Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Metho...Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.展开更多
Background Epilepsy is one of the common clinical disorders with comorbid anxiety and depression that severely afects their quality of life and increases their suicidality,while screening for anxiety and depression cu...Background Epilepsy is one of the common clinical disorders with comorbid anxiety and depression that severely afects their quality of life and increases their suicidality,while screening for anxiety and depression currently lacks objective identifers.This study aimed to analyze the characteristics of the electroencephalogram(EEG)power spectrum in patients with epilepsy with comorbid anxiety and depression,utilizing resting EEG data.Methods Resting EEG data were collected under standard conditions from two groups:patients with epilepsy comorbid with anxiety and depression(n=42)and patients without comorbidities(n=45).EEG power was calculated using data processing with EEGLAB and MATLAB.This study compared the absolute and relative powers of theδ,θ,α,β,andγfrequency bands,as well as the values of(δ+θ)/(α+β),between the two groups.Additionally,the correlation between the EEG power of each frequency band and anxiety and depression scores was analyzed.Results 1)Among individuals with epilepsy comorbid with anxiety and depression,lower absolute power ofδ,α,andθat specifc sites was observed(P<0.05),along with lower relative power ofθat certain sites(P<0.05).Conversely,higher relative power ofβandγat specifc sites was noted in those with comorbidities(P<0.05).2)There was no statistically signifcant diference in the values of(δ+θ)/(α+β)between the two groups(P>0.05).3)Depression scores exhibited a negative correlation withθabsolute power at the T3 and T4 sites(P<0.05),while showing a positive correlation withβrelative power at the C4 and T6 sites(P<0.05).Anxiety scores displayed a positive correlation withβrelative power at the F4,C3,C4 and T6 sites andγrelative power at F8 site(P<0.05).Conclusions The fndings suggest that comorbid anxiety and depression may impact resting EEG power spectra in individuals with epilepsy,particularly in regions exhibiting altered network connectivity.Furthermore,a positive correlation was observed between anxiety and depression scores andβrelative power in the right central and right posterior temporal regions,indicating potential screening utility.展开更多
Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network lev...Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.展开更多
BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explo...BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explore the potential characteristics of the brain network and functional connectivity(FC)of SA.METHODS Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by DPABINet.The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.RESULTS The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance.The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE score.All 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was 0.895.CONCLUSION The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.展开更多
AIM:To investigate changes in local brain activity after laser assisted in situ keratomileusis(LASIK)in myopia patients,and further explore whether post-LASIK(POL)patients and healthy controls(HCs)can be distinguished...AIM:To investigate changes in local brain activity after laser assisted in situ keratomileusis(LASIK)in myopia patients,and further explore whether post-LASIK(POL)patients and healthy controls(HCs)can be distinguished by differences in dynamic amplitude of low-frequency fluctuations(dALFF)in specific brain regions.METHODS:The resting-state functional magnetic resonance imaging(rs-fMRI)data were collected from 15 myopic patients who underwent LASIK and 15 matched healthy controls.This method was selected to calculate the corresponding dALFF values of each participant,to compare dALFF between the groups and to determine whether dALFF distinguishes reliably between myopic patients after LASIK and HCs using the linear support vector machine(SVM)permutation test(5000 repetitions).RESULTS:dALFF was lower in POL than in HCs at the right precentral gyrus and right insula.Classification accuracy of the SVM was 89.1%(P<0.001).CONCLUSION:The activity of spontaneous neurons in the right precentral gyrus and right insula of myopic patients change significantly after LASIK.SVM can correctly classify POL patients and HCs based on dALFF differences.展开更多
