be nicely captured with our system, and the subjects could have the ability to express our real feeling through the EEG signal after training.

In present paper, we described a novel wide-view visual presentation system for fMRI studies. To achieve a wide field view, a translucent spherical screen with a curvature radius of 30 mm was placed 30 mm away from the sub-jects' eyes. The subjects wore contact lenses that enabled them to focus on the screen, and the resulting visual field reached 120ai¾?. Then, high resolution com-puter-generated images were projected onto the translucent hemispheric screen inside the MRI bore. Signal-to-noise ratio valuation experiment was performed to evaluate the clarity and quality of the MRI images. In addition, we success-fully applied this visual presentation system to studies of visual retinotopic mapping and object perception neural function in the peripheral visual field. Our study demonstrated that the system is compatible with the MRI environ-ment. Moreover, this system was more effective at mapping checkerboard stim-uli in V1-V3, and successfully located several classical category-selective areas, including the face-selective area (FFA), occipital face area (OFA), house-selective area (PPA), transverse occipital sulcus (TOS), lateral occipital com-plex (LOC) in higher-level visual areas. In conclusion, the wide-view visual presentation system within the MRI environment can be applied to many kinds of fMRI studies of peripheral vision.

We developed a novel wide-view visual presentation system for fMRI studies. Computer-generated images were projected onto a hemispheric, translucent screen inside the MRI bore and were then back-projected onto a 52mm diameter screen. To achieve a wide field view, a spherical screen with a curvature radius of 30mm was placed 30mm away from the subjects' eyes. The subjects wore contact lenses that enabled them to focus on the screen, and the resulting visual field reached 120°. To evaluate the clarity and quality of the MRI images, a signal-to-noise ratio valuation experiment was performed. In addition, we successfully applied this visual presentation system to studies of visual retinotopic mapping and object perception neural function in the peripheral visual field. Our study demonstrated that the system is compatible with the MRI environment. Based on the wide-field mapping results, this system was more effective at mapping a checkerboard stimuli in V1-V3 from the central to peripheral visual fields. In higher-level visual areas, we successfully located several classical category-selective areas, including the face-selective area (FFA), occipital face area (OFA), house-selective area (PPA), transverse occipital sulcus (TOS), lateral occipital complex (LOC) and posterior fusiform area (pFs). In these areas, we found that the response amplitudes exhibited different decreasing trends with increasing eccentricity. In conclusion, we developed a simple, effective method for presenting wide-view visual stimuli within the MRI environment that can be applied to many kinds of fMRI studies of peripheral vision.

The potential for human neuroimaging to read-out the detailed contents of a person's mental state has yet to be fully explored. For fMRI decoding, it is important to choose an appropriate set of voxels (or features) as inputs to the decoder, since the presence of many irrelevant voxels could lead to poor generalization performance, a problem known as overfitting. We applied ARD-based sparse Bayesian algorithm to solve overfitting in fMRI classification. The simulated data demonstrated that sparse logistic regression can select effective features through the weight parameters for each class, and most of the selected features lied in the class they belong to. We observed that sparse logistic regression over pruned some effective features under the condition of 80 features, yet it had limited negative impact on the performance of prediction. The reason is perhaps that these over pruned features have similar values for all classes. On the other hand, it indicated that the selected features contained enough information for classification. Another interesting result is that as more irrelevant features were added to the training set, the performance of C-SVM dropped sharply. This demonstrated that sparse logistic regression can outperform C-SVM when irrelevant features are far more than relevant ones. Despite sparse sparse logistic regression wrongly selected some effective features, it had very limited impact on the prediction performance using both correct and wrong features.

Using publicly available software packages (BrainVoyager, SPM, AFNI and FreeSurfer), performing multiple cortical representation methods for most of the necessary processing steps in fMRI data analyses. We will argue that specifying where in the brain activation has occurred is both conceptually and technically more difficult than has been generally assumed. In this report, we briefly presented methods related to the most commonly used analysis pipeline. (1) An automated procedure was performed to localize the borders between early visual areas, and retinotopic maps were projected onto flattened cortical surface representations obtained by BrainVoyager Qx 2.0.7. (2) Attention and face cortex response areas were defined by SPM8. (3) Functional data analyses were performed to integrate the cortex surface by AFNI and FreeSurfer. The results presenting the most suitable and valuable data processing applications using three of the types of software described above.

Purpose To develop and validate the functionality of a novel wide-view visual presentation system with a horizontal and vertical eccentricity angle of 60° for retinotopic mapping by functional MRI (fMRI). Materials and Methods The wide-view presentation system consisted of a 52-mm diameter optical fiber, an entrance apparatus and a presentation apparatus. The terminal edge of the optical fiber at the entrance is flat, while the terminal edge on the presentation apparatus is a sphere of 60 mm in diameter. The subjects wore contact lenses with +20, +22, or +25 magnification to focus on the stimulus, and the visual field eccentricity angle could reach 60°. The signal to noise ratio valuation experiment was performed to evaluate the clarity and quality of the MRI picture image. Checkerboard and random dot stimuli were used to prove that this system could be applied to retinotopic mapping by fMRI. Results The results of the experiment demonstrated that the system is safe in the MRI environment with minimal distortion and can be used for visual retinotopic mapping studies. Wide-field mapping areas (V6, MST) were found in the human visual cortex. Compared with previous studies, the V1 and MT+ surface area approaches but does not fully cover the anatomical area. Nonetheless, the area achieved using the new system is larger than those achievable in previous fMRI studies. Conclusion We developed a versatile, low-cost system for presenting wide-view visual stimuli in the MRI environment. The fMRI retinotopic mapping results proved the viability of this system. J. Magn. Reson. Imaging 2011. © 2011 Wiley-Liss, Inc.

