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Background: Obesity is a modifiable risk factor for Alzheimer's disease (AD). However, its relation with tau pathology (i.e., aberrant tau protein behavior in tauopathies such as AD) has been inconclusive. Objective: This study investigated the interaction between a high-fat diet (HFD) and tau pathology in adult male mice. Methods: Transgenic mice overexpressing human P301S Tau (those with the pathology) and wild-type (WT) littermates were subjected to behavioral tests, functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and western blotting analysis to investigate the effects of prolonged HFD versus regular diet during adulthood. Results: HFD increased body weight in both WT and P301S mice but had minimal effect on blood glucose levels. The brain response to HFD was tau genotype-specific. WT mice exhibited decreased recognition memory and enhanced network connectivity in fMRI, while P301S mice exhibited white matter tract disorganization in DTI as the sole significant finding. The reduction of insulin receptor β, insulin downstream signaling, neuronal nuclear protein, CD68-positive phagocytic activity, and myelin basic protein level were confined to the cortex of WT mice. In contrast to P301S mice, WT mice showed significant changes in the tau protein and its phosphorylation levels along with increased soluble neurofilament light levels in the hippocampus. Conclusions: HFD-induced brain dysfunction and pathological changes were blunted in mice with the pathology and more profound in healthy mice. Our findings highlight the need to consider this interaction between obesity and tau pathology when tailoring treatment strategies for AD and other tauopathies. [ABSTRACT FROM AUTHOR]

Brain decoding is to predict the external stimulus information from the collected brain response activities, and visual information is one of the most important sources of external stimulus information. Decoding functional magnetic resonance imaging (fMRI) based on visual stimulation is helpful in understanding the working mechanism of the brain visual function regions. Traditional brain decoding algorithms cannot accurately extract stimuli features from fMRI. To address these shortcomings, this paper proposed a brain decoding algorithm based on a graph convolution network (GCN). Firstly, 11 regions of interest (ROI) were selected according to the human brain visual function regions, which can avoid the noise interference of the non-visual regions of the human brain; then, a deep three-dimensional convolution neural network was specially designed to extract the features of these 11 regions; next, the GCN was used to extract the functional correlation features between the different human brain visual regions. Furthermore, to avoid the problem of gradient disappearance when there were too many layers of graph convolutional neural network, the residual connections were adopted in our algorithm, which helped to integrate different levels of features in order to improve the accuracy of the proposed GCN. The proposed algorithm was tested on the public dataset, and the recognition accuracy reached 98.67%. Compared with the other state-of-the-art algorithms, the proposed algorithm performed the best. [ABSTRACT FROM AUTHOR]

Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders. • We use NGC technique to explore sex-dependent brain aging patterns. • Brain aging may cause widespread NGC interaction declines at multi-scales. • NGC interactions of females have stronger heterogeneity than males during aging. • NGC can work as a sensitive metric in reflecting the cognitive patterns of brain. [ABSTRACT FROM AUTHOR]

Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation. [ABSTRACT FROM AUTHOR]

Integrating multimodal information into a unified perception is a fundamental human capacity. McGurk effect is a remarkable multisensory illusion that demonstrates a percept different from incongruent auditory and visual syllables. However, not all listeners perceive the McGurk illusion to the same degree. The neural basis for individual differences in modulation of multisensory integration and syllabic perception remains largely unclear. To probe the possible involvement of specific neural circuits in individual differences in multisensory speech perception, we first implemented a behavioral experiment to examine the McGurk susceptibility. Then, functional magnetic resonance imaging was performed in 63 participants to measure the brain activity in response to non-McGurk audiovisual syllables. We revealed significant individual variability in McGurk illusion perception. Moreover, we found significant differential activations of the auditory and visual regions and the left Superior temporal sulcus (STS), as well as multiple motor areas between strong and weak McGurk perceivers. Importantly, the individual engagement of the STS and motor areas could specifically predict the behavioral McGurk susceptibility, contrary to the sensory regions. These findings suggest that the distinct multimodal integration in STS as well as coordinated phonemic modulatory processes in motor circuits may serve as a neural substrate for interindividual differences in multisensory speech perception. [ABSTRACT FROM AUTHOR]

Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation.