BACKGROUND Very late-onset schizophrenia-like psychosis(VLOSLP)is a subtype of schizophrenia spectrum disorders in which individuals experience psychotic symptoms for the first time after the age of 60.The incidence o...BACKGROUND Very late-onset schizophrenia-like psychosis(VLOSLP)is a subtype of schizophrenia spectrum disorders in which individuals experience psychotic symptoms for the first time after the age of 60.The incidence of VLOSLP shows a linear relationship with increasing age.However,no studies have reported alterations in spontaneous brain activity among VLOSLP patients and their correlation with cognitive function and clinical symptoms.AIM To explore VLOSLP brain activity and correlations with cognitive function and clinical symptoms using resting-state functional magnetic resonance imaging.METHODS This study included 33 VLOSLP patients and 34 healthy controls.The cognitive assessment utilized the Mini Mental State Examination,Montreal Cognitive Assessment,and the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS).Clinical characteristic acquisition was performed via the Positive and Negative Syndrome Scale(PANSS).All participants were scanned via resting-state functional magnetic resonance imaging,and the data were processed using amplitude of low-frequency fluctuations(ALFF),fractional ALFF(fALFF),regional homogeneity,and voxelmirrored homotopic connectivity(VMHC).RESULTS The VLOSLP group presented decreased ALFF values in the left cuneus,right precuneus,right precentral gyrus,and left paracentral lobule;increased fALFF values in the left caudate nucleus;decreased fALFF values in the right calcarine fissure and surrounding cortex(CAL)and right precuneus;increased regional homogeneity values in the right putamen;and decreased VMHC values in the bilateral CAL,bilateral superior temporal gyrus,and bilateral cuneus.In the VLOSLP group,ALFF values in the right precuneus were negatively correlated with Mini Mental State Examination score and PANSS positive subscale score,and VMHC values in the bilateral CAL were negatively correlated with the RBANS total score,RBANS delayed memory score,and PANSS positive subscale score.CONCLUSION The changes of brain activity in VLOSLP are concentrated in the right precuneus and bilateral CAL regions,which may be associated with cognitive impairment and clinically positive symptoms.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
Electroencephalography(EEG)is a powerful tool for investigating the brain bases of human psychological processes non-invasively.Some important mental functions could be encoded by resting-state EEG activity;that is,th...Electroencephalography(EEG)is a powerful tool for investigating the brain bases of human psychological processes non-invasively.Some important mental functions could be encoded by resting-state EEG activity;that is,the intrinsic neural activity not elicited by a specific task or stimulus.The extraction of informative features from resting-state EEG requires complex signal processing techniques.This review aims to demystify the widely used resting-state EEG signal processing techniques.To this end,we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing.We then examine in detail spectral,connectivity,and microstate analysis,covering the oft-used EEG measures,practical issues involved,and data visualization.Finally,we briefly touch upon advanced techniques like nonlinear neural dynamics,complex networks,and machine learning.展开更多
BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers base...BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.展开更多
Ejaculation is regulated by the central nervous system.However,the central pathophysiology of primary intravaginal anejaculation(PIAJ)is unclear.The present study aimed to examine the changes in regional brain activit...Ejaculation is regulated by the central nervous system.However,the central pathophysiology of primary intravaginal anejaculation(PIAJ)is unclear.The present study aimed to examine the changes in regional brain activity and functional connectivity underlying PIAJ.A total of 20 PIAJ patients and 16 healthy controls(HCs)were enrolled from September 2020 to September 2022 in the Department of Andrology,Nanjing Drum Tower Hospital(Nanjing,China).Magnetic resonance imaging data were acquired from all participants and then were preprocessed.The measures of fractional amplitude of low-frequency fluctuation(fALFF),regional homogeneity(ReHo),and functional connectivity(FC)were calculated and compared between the groups.PIAJ patients showed increased fALFF values in the left precuneus compared with HCs.Additionally,PIAJ patients showed increased ReHo values in the left precuneus,left postcentral gyrus,left superior occipital gyrus,left calcarine fissure,right precuneus,and right middle temporal gyrus,and decreased ReHo values in the left inferior parietal gyrus,compared with HCs.Finally,brain regions with altered fALFF and ReHo values in PIAJ patients showed increased FC with widespread cortical regions,which included the frontal,parietal,temporal,and occipital regions,compared with HCs.In conclusion,increased regional brain activity in the parietal,temporal,and occipital regions,and increased FC between these brain regions,may be associated with PIAJ occurrence.展开更多
基金This study received financial support from the Jilin Province Health and Technology Capacity Enhancement Project(Project Number:222Lc132).
文摘Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.
基金funded by the National Natural Science Foundation of China(82101527,32160190)the Science and Technology Project in Guizhou Province(Grant No.QKHJC-ZK[2021]No.408)the Science and Technology Foundation of the Guizhou Provincial Health Commission(gzwkj2021-026).
