F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM...F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.展开更多
Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clin...Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.展开更多
The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when co...The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.展开更多
In recent years,target detection of aerial images of unmannedaerial vehicle(UAV)has become one of the hottest topics.However,targetdetection of UAV aerial images often presents false detection and misseddetection.We p...In recent years,target detection of aerial images of unmannedaerial vehicle(UAV)has become one of the hottest topics.However,targetdetection of UAV aerial images often presents false detection and misseddetection.We proposed a modified you only look once(YOLO)model toimprove the problems arising in object detection in UAV aerial images:(1)A new residual structure is designed to improve the ability to extract featuresby enhancing the fusion of the inner features of the single layer.At the sametime,triplet attention module is added to strengthen the connection betweenspace and channel and better retain important feature information.(2)Thefeature information is enriched by improving the multi-scale feature pyramidstructure and strengthening the feature fusion at different scales.(3)A newloss function is created and the diagonal penalty term of the anchor frame isintroduced to improve the speed of training and the accuracy of reasoning.The proposed model is called residual feature fusion triple attention YOLO(RT-YOLO).Experiments showed that the mean average precision(mAP)ofRT-YOLO is increased from 57.2%to 60.8%on the vehicle detection in aerialimage(VEDAI)dataset,and the mAP is also increased by 1.7%on the remotesensing object detection(RSOD)dataset.The results show that theRT-YOLOoutperforms other mainstream models in UAV aerial image object detection.展开更多
Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alz...Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.展开更多
Attention deficit hyperactivity disorder(ADHD) is the most common neurodevelopmental disorder in children and adolescents, with prevalence ranging between 5% and 12% in the developed countries. Tic disorders(TD) are c...Attention deficit hyperactivity disorder(ADHD) is the most common neurodevelopmental disorder in children and adolescents, with prevalence ranging between 5% and 12% in the developed countries. Tic disorders(TD) are common co-morbidities in paediatric ADHD patients with or without pharmacotherapy treatment. There has been conflicting evidence of the role of psychostimulants in either precipitating or exacerbating TDs in ADHD patients. We carried out a literature review relating to the management of TDs in children and adolescents with ADHD through a comprehensive search of MEDLINE, EMBASE, CINAHL and Cochrane databases. No quantitative synthesis(meta-analysis) was deemed appropriate. Metaanalysis of controlled trials does not support an association between new onset or worsening of tics and normal doses of psychostimulant use. Supratherapeutic doses of dextroamphetamine have been shown to exacerbate TD. Most tics are mild or moderate and respond to psychoeducation and behavioural management. Level A evidence support the use of alpha adrenergic agonists, including Clonidine and Guanfacine, reuptake noradrenenaline inhibitors(Atomoxetine) and stimulants(Methylphenidate and Dexamphetamines) for the treatment of Tics and comorbid ADHD. Priority should be given to the management of co-morbid Tourette's syndrome(TS) or severely disabling tics in children and adolescents with ADHD. Severe TDs may require antipsychotic treatment. Antipsychotics, especially Aripiprazole, are safe and effective treatment for TS or severe Tics, but they only moderately control the co-occurring ADHD symptomatology. Short vignettes of different common clinical scenarios are presented to help clinicians determine the most appropriate treatment to consider in each patient presenting with ADHD and co-morbid TDs.展开更多
A growing number of oil and gas reservoirs have been discovered in granite and metamorphic crystallized rock areas. Statistics show that, about 157 oil and gas fields were found in crystallized bedrocks, with oil rese...A growing number of oil and gas reservoirs have been discovered in granite and metamorphic crystallized rock areas. Statistics show that, about 157 oil and gas fields were found in crystallized bedrocks, with oil reserves of 5048x 10^8 t, and gas reserves of 2681x10^8m3. Among the discovered industrial oil and gas fields hosted in crystallized rocks, most occurred in granite rocks, occupying 40% in quantity and 75% in reserves, followed by those hosted in mafic and ultra-mafic rocks （about 3%）, and then tbllowed by those in volcanic rocks and metamorphic rocks.展开更多
Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor symptoms.Among motor symptoms,one of the major disabling symptom i...Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor symptoms.Among motor symptoms,one of the major disabling symptom is Freezing of Gait(FoG)that affects the daily standard of living of PD patients.Available treatments target to improve the symptoms of PD.Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual.This work proposed a novel attention-basedmodel for the detection of FoG events and PD,andmeasuring the intensity of PD on the United Parkinson’s Disease Rating Scale.Two separate datasets,that is,UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective problems.The results show a definite rise in the various performance metrics when compared to landmark models on these problems using these datasets.These results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected problem.High valueswere obtained for various performance metrics like accuracy of 98.74%for detection FoG,98.72%for detection of PD and 98.05%for measuring the intensity of PD on UPDRS.The model was also analyzed for robustness against noisy samples,where also model exhibited consistent performance.These results strongly suggest that the proposed model provides a better classification method for selected problem.展开更多
Alzheimer’s Disease (AD) is characterized by an early and significant memory impairment, and progresses to affect other cognitive domains. Impairments in Focused Attention (FA) have been observed in patients diagnose...Alzheimer’s Disease (AD) is characterized by an early and significant memory impairment, and progresses to affect other cognitive domains. Impairments in Focused Attention (FA) have been observed in patients diagnosed with mild AD. A functional magnetic resonance imaging (fMRI) Stroop paradigm with verbal responses was used to investigate the neural correlates of FA in AD patients. Twenty-one patients diagnosed with mild AD performed a verbal Stroop—fMRI paradigm. Colour words were printed in an incongruent ink colour. Series 1 consisted of four blocks “Read the word” followed by four blocks “Say the colour of the ink”;Series 2 alternated between the two conditions. Functional data were analyzed using SPM5 to detect anatomical areas with significant signal intensity differences between the conditions. Within-group analyses of the colour minus word contrast yielded significant activation in the following left hemisphere regions: precentral gyrus, inferior frontal gyrus, fusiform gyrus and supplementary motor area (p < 0.05, uncorrected). Relative to cognitively normal older adults who underwent the same experimental task, Stroop performance was significantly worse in AD patients. The fMRI task yielded similar activated brain regions between the two groups. The use of verbal responses in this novel fMRI Stroop task avoids the confusion and memorizing of button locations seen with the manual response modality, allowing the neural correlates of FA to be investigated in AD patients.展开更多
Basal forebrain corticopetal cholinergic neurons are known to be necessary for normal attentional process-ing. Alterations of cholinergic system functioning have been associated with several neuropsychiatric diseases,...Basal forebrain corticopetal cholinergic neurons are known to be necessary for normal attentional process-ing. Alterations of cholinergic system functioning have been associated with several neuropsychiatric diseases, such as Alzheimer’s disease and schizophrenia, in which attentional dysfunction is thought to be a key contrib-uting factor. Loss of cortical cholinergic inputs impairs performance in attention-demanding tasks. Moreover, measures of acetylcholine with microdialysis and, more recently, of choline with enzyme-coated microelectrodes have begun to elucidate the precise cognitive demands that activate the cholinergic system on distinct time scales. However, the receptor actions following acetyl-choline release under attentionally-challenging condi-tions are only beginning to be understood. The present review is designed to summarize the evidence regarding the actions of acetylcholine at muscarinic and nicotinic receptors under cognitively challenging conditions in order to evaluate the functions mediated by these two different cholinergic receptor classes. Moreover, evi-dence that supports beneficial effects of muscarinic muscarinic-1 receptor agonists and selective nicotinic receptor subtype agonists for cognitive processing will be discussed. Finally, some challenges and limitations of targeting the cholinergic system for treating cognitive defcits along with future research directions will be mentioned. In conclusion, multiple aspects of cholinergic neurotransmission must be considered when attempting to restore function of this neuromodulatory system.展开更多
文摘F_(10.7)指数是太阳活动的重要指标,准确预测F_(10.7)指数有助于预防和缓解太阳活动对无线电通信、导航和卫星通信等领域的影响.基于F_(10.7)射电流量的特性,在双向长短时记忆网络(Bidirectional Long Short-Term Memory Network,BiLSTM)基础上融入注意力机制(Attention),提出了一种基于BiLSTM-Attention的F_(10.7)预报模型.在加拿大DRAO数据集上其平均绝对误差(MAE)为5.38,平均绝对百分比误差(MAPE)控制在5%以内,相关系数(R)高达0.987,与其他RNN模型相比拥有优越的预测性能.针对中国廊坊L&S望远镜观测的F_(10.7)数据集,提出了一种转换平均校准(Conversion Average Calibration,CAC)方法进行数据预处理,处理后的数据与DRAO数据集具有较高的相关性.基于该数据集对比分析了RNN系列模型的预报效果,实验结果表明,BiLSTM-Attention和BiLSTM两种模型在预测F_(10.7)指数方面具有较好的优势,表现出较好的预测性能和稳定性.