Brain decoding is to predict the external stimulus information from the collected brain response activities, and visual information is one of the most important sources of external stimulus information. Decoding functional magnetic resonance imaging (fMRI) based on visual stimulation is helpful in understanding the working mechanism of the brain visual function regions. Traditional brain decoding algorithms cannot accurately extract stimuli features from fMRI. To address these shortcomings, this paper proposed a brain decoding algorithm based on a graph convolution network (GCN). Firstly, 11 regions of interest (ROI) were selected according to the human brain visual function regions, which can avoid the noise interference of the non-visual regions of the human brain; then, a deep three-dimensional convolution neural network was specially designed to extract the features of these 11 regions; next, the GCN was used to extract the functional correlation features between the different human brain visual regions. Furthermore, to avoid the problem of gradient disappearance when there were too many layers of graph convolutional neural network, the residual connections were adopted in our algorithm, which helped to integrate different levels of features in order to improve the accuracy of the proposed GCN. The proposed algorithm was tested on the public dataset, and the recognition accuracy reached 98.67%. Compared with the other state-of-the-art algorithms, the proposed algorithm performed the best.

Abstract Background While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain–machine interface training system for robotic arm control is proposed and developed. Methods The proposed system uses a Microsoft Kinect to detect and estimates the 3D positions of the possible target objects. The predicted velocity vector for robot arm input is compensated using the artificial potential to follow an intended one among the possible targets. Two participants with cervical spinal cord injury trained with the system to explore its possible effects. Results In a situation with four possible targets, the proposed system significantly improved the distance error to the intended target compared to the unintended ones (p

Cerebral small vessel disease (CSVD) is characterized by widespread functional changes in the brain, as evident from abnormal brain activations during cognitive tasks. However, the existing findings in this area are not yet conclusive. We systematically reviewed 25 studies reporting task-related fMRI in five cognitive domains in CSVD, namely executive function, working memory, processing speed, motor, and affective processing. The findings highlighted: (1) CSVD affects cognitive processes in a domain-specific manner; (2) Compensatory and regulatory effects were observed simultaneously in CSVD, which may reflect the interplay between the negative impact of brain lesion and the positive impact of cognitive reserve. Combined with behavioral and functional findings in CSVD, we proposed an integrated model to illustrate the relationship between altered activations and behavioral performance in different stages of CSVD: functional brain changes may precede and be more sensitive than behavioral impairments in the early pre-symptomatic stage; Meanwhile, compensatory and regulatory mechanisms often occur in the early stages of the disease, while dysfunction/decompensation and dysregulation often occur in the late stages. Overall, abnormal hyper-/hypo-activations are crucial for understanding the mechanisms of small vessel lesion-induced behavioral dysfunction, identifying potential neuromarker and developing interventions to mitigate the impact of CSVD on cognitive function., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2023. Published by Elsevier Masson SAS.)

In recent years, deep learning models have shown their advantages in neuroimage analysis, such as brain disease diagnosis. Unfortunately, it is usually difficult to acquire numerous brain networks at a single centralized site to effectively train a high-quality deep learning model. To address this issue, federated learning (FL) has gained popularity in brain disease diagnosis, which allows deep learning models to be trained without centralizing data. However, most FL-based works might still face two following challenges. Firstly, the high-dimensional features of brain networks are often far larger than sample size, which might lead to poor performance due to the curse of dimensionality. Secondly, differences in data distributions across different sites can impact the communication efficiency and performance of FL models. To overcome these challenges, we design a novel FL framework for diagnosing brain disorders, named FedBrain. Firstly, FedBrain proposes data augmentation based on L 1 regularization to select significant features shared by all clients. The domain alignment loss based on the maximum mean discrepancy criterion is introduced to minimize differences in the marginal and conditional distributions between local clients. Furthermore, FedBrain proposes a personalized predictor based on mixture of experts to adapt to different clients, using a global and private predictor as two experts. Eventually, FedBrain integrates the above modules with differential privacy and homomorphic encryption into a unified FL framework. Experimental results on the Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate its effectiveness and robustness, which shows that FedBrain can reduce the communication burden of FL and achieve the highest average accuracy of 79% against other counterparts. [ABSTRACT FROM AUTHOR]