文摘Background Epilepsy is one of the common clinical disorders with comorbid anxiety and depression that severely afects their quality of life and increases their suicidality,while screening for anxiety and depression currently lacks objective identifers.This study aimed to analyze the characteristics of the electroencephalogram(EEG)power spectrum in patients with epilepsy with comorbid anxiety and depression,utilizing resting EEG data.Methods Resting EEG data were collected under standard conditions from two groups:patients with epilepsy comorbid with anxiety and depression(n=42)and patients without comorbidities(n=45).EEG power was calculated using data processing with EEGLAB and MATLAB.This study compared the absolute and relative powers of theδ,θ,α,β,andγfrequency bands,as well as the values of(δ+θ)/(α+β),between the two groups.Additionally,the correlation between the EEG power of each frequency band and anxiety and depression scores was analyzed.Results 1)Among individuals with epilepsy comorbid with anxiety and depression,lower absolute power ofδ,α,andθat specifc sites was observed(P<0.05),along with lower relative power ofθat certain sites(P<0.05).Conversely,higher relative power ofβandγat specifc sites was noted in those with comorbidities(P<0.05).2)There was no statistically signifcant diference in the values of(δ+θ)/(α+β)between the two groups(P>0.05).3)Depression scores exhibited a negative correlation withθabsolute power at the T3 and T4 sites(P<0.05),while showing a positive correlation withβrelative power at the C4 and T6 sites(P<0.05).Anxiety scores displayed a positive correlation withβrelative power at the F4,C3,C4 and T6 sites andγrelative power at F8 site(P<0.05).Conclusions The fndings suggest that comorbid anxiety and depression may impact resting EEG power spectra in individuals with epilepsy,particularly in regions exhibiting altered network connectivity.Furthermore,a positive correlation was observed between anxiety and depression scores andβrelative power in the right central and right posterior temporal regions,indicating potential screening utility.
基金supported by the National Natural Science Foundation of China,Nos.81871836(to MZ),82172554(to XH),and 81802249(to XH),81902301(to JW)the National Key R&D Program of China,Nos.2018YFC2001600(to JX)and 2018YFC2001604(to JX)+3 种基金Shanghai Rising Star Program,No.19QA1409000(to MZ)Shanghai Municipal Commission of Health and Family Planning,No.2018YQ02(to MZ)Shanghai Youth Top Talent Development PlanShanghai“Rising Stars of Medical Talent”Youth Development Program,No.RY411.19.01.10(to XH)。
文摘Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.
基金Supported by the Wuxi Municipal Health Commission Major Project,No.Z202107。
文摘BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving SA.AIM To explore the potential characteristics of the brain network and functional connectivity(FC)of SA.METHODS Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by DPABINet.The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.RESULTS The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance.The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE score.All 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was 0.895.CONCLUSION The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.
基金Supported by National Natural Science Foundation of China(No.82160195No.82460203)Key R&D Program of Jiangxi Province(No.20223BBH80014).
文摘AIM:To investigate changes in local brain activity after laser assisted in situ keratomileusis(LASIK)in myopia patients,and further explore whether post-LASIK(POL)patients and healthy controls(HCs)can be distinguished by differences in dynamic amplitude of low-frequency fluctuations(dALFF)in specific brain regions.METHODS:The resting-state functional magnetic resonance imaging(rs-fMRI)data were collected from 15 myopic patients who underwent LASIK and 15 matched healthy controls.This method was selected to calculate the corresponding dALFF values of each participant,to compare dALFF between the groups and to determine whether dALFF distinguishes reliably between myopic patients after LASIK and HCs using the linear support vector machine(SVM)permutation test(5000 repetitions).RESULTS:dALFF was lower in POL than in HCs at the right precentral gyrus and right insula.Classification accuracy of the SVM was 89.1%(P<0.001).CONCLUSION:The activity of spontaneous neurons in the right precentral gyrus and right insula of myopic patients change significantly after LASIK.SVM can correctly classify POL patients and HCs based on dALFF differences.
基金Supported by Wuxi Municipal Health Commission Major Project,No.202107and Wuxi Taihu Talent Project,No.WXTTP 2021.
