The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment.Previous studies have suggested that there is an auditory“where”pathway in the cortex for proce...The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment.Previous studies have suggested that there is an auditory“where”pathway in the cortex for processing sound locations.The neural activation in regions along this pathway encodes sound locations by opponent hemifield coding,in which each unilateral region is activated by sounds coming from the contralateral hemifield.However,it is still unclear how these regions interact with each other to form a unified representation of the auditory space.In the present study,we investigated whether functional connectivity in the auditory“where”pathway encoded sound locations during passive listening.Participants underwent functional magnetic resonance imaging while passively listening to sounds from five distinct horizontal locations(−90°,−45°,0°,45°,90°).We were able to decode sound locations from the functional connectivity patterns of the“where”pathway.Furthermore,we found that such neural representation of sound locations was primarily based on the coding of sound lateralization angles to the frontal midline.In addition,whole-brain analysis indicated that functional connectivity between occipital regions and the primary auditory cortex also encoded sound locations by lateralization angles.Overall,our results reveal a lateralization-angle-based representation of sound locations encoded by functional connectivity patterns,which could add on the activation-based opponent hemifield coding to provide a more precise representation of the auditory space.展开更多
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia,such as voxel-based morphometry(VBM),tensor-based morphometry(TBM...A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia,such as voxel-based morphometry(VBM),tensor-based morphometry(TBM),and projection-based thickness(PBT),is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia.However,such studies are still lacking.Here,we performed VBM,TBM,and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls.We found that,although all methods detected wide-spread structural changes,different methods captured different information-only 10.35%of the grey matter changes in cortex were detected by all three methods,and VBM only detected 11.36%of the white matter changes detected by TBM.Further,pattern classification between patients and controls revealed that combining different measures improved the classification accuracy(81.9%),indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.展开更多
Although distinctive neural and physiological states are suggested to underlie the six basic emotions,basic emotions are often indistinguishable from functional magnetic resonance imaging(f MRI)voxelwise activation(VA...Although distinctive neural and physiological states are suggested to underlie the six basic emotions,basic emotions are often indistinguishable from functional magnetic resonance imaging(f MRI)voxelwise activation(VA)patterns.Here,we hypothesize that functional connectivity(FC)patterns across brain regions may contain emotion-representation information beyond VA patterns.We collected whole-brain f MRI data while human participants viewed pictures of faces expressing one of the six basic emotions(i.e.,anger,disgust,fear,happiness,sadness,and surprise)or showing neutral expressions.We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space.Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions.An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified.Finally,we demonstrated that within the same brain regions,FC-based decoding consistently performed better than VA-based decoding.Taken together,our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.展开更多
Background： Electroconvulsive therapy （ECT） can alleviate the symptoms of treatment-resistant depression （TRD）. Functional network connectivity （FNC） is a newly developed method to investigate the brain＇s func...Background： Electroconvulsive therapy （ECT） can alleviate the symptoms of treatment-resistant depression （TRD）. Functional network connectivity （FNC） is a newly developed method to investigate the brain＇s functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients. Methods： This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis （G1G-ICA） was used to compute subject-specific functional networks （FNs）. Grassmann maniibld and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks＇ connectivity patterns （FCP）. Pearson＇s correlation analysis was used to calculate the correlations between the FCP and ECT response. Results： A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ~ 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT （the Hamilton scores reduction rate was more than 50%）, response rate 51%. 8 FNs （anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network） were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response. Conclusions： The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to explore the mechanisms underlying ECT＇s effects on depression and explore the specific predictors of the effects of ECT based on the pre-ECT treatment magnetic resonance imaging.展开更多
基金supported by the National Key Research and Development Program of China(2023YFF1203502)the National Natural Science Foundation of China(62171300,62301343,and 62394314)+1 种基金the Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203109)the Capital Medical University Research and Development Fund(PYZ22027).
文摘The ability to localize sound sources rapidly allows human beings to efficiently understand the surrounding environment.Previous studies have suggested that there is an auditory“where”pathway in the cortex for processing sound locations.The neural activation in regions along this pathway encodes sound locations by opponent hemifield coding,in which each unilateral region is activated by sounds coming from the contralateral hemifield.However,it is still unclear how these regions interact with each other to form a unified representation of the auditory space.In the present study,we investigated whether functional connectivity in the auditory“where”pathway encoded sound locations during passive listening.Participants underwent functional magnetic resonance imaging while passively listening to sounds from five distinct horizontal locations(−90°,−45°,0°,45°,90°).We were able to decode sound locations from the functional connectivity patterns of the“where”pathway.Furthermore,we found that such neural representation of sound locations was primarily based on the coding of sound lateralization angles to the frontal midline.In addition,whole-brain analysis indicated that functional connectivity between occipital regions and the primary auditory cortex also encoded sound locations by lateralization angles.Overall,our results reveal a lateralization-angle-based representation of sound locations encoded by functional connectivity patterns,which could add on the activation-based opponent hemifield coding to provide a more precise representation of the auditory space.
基金This work was supported by the National Key Research and Development Program of China(2017 YFC0909201 and 2018YFC1314300)the National Natural Science Foundation of China(81571659,81971694,81971599,81771818,81425013,and 81871052)and the Tianjin Key Technology R&D Program(17ZXMFSY00090).
文摘A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia,such as voxel-based morphometry(VBM),tensor-based morphometry(TBM),and projection-based thickness(PBT),is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia.However,such studies are still lacking.Here,we performed VBM,TBM,and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls.We found that,although all methods detected wide-spread structural changes,different methods captured different information-only 10.35%of the grey matter changes in cortex were detected by all three methods,and VBM only detected 11.36%of the white matter changes detected by TBM.Further,pattern classification between patients and controls revealed that combining different measures improved the classification accuracy(81.9%),indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
基金supported by the National Natural Science Foundation of China(31930053)the National Science and Technology Innovation 2030 Major Program(2022ZD0204802)+2 种基金Beijing Academy of Artificial Intelligence(BAAI)Project funded by China Postdoctoral Science Foundation(2022M710210)the Fundamental Research Funds for the Central Universities(2021FZZX001-06)。
文摘Although distinctive neural and physiological states are suggested to underlie the six basic emotions,basic emotions are often indistinguishable from functional magnetic resonance imaging(f MRI)voxelwise activation(VA)patterns.Here,we hypothesize that functional connectivity(FC)patterns across brain regions may contain emotion-representation information beyond VA patterns.We collected whole-brain f MRI data while human participants viewed pictures of faces expressing one of the six basic emotions(i.e.,anger,disgust,fear,happiness,sadness,and surprise)or showing neutral expressions.We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space.Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions.An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified.Finally,we demonstrated that within the same brain regions,FC-based decoding consistently performed better than VA-based decoding.Taken together,our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.
文摘Background： Electroconvulsive therapy （ECT） can alleviate the symptoms of treatment-resistant depression （TRD）. Functional network connectivity （FNC） is a newly developed method to investigate the brain＇s functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients. Methods： This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis （G1G-ICA） was used to compute subject-specific functional networks （FNs）. Grassmann maniibld and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks＇ connectivity patterns （FCP）. Pearson＇s correlation analysis was used to calculate the correlations between the FCP and ECT response. Results： A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ~ 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT （the Hamilton scores reduction rate was more than 50%）, response rate 51%. 8 FNs （anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network） were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response. Conclusions： The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to explore the mechanisms underlying ECT＇s effects on depression and explore the specific predictors of the effects of ECT based on the pre-ECT treatment magnetic resonance imaging.