Observing mouth movements has strikingly effects on the perception of speech. Any mismatch between sound and mouth movements will result in listeners perceiving illusory consonants (McGurk effect), whereas matching mouth movements assist with the correct recognition of speech sounds. Recent neuroimaging studies have yielded evidence that the motor areas are involved in speech processing, yet their contributions to multisensory illusion remain unclear. Using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) in an event-related design, we aimed to identify the functional roles of the motor network in the occurrence of multisensory illusion in female and male brains. fMRI showed bilateral activation of the inferior frontal gyrus (IFG) in audiovisually incongruent trials. Activity in the left IFG was negatively correlated with occurrence of the McGurk effect. The effective connectivity between the left IFG and the bilateral precentral gyri was stronger in incongruent than in congruent trials. The McGurk effect was reduced in incongruent trials by applying single-pulse TMS to motor cortex (Ml) lip areas, indicating that TMS facilitates the left IFG-precentral motor network to reduce the McGurk effect. TMS of the Ml lip areas was effective in reducing the McGurk effect within the specific temporal range from 100 ms before to 200 ms after the auditory onset, and TMS of the Ml foot area did not influence the McGurk effect, suggesting topographical specificity. These results provide direct evidence that the motor network makes specific temporal and topographical contributions to the processing of multisensory integration of speech to avoid illusion. [ABSTRACT FROM AUTHOR]

Un-sun Chung,1 Doug Hyun Han,2 Yee Jin Shin,3 Perry F Renshaw4 1Department of Psychiatry, Kyungpook National University Hospital, Daegu, Republic of Korea; 2Department of Psychiatry, Chung Ang University Hospital, Seoul, Republic of Korea; 3Department of Psychiatry, Yonsei University Hospital, Seoul, Republic of Korea; 4Brain Institute, Utah University, Salt Lake City, UT, USA Abstract: To help patients with autism spectrum disorder (ASD) improve their social skills, effective interventions and new treatment modalities are necessary. We hypothesized that a prosocial online game would improve social cognition in ASD adolescents, as assessed using metrics of social communication, facial recognition, and emotional words. Ten ASD adolescents underwent cognitive behavior therapy (CBT) using a prosocial online game (game-CBT), and ten ASD adolescents participated in an offline-CBT. At baseline and 6 weeks later, social communication quality, correct identification of emotional words and facial emoticons, and brain activity were assessed in both groups. Social communication quality and correct response rate of emotional words and facial emoticons improved in both groups over the course of the intervention, and there were no significant differences between groups. In response to the emotional words, the brain activity within the temporal and parietal cortices increased in the game-CBT group, while the brain activity within cingulate and parietal cortices increased in the offline-CBT group. In addition, ASD adolescents in the game-CBT group showed increased brain activity within the right cingulate gyrus, left medial frontal gyrus, left cerebellum, left fusiform gyrus, left insular cortex, and sublobar area in response to facial emoticons. A prosocial online game designed for CBT was as effective as offline-CBT in ASD adolescents. Participation in the game especially increased social arousal and aided ASD adolescents in recognizing emotion. The therapy also helped participants more accurately consider associated environments in response to facial emotional stimulation. However, the online CBT was less effective than the offline-CBT at evoking emotions in response to emotional words. Keywords: autism, online games, sociality, cognitive behavior therapy, functional magnetic resonance image

Summary: Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi‐resolution spatio‐temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole‐brain connectivity. The proposed model allows for testing voxel‐specific activation while accounting for non‐stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between‐ROIs). The model is used in a motor‐task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment in Parkinson’s disease (PD) has been linked with functional brain changes. Previously, using functional magnetic resonance imaging (fMRI), we reported reduced cortico-striatal activity in patients with PD who also had mild cognitive impairment (MCI) versus those who did not (non-MCI). We followed up these patients to investigate the longitudinal effect on the neural activity. Twenty-four non-demented patients with Parkinson’s disease (non-MCI: 12, MCI; 12) were included in the study. Each participant underwent two fMRIs while performing the Wisconsin Card Sorting Task 20 months apart. The non-MCI patients recruited the usual cognitive corticostriatal loop at the first and second sessions (Time 1 and Time 2, respectively). However, decreased activity was observed in the cerebellum and occipital area and increased activity was observed in the medial prefrontal cortex and parietal lobe during planning set-shift at Time 2. Increased activity in the precuneus was also demonstrated while executing set-shifts at Time 2. The MCI patients revealed more activity in the frontal, parietal and occipital lobes during planning set-shifts, and in the parietal and occipital lobes, precuneus, and cerebellum, during executing set-shift at Time 2. Analysis regrouping of both groups of PD patients revealed that hippocampal and thalamic activity at Time 1 was associated with less cognitive decline over time. Our results reveal that functional alteration along the time-points differed between the non-MCI and MCI patients. They also underline the importance of preserving thalamic and hippocampal function with respect to cognitive decline over time.

