Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.
Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany.
J Neurosci. 2020 Nov 18;40(47):9028-9042. doi: 10.1523/JNEUROSCI.0897-20.2020. Epub 2020 Oct 12.
Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity in psychiatric disorders. However, noninvasive neuroimaging techniques such as magnetic resonance spectroscopy (MRS) may cover anatomically and functionally distinct areas, such as and of the pregenual anterior cingulate cortex (pgACC). Here, we aimed to overcome this low spatial specificity of MRS by predicting local glutamate and GABA based on functional characteristics and neuroanatomy in a sample of 88 human participants (35 females), using complementary machine learning approaches. Functional connectivity profiles of pgACC area predicted pgACC glutamate better than chance ( = 0.324) and explained more variance compared with area using both elastic net and partial least-squares regression. In contrast, GABA could not be robustly predicted. To summarize, machine learning helps exploit the high resolution of fMRI to improve the interpretation of local neurometabolism. Our augmented multimodal imaging analysis can deliver novel insights into neurobiology by using complementary information. Magnetic resonance spectroscopy (MRS) measures local glutamate and GABA noninvasively. However, conventional MRS requires large voxels compared with fMRI, because of its inherently low signal-to-noise ratio. Consequently, a single MRS voxel may cover areas with distinct cytoarchitecture. In the largest multimodal 7 tesla machine learning study to date, we overcome this limitation by capitalizing on the spatial resolution of fMRI to predict local neurotransmitters in the PFC. Critically, we found that prefrontal glutamate could be robustly and exclusively predicted from the functional connectivity fingerprint of one of two anatomically and functionally defined areas that form the pregenual anterior cingulate cortex. Our approach provides greater spatial specificity on neurotransmitter levels, potentially improving the understanding of altered functional connectivity in mental disorders.
局部神经递质测量为精神障碍改变功能连接的神经生物学变化提供了关键见解。然而，磁共振波谱（MRS）等非侵入性神经影像学技术可能涵盖解剖和功能上不同的区域，例如前扣带回皮质的吻侧前（pgACC）的 和 。在这里，我们旨在通过在 88 名人类参与者（35 名女性）的样本中使用互补的机器学习方法，根据功能特征和神经解剖结构，预测局部谷氨酸和 GABA，从而克服 MRS 的低空间特异性。pgACC 区域的功能连接特征可以比随机更好地预测 pgACC 谷氨酸（ = 0.324），并且与使用弹性网和偏最小二乘回归的区域相比，可以解释更多的方差。相比之下，GABA 不能被稳健地预测。总之，机器学习有助于利用 fMRI 的高分辨率来改善对局部神经代谢的解释。我们的增强型多模态成像分析可以通过使用互补信息为神经生物学提供新的见解。磁共振波谱（MRS）可以非侵入性地测量局部谷氨酸和 GABA。然而，与 fMRI 相比，由于其固有的信噪比低，传统的 MRS 需要较大的体素。因此，单个 MRS 体素可能覆盖具有不同细胞结构的区域。在迄今为止最大的 7 特斯拉多模态机器学习研究中，我们通过利用 fMRI 的空间分辨率来预测 PFC 中的局部神经递质，克服了这一限制。至关重要的是，我们发现前额叶谷氨酸可以从两个解剖和功能定义的区域之一的功能连接特征中稳健且唯一地预测出来，这两个区域构成了前扣带皮质的吻侧前。我们的方法提供了更高的神经递质水平的空间特异性，可能有助于更好地理解精神障碍中的改变功能连接。