基金funded by the National Natural Science Foundation of China(No.62076044)Scientific Research Foundation of Chongqing University of Technology(No.2020ZDZ015).
文摘Alzheimer’s disease(AD)is a complex,progressive neurodegenerative disorder.The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice.In this study,we introduce an advanced diagnostic methodology rooted in theMed-3D transfermodel and enhanced with an attention mechanism.We aim to improve the precision of AD diagnosis and facilitate its early identification.Initially,we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation,which are commonly observed in imaging datasets.Subsequently,an attention mechanism is incorporated to selectively focus on the salient features within the imaging data.Building upon this foundation,we present the novelMed-3D transfermodel,designed to further elucidate and amplify the intricate features associated withADpathogenesis.Our proposedmodel has demonstrated promising results,achieving a classification accuracy of 92%.To emphasize the robustness and practicality of our approach,we introduce an adaptive‘hot-updating’auxiliary diagnostic system.This system not only enables continuous model training and optimization but also provides a dynamic platform to meet the real-time diagnostic and therapeutic demands of AD.
基金Institutional Fund Projects under Grant No.(IFPIP:638-830-1443).
文摘The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.
基金supported in part by the Scientific Research Project of Hunan Provincial Department of Education under Grant 18A332 and 19A066,authors HW.D and Z.C,http://kxjsc.gov.hnedu.cn/in part by the Science and Technology Plan Project of Hunan Province under Grant 2016TP1020,author HW.D,http://kjt.hunan.gov.cn/.
文摘In recent years,target detection of aerial images of unmannedaerial vehicle(UAV)has become one of the hottest topics.However,targetdetection of UAV aerial images often presents false detection and misseddetection.We proposed a modified you only look once(YOLO)model toimprove the problems arising in object detection in UAV aerial images:(1)A new residual structure is designed to improve the ability to extract featuresby enhancing the fusion of the inner features of the single layer.At the sametime,triplet attention module is added to strengthen the connection betweenspace and channel and better retain important feature information.(2)Thefeature information is enriched by improving the multi-scale feature pyramidstructure and strengthening the feature fusion at different scales.(3)A newloss function is created and the diagonal penalty term of the anchor frame isintroduced to improve the speed of training and the accuracy of reasoning.The proposed model is called residual feature fusion triple attention YOLO(RT-YOLO).Experiments showed that the mean average precision(mAP)ofRT-YOLO is increased from 57.2%to 60.8%on the vehicle detection in aerialimage(VEDAI)dataset,and the mAP is also increased by 1.7%on the remotesensing object detection(RSOD)dataset.The results show that theRT-YOLOoutperforms other mainstream models in UAV aerial image object detection.
文摘Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.
文摘Attention deficit hyperactivity disorder(ADHD) is the most common neurodevelopmental disorder in children and adolescents, with prevalence ranging between 5% and 12% in the developed countries. Tic disorders(TD) are common co-morbidities in paediatric ADHD patients with or without pharmacotherapy treatment. There has been conflicting evidence of the role of psychostimulants in either precipitating or exacerbating TDs in ADHD patients. We carried out a literature review relating to the management of TDs in children and adolescents with ADHD through a comprehensive search of MEDLINE, EMBASE, CINAHL and Cochrane databases. No quantitative synthesis(meta-analysis) was deemed appropriate. Metaanalysis of controlled trials does not support an association between new onset or worsening of tics and normal doses of psychostimulant use. Supratherapeutic doses of dextroamphetamine have been shown to exacerbate TD. Most tics are mild or moderate and respond to psychoeducation and behavioural management. Level A evidence support the use of alpha adrenergic agonists, including Clonidine and Guanfacine, reuptake noradrenenaline inhibitors(Atomoxetine) and stimulants(Methylphenidate and Dexamphetamines) for the treatment of Tics and comorbid ADHD. Priority should be given to the management of co-morbid Tourette's syndrome(TS) or severely disabling tics in children and adolescents with ADHD. Severe TDs may require antipsychotic treatment. Antipsychotics, especially Aripiprazole, are safe and effective treatment for TS or severe Tics, but they only moderately control the co-occurring ADHD symptomatology. Short vignettes of different common clinical scenarios are presented to help clinicians determine the most appropriate treatment to consider in each patient presenting with ADHD and co-morbid TDs.