Objective Cognitive impairments had been reported in childhood acute lymphoblastic leukemia, what caused the impairments needed to be demonstrated, chemotherapy-related or the disease itself. The primary aim of this exploratory investigation was to determine if there were changes in brain function of children with acute lymphoblastic leukemia before chemotherapy. Methods In this study, we advanced a measure named regional homogeneity to evaluate the resting-state brain activities, intelligence quotient test was performed at same time. Using regional homogeneity, we first investigated the resting state brain function in patients with new onset childhood acute lymphoblastic leukemia before chemotherapy, healthy children as control. Results The decreased ReHo values were mainly founded in the default mode network and left frontal lobe, bilateral inferior parietal lobule, bilateral temporal lobe, bilateral occipital lobe, precentral gyrus, bilateral cerebellum in the newly diagnosed acute lymphoblastic leukemia patients compared with the healthy control. While in contrast, increased ReHo values were mainly shown in the right frontal lobe (language area), superior frontal gyrus-R, middle frontal gyrus-R and inferior parietal lobule-R for acute lymphoblastic leukemia patients group. There were no significant differences for intelligence quotient measurements between the acute lymphoblastic leukemia patient group and the healthy control in performance intelligence quotient, verbal intelligence quotient, total intelligence quotient. Conclusion The altered brain functions are associated with cognitive change and language, it is suggested that there may be cognition impairment before the chemotherapy. Regional homogeneity by functional magnetic resonance image is a sensitive way for early detection on brain damage in childhood acute lymphoblastic leukemia. [ABSTRACT FROM AUTHOR]

Social cognition impairments are frequently found in patients with mild traumatic brain injury (TBI) when structural lesions may not reveal the severity of the injury. Though instruments used to assess social behavior are thought to be sensitive, the absence of structural damage in TBI patients may lead to underscore such problems. The aim of this study was to develop a complementary diagnostic tool such as a paradigm for functional Magnetic resonance Imaging (fMRI) involving a simple task that could tell how patients understand certain complex social behavior by identifying different movements with or without social intentions where language and complex cognitive process were not required. Eleven patients with mild TBI and social cognition difficulties and twelve control subjects were matched by demographic variables. A paradigm of social fMRI was developed by using dots in movement representing human motion, human motion with social intention such as dancing or sharing, and dots moving without meaning. Patients had less activation in parietotemporal junction and bilateral middle frontal gyrus in the social perception task movement compared with control group subjects. The fMRI paradigm developed can be an additional diagnostic tool for identifying social cognition impairments in mild TBI patients. Regardless the absence of structural injury, changes in activation areas suggest a prospective use of this tool since clinical, cognitive and functional outcomes support such finding. [ABSTRACT FROM AUTHOR]

