Functional connectivity in fMRI: A modeling approach for estimation and for relating to local circuits.
Functional connectivity in fMRI: A modeling approach for estimation and for relating to local circuits.
复制标题
fMRI 中的功能连接：一种用于估计和与局部电路相关的建模方法。
DOI：
10.1016/j.neuroimage.2006.10.008
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发表时间：
2007
期刊：
影响因子：
5.7
通讯作者：
Tagamets,M-A
中科院分区：
文献类型：
--
作者：
Winder,Ransom;Cortes,CarlosR;Reggia,JamesA;Tagamets,M-A
虽然在将神经元事件与大脑新陈代谢和血液流动的变化联系起来方面已经取得了进展，但从潜在的大脑回路的角度来解释功能神经成像数据仍然知之甚少。对区域之间和区域内的连接模式进行计算建模可能有助于这种解释。我们提出了腹侧视觉通路及其相关功能联系的神经网络模型。这包括一种新的学习方法，该方法调整区域间连接的大小，以便与任意功能磁共振成像(FMRI)数据集的实验结果相匹配。我们证明了当在具有已知的、随机选择的连接权的模型系统上训练时，该方法能够找到合适的连接强度。然后，我们使用这种方法来检查人类受试者的一对一匹配任务的fMRI结果，包括健康受试者和精神分裂症患者。通过学习方法发现的结果支持了之前的发现，即精神分裂症患者左侧颞叶和额叶皮质之间的连接断开，右侧颞叶-额叶连接强度随之增加。然后，我们证明，这种连接中断可能是由于额叶皮质局部递归回路减少所致。该方法通过包括局部电路和区域间连接的特征，例如地形和稀疏性，以及总连接强度，扩展了当前可用于从人体成像数据估计功能连接性的方法。此外，我们的结果表明，精神分裂症的额颞叶功能中断可能是由于额叶皮质内局部突触连接减少而不是区域间连接受损所致。
Although progress has been made in relating neuronal events to changes in brain metabolism and blood flow, the interpretation of functional neuroimaging data in terms of the underlying brain circuits is still poorly understood. Computational modeling of connection patterns both among and within regions can be helpful in this interpretation. We present a neural network model of the ventral visual pathway and its relevant functional connections. This includes a new learning method that adjusts the magnitude of interregional connections in order to match experimental results of an arbitrary functional magnetic resonance imaging (fMRI) data set. We demonstrate that this method finds the appropriate connection strengths when trained on a model system with known, randomly chosen connection weights. We then use the method for examining fMRI results from a one-back matching task in human subjects, both healthy and those with schizophrenia. The results discovered by the learning method support previous findings of a disconnection between left temporal and frontal cortices in the group with schizophrenia and a concomitant increase of right-sided temporo-frontal connection strengths. We then demonstrate that the disconnection may be explained by reduced local recurrent circuitry in frontal cortex. This method extends currently available methods for estimating functional connectivity from human imaging data by including both local circuits and features of interregional connections, such as topography and sparseness, in addition to total connection strengths. Furthermore, our results suggest how fronto-temporal functional disconnection in schizophrenia can result from reduced local synaptic connections within frontal cortex rather than compromised interregional connections.