文摘A growing number of oil and gas reservoirs have been discovered in granite and metamorphic crystallized rock areas. Statistics show that, about 157 oil and gas fields were found in crystallized bedrocks, with oil reserves of 5048x 10^8 t, and gas reserves of 2681x10^8m3. Among the discovered industrial oil and gas fields hosted in crystallized rocks, most occurred in granite rocks, occupying 40% in quantity and 75% in reserves, followed by those hosted in mafic and ultra-mafic rocks （about 3%）, and then tbllowed by those in volcanic rocks and metamorphic rocks.
基金This work has been funded by the Faculty Research Grants,Augustana College,Rock Island Illinois,USA,Initials of the Author:TKM,website:https://www.augustana.edu/aboutus/offices/academic-affairs/scholarship-grants.
文摘Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor symptoms.Among motor symptoms,one of the major disabling symptom is Freezing of Gait(FoG)that affects the daily standard of living of PD patients.Available treatments target to improve the symptoms of PD.Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual.This work proposed a novel attention-basedmodel for the detection of FoG events and PD,andmeasuring the intensity of PD on the United Parkinson’s Disease Rating Scale.Two separate datasets,that is,UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective problems.The results show a definite rise in the various performance metrics when compared to landmark models on these problems using these datasets.These results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected problem.High valueswere obtained for various performance metrics like accuracy of 98.74%for detection FoG,98.72%for detection of PD and 98.05%for measuring the intensity of PD on UPDRS.The model was also analyzed for robustness against noisy samples,where also model exhibited consistent performance.These results strongly suggest that the proposed model provides a better classification method for selected problem.
文摘Alzheimer’s Disease (AD) is characterized by an early and significant memory impairment, and progresses to affect other cognitive domains. Impairments in Focused Attention (FA) have been observed in patients diagnosed with mild AD. A functional magnetic resonance imaging (fMRI) Stroop paradigm with verbal responses was used to investigate the neural correlates of FA in AD patients. Twenty-one patients diagnosed with mild AD performed a verbal Stroop—fMRI paradigm. Colour words were printed in an incongruent ink colour. Series 1 consisted of four blocks “Read the word” followed by four blocks “Say the colour of the ink”;Series 2 alternated between the two conditions. Functional data were analyzed using SPM5 to detect anatomical areas with significant signal intensity differences between the conditions. Within-group analyses of the colour minus word contrast yielded significant activation in the following left hemisphere regions: precentral gyrus, inferior frontal gyrus, fusiform gyrus and supplementary motor area (p < 0.05, uncorrected). Relative to cognitively normal older adults who underwent the same experimental task, Stroop performance was significantly worse in AD patients. The fMRI task yielded similar activated brain regions between the two groups. The use of verbal responses in this novel fMRI Stroop task avoids the confusion and memorizing of button locations seen with the manual response modality, allowing the neural correlates of FA to be investigated in AD patients.
基金Supported by AG030646 and the Jeffress Memorial Trust
文摘Basal forebrain corticopetal cholinergic neurons are known to be necessary for normal attentional process-ing. Alterations of cholinergic system functioning have been associated with several neuropsychiatric diseases, such as Alzheimer’s disease and schizophrenia, in which attentional dysfunction is thought to be a key contrib-uting factor. Loss of cortical cholinergic inputs impairs performance in attention-demanding tasks. Moreover, measures of acetylcholine with microdialysis and, more recently, of choline with enzyme-coated microelectrodes have begun to elucidate the precise cognitive demands that activate the cholinergic system on distinct time scales. However, the receptor actions following acetyl-choline release under attentionally-challenging condi-tions are only beginning to be understood. The present review is designed to summarize the evidence regarding the actions of acetylcholine at muscarinic and nicotinic receptors under cognitively challenging conditions in order to evaluate the functions mediated by these two different cholinergic receptor classes. Moreover, evi-dence that supports beneficial effects of muscarinic muscarinic-1 receptor agonists and selective nicotinic receptor subtype agonists for cognitive processing will be discussed. Finally, some challenges and limitations of targeting the cholinergic system for treating cognitive defcits along with future research directions will be mentioned. In conclusion, multiple aspects of cholinergic neurotransmission must be considered when attempting to restore function of this neuromodulatory system.