Theoretical consideration predicts that the alteration of local and shared information in the brain is a key element in the mechanism of anesthetic-induced unconsciousness. Ordinal pattern analysis, such as permutation entropy (PE) and symbolic mutual information (SMI), have been successful in quantifying local and shared information in neurophysiological data; however, they have been rarely applied to altered states of consciousness, especially to data obtained with functional magnetic resonance imaging (fMRI). PE and SMI analysis, together with the superb spatial resolution of fMRI recording, enables us to explore the local information of specific brain areas, the shared information between the areas, and the relationship between the two. Given the spatially divergent action of anesthetics on regional brain activity, we hypothesized that anesthesia would differentially influence entropy (PE) and shared information (SMI) across various brain areas, which may represent fundamental, mechanistic indicators of loss of consciousness. FMRI data were collected from 15 healthy participants during four states: wakefulness (W), light (conscious) sedation (L), deep (unconscious) sedation (D), and recovery (R). Sedation was produced by the common, clinically used anesthetic, propofol. Firstly, we found that that global PE decreased from W to D, and increased from D to R. The PE was differentially affected across the brain areas; specifically, the PE in the subcortical network was reduced more than in the cortical networks. Secondly, SMI was also differentially affected in different areas, as revealed by the reconfiguration of its spatial pattern (topographic structure). The topographic structures of SMI in the conscious states W, L, and R were distinctively different from that of the unconscious state D. Thirdly, PE and SMI were positively correlated in W, L, and R, whereas this correlation was disrupted in D. And lastly, PE changes occurred preferentially in highly connected hub regions. These findings advance our understanding of brain dynamics and information exchange, emphasizing the importance of topographic structure and the relationship of local and shared information in anesthetic-induced unconsciousness.

Identification of optimal treatment strategies to improve recovery is limited by the incomplete understanding of the neurobiological principles of recovery. Motor cortex (M1) reorganization of the lesioned hemisphere (ipsilesional M1) plays a major role in post-stroke motor recovery, and is a primary target for rehabilitation therapy. Reorganization of M1 in the hemisphere contralateral to the stroke (contralesional M1) may, however, serve as an additional source of cortical reorganization and related recovery. The extent and outcome of such reorganization depends on many factors, including lesion size and time since stroke. In the chronic phase post-stroke, contralesional M1 seems to interfere with motor function of the paretic limb in a subset of patients, possibly through abnormally increased inhibition of lesioned M1 by the contralesional M1. In such patients, decreasing contralesional M1 excitability by cortical stimulation results in improved performance of the paretic limb. However, emerging evidence suggests a potentially supportive role of contralesional M1. After infarction of M1 or its corticospinal projections, there is abnormally increased excitatory neural activity and activation in contralesional M1 that correlates with favorable motor recovery. Decreasing contralesional M1 excitability in these patients may result in deterioration of paretic limb performance. In animal stroke models, reorganizational changes in contralesional M1 depend on the lesion size and rehabilitation treatment and include long-term changes in neurotransmitter systems, dendritic growth and synapse formation. While there is, therefore, some evidence that activity in contralesional M1 will impact the extent of motor function of the paretic limb in the subacute and chronic phase post-stroke and may serve as a new target for rehabilitation treatment strategies, the precise factors that specifically influence its role in the recovery process remain to be defined.

The alteration of pathological cortical surface may lead to the corresponding clinical manifestations of sporadic Parkinson’s disease (sPD). Therefore, we investigated the correlates of cortical surface and clinical manifestations in the mid-stage sPD. Sixty seven mid-stage sPD patients and thirty five matched controls were performed the corticometry of magnetic resonance imaging (MRI) and the assessment of clinical manifestations including the demographic and disease-related characteristics, and underwent the final analysis of the correlates between cortical surface and clinical manifestations. The result revealed a significant correlation between CDT and Frontal-Sup-Orb-L, Frontal-Sup-Medial-L, Frontal-Mid-Orb-L and Rectus-L; SVFT and Frontal-Mid-L and Frontal-Inf-Tri-L; DF and Frontal-Sup-R, Frontal-Mid-R and Frontal-Sup-Medial-R; Webster and Occipital-Mid-R, Angular-R, Temporal-Sup-R and Temporal-Mid-R respectively in the mid-stage sPD patients. Our data suggested that the alterations of cortical surface in the left Frontal-Sup-Orb, Sup-Medial, Mid-Orb, Mid, Inf-Tri and Rectus, the right Frontal-Sup, Mid, Sup-Medial, and Occipital-Mid, Angular, Temporal-Sup and Temporal-Mid were the pathological base of some clinical manifestations including the cognitive impairment, the space structure, memory, attention, the abstract thinking, design, layout, utilization, digital, calculation, the time and spatial orientation concept, the operation sequence recognition and the partial motor dysfunctions in the mid-stage sPD, and that the dysfunctions of these brain regions contributed by the cortical surface lesion were closely correlated with some clinical manifestations of mid-stage sPD. [ABSTRACT FROM AUTHOR]