Using a novel wide-view visual presentation system for vision research developed by us and visual retinotopic mapping functional magnetic resonance imaging (fMRI). We have investigated the variation of BLOD response, in three visual areas (V1–V3), as a function of visual retintotopic areas and five contrast levels (96%, 48%, 24%, 12% and 6%). Each visual area was identified in each subject using a combination of retinotopic mapping methods and cortical flattening techniques. A checkerboard patterns with five level contrasts were used as the stimuli in this study. We also constructed BLOD responses amplitude tuning curves, averaged across subjects, for three visual areas (V1–V3) and three eccentricities (0–20°, 20–40°, 40– 60°). The result indicated that the peripheral stimulation would lead to lower BLOD responses, compared with the visual areas relative to central field stimulation. The present signal change of each visual area is consistent with the known physiological properties of cells in the corresponding macaque visual areas in previous functional imaging studies. In presented study, we confirmed visual areas V3 showed more sensitivity to contrast, than V2, and than V1 in central and peri-central visual area. More importantly, we revealed the biological responses for peripheral visual areas (40–60°), which the V1–V3 had BLOD signal change only for higher contrast (96%and 48%), but no BLOD response for lower contrast (24%, 12% and 6%).

In this article, we introduced some topics relevant to neuromedical engineering and used several measurement methods. In the future, we will continue to develop new technologies and search for new ideas for diagnosing neuropathies.

Recent imaging studies have identified a region in occipito-temporal cortex essential for reading and labeled it as the visual word form area (VWFA). Several studies suggested that the development of this word specific region is closely related to the experience with words. In this study, two groups of Chinese subjects, one literate and the other illiterate, were asked to compare two Chinese characters presented at left and right side of a fixation point and decide whether they were the same character or not. Similar comparison of simple figures was used as control condition. We found stronger activation to Chinese characters than to simple figures in left and right hemispheric occipito-temporal regions in both literate and illiterate subjects. However, at the peak voxels revealed by Chinese character and simple figure contrast, the response to Chinese character stimuli in left occipito-temporal region of literate subjects was significantly stronger than that in the right one, whereas in illiterate subjects no such hemispheric preference was observed. We propose that the enhancement of the left peak voxel activation in literate group is due to their long-time acquaintance with reading.

Using functional magnetic resonance imaging (fMRI) we have researched the variation in response magnitude, in each visual areas (V1-V3), as a function of visual retintotopic areas (V1-V3) and temporal frequency (TF). Each visual area was identified in each subject using a combination of retinotopic mapping fMRI and cortical flattening techniques. A checkerboard was used as the stimulus in the study in which we TF was 2, 4, 8, 16Hz. It was hypothesized the peripheral stimulation would lead to faster onset latencies, as well as preferentially activae doral stream visual areas relative to central field stimulation. For the experiment we constructed fMRI amplitude tuning curves, averaged across subjects, for each visual area. The tuning curves that resulted are consistent with the known physiological properties of cells in the corresponding macaque visual areas, previous functional imaging studies, and in the case of V1, the psychophysically determined contrast sensitivity functions for temporal frequency. And retinotopic mapping is a key property of organization of human occipital cortex. The retinotopic organization of the central visual field of visual areas V1/V2/V3 has been well estimated. We also used functional magnetic resonance Imaging (fMRI) to measure the retinotopic mapping of the peripheral visual field (eccentricity up to 60deg). We estimate the locations of the visual areas in 0∽60deg and the result is coincident with the anatomical studies. Visual areas V1, V2, V3 showed more direction sensitivity at low temporal frequencies in the central visual area, while there was the same signal change in the peripheral visual areas.

The retinotopic characteristics on human peripheral vision are still not well known. The position, surface area and visual field representation of human visual areas V1, V2 and V3 were measured using fMRI in 8 subjects (16 hemispheres). Cortical visual field maps of the 120deg were measured using rotating wedge and expanding ring stimuli. The boundaries between areas were identified using an automated procedure to fit an atlas of the expected visual field map to the data. All position and surface area measurements were made along the boundary between white matter and gray matter. In this study, we developed a new visual presentation system widest view (60 deg of eccentricity). The wide-view visual presentation system was made from nonmagnetic optical fibers and a contact lens, so can use in general clinical fMRI condition and the cost is lower. We used the newly wide view visual presentation system, the representation of the visual field in areas V1, V2 and V3 spans about 2223 mm∧2,1679 mm∧2 and 1690 mm∧2.