文摘BACKGROUND Very late-onset schizophrenia-like psychosis(VLOSLP)is a subtype of schizophrenia spectrum disorders in which individuals experience psychotic symptoms for the first time after the age of 60.The incidence of VLOSLP shows a linear relationship with increasing age.However,no studies have reported alterations in spontaneous brain activity among VLOSLP patients and their correlation with cognitive function and clinical symptoms.AIM To explore VLOSLP brain activity and correlations with cognitive function and clinical symptoms using resting-state functional magnetic resonance imaging.METHODS This study included 33 VLOSLP patients and 34 healthy controls.The cognitive assessment utilized the Mini Mental State Examination,Montreal Cognitive Assessment,and the Repeatable Battery for the Assessment of Neuropsychological Status(RBANS).Clinical characteristic acquisition was performed via the Positive and Negative Syndrome Scale(PANSS).All participants were scanned via resting-state functional magnetic resonance imaging,and the data were processed using amplitude of low-frequency fluctuations(ALFF),fractional ALFF(fALFF),regional homogeneity,and voxelmirrored homotopic connectivity(VMHC).RESULTS The VLOSLP group presented decreased ALFF values in the left cuneus,right precuneus,right precentral gyrus,and left paracentral lobule;increased fALFF values in the left caudate nucleus;decreased fALFF values in the right calcarine fissure and surrounding cortex(CAL)and right precuneus;increased regional homogeneity values in the right putamen;and decreased VMHC values in the bilateral CAL,bilateral superior temporal gyrus,and bilateral cuneus.In the VLOSLP group,ALFF values in the right precuneus were negatively correlated with Mini Mental State Examination score and PANSS positive subscale score,and VMHC values in the bilateral CAL were negatively correlated with the RBANS total score,RBANS delayed memory score,and PANSS positive subscale score.CONCLUSION The changes of brain activity in VLOSLP are concentrated in the right precuneus and bilateral CAL regions,which may be associated with cognitive impairment and clinically positive symptoms.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
基金supported by the National Natural Science Foundation of China(Grant No.31822025,No.31671141)
文摘Electroencephalography(EEG)is a powerful tool for investigating the brain bases of human psychological processes non-invasively.Some important mental functions could be encoded by resting-state EEG activity;that is,the intrinsic neural activity not elicited by a specific task or stimulus.The extraction of informative features from resting-state EEG requires complex signal processing techniques.This review aims to demystify the widely used resting-state EEG signal processing techniques.To this end,we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing.We then examine in detail spectral,connectivity,and microstate analysis,covering the oft-used EEG measures,practical issues involved,and data visualization.Finally,we briefly touch upon advanced techniques like nonlinear neural dynamics,complex networks,and machine learning.
文摘BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.
基金supported by grants from the Nanjing Medical Technology Development Project(No.YKK19059)Excellent Young Doctor Training Program of Jiangsu Province Hospital of Chinese Medicine(No.2023QB0126)+1 种基金Jiangsu Province Graduate Research and Practice Innovation Program Project-School Assisted General Project(No.SJCX23_0804)the General project of Natural Science Foundat。
文摘Ejaculation is regulated by the central nervous system.However,the central pathophysiology of primary intravaginal anejaculation(PIAJ)is unclear.The present study aimed to examine the changes in regional brain activity and functional connectivity underlying PIAJ.A total of 20 PIAJ patients and 16 healthy controls(HCs)were enrolled from September 2020 to September 2022 in the Department of Andrology,Nanjing Drum Tower Hospital(Nanjing,China).Magnetic resonance imaging data were acquired from all participants and then were preprocessed.The measures of fractional amplitude of low-frequency fluctuation(fALFF),regional homogeneity(ReHo),and functional connectivity(FC)were calculated and compared between the groups.PIAJ patients showed increased fALFF values in the left precuneus compared with HCs.Additionally,PIAJ patients showed increased ReHo values in the left precuneus,left postcentral gyrus,left superior occipital gyrus,left calcarine fissure,right precuneus,and right middle temporal gyrus,and decreased ReHo values in the left inferior parietal gyrus,compared with HCs.Finally,brain regions with altered fALFF and ReHo values in PIAJ patients showed increased FC with widespread cortical regions,which included the frontal,parietal,temporal,and occipital regions,compared with HCs.In conclusion,increased regional brain activity in the parietal,temporal,and occipital regions,and increased FC between these brain regions,may be associated with PIAJ occurrence.
