Thorp John N, Gasser Camille, Blessing Esther, Davachi Lila
Department of Psychology, Columbia University, New York, New York 10027.
Department of Psychiatry, New York University Langone Medical Center, New York University Grossman School of Medicine, New York, New York 10016.
J Neurosci. 2022 Sep 28;42(39):7431-7441. doi: 10.1523/JNEUROSCI.0269-22.2022.
A particularly elusive puzzle concerning the hippocampus is how the structural differences along its long anteroposterior axis might beget meaningful functional differences, particularly in terms of the granularity of information processing. One measure posits to quantify this granularity by calculating the average statistical independence of the BOLD signal across neighboring voxels, or intervoxel similarity (IVS), and has shown the anterior hippocampus to process coarser-grained information than the posterior hippocampus. This measure, however, has yielded opposing results in studies of developmental and healthy aging samples, which also varied in fMRI acquisition parameters and hippocampal parcellation methods. To reconcile these findings, we measured IVS across two separate resting-state fMRI acquisitions and compared the results across many of the most widely used parcellation methods in a large young-adult sample of male and female humans (Acquisition 1, = 233; Acquisition 2, = 176). Finding conflicting results across acquisitions and parcellations, we reasoned that a data-driven approach to hippocampal parcellation is necessary. To this end, we implemented a group masked independent components analysis to identify functional subunits of the hippocampus, most notably separating the anterior hippocampus into separate anterior-medial, anterior-lateral, and posteroanterior-lateral components. Measuring IVS across these components revealed a decrease in IVS along the medial-lateral axis of the anterior hippocampus but an increase from anterior to posterior. We conclude that intervoxel similarity is deeply affected by parcellation and that grounding one's parcellation in a functionally informed approach might allow for a more complex and reliable characterization of the hippocampus. Processing information along hierarchical scales of granularity is critical for many of the feats of cognition considered most human. Recently, the changes in structure, cortical connectivity, and apparent functional properties across parcels of the hippocampal long axis have been hypothesized to underlie this hierarchical gradient in information processing. We show here, however, that the choice of parcellation method itself drastically affects one particular measure of granularity across the hippocampus and that a functionally informed approach to parcellation reveals gradients both within the anterior hippocampus and in nonlinear form across the long axis. These results point to the issue of parcellation as a critical one in the study of the hippocampus and reorient interpretation of existing results.
一个关于海马体的特别难以捉摸的谜题是，其沿前后长轴的结构差异如何产生有意义的功能差异，特别是在信息处理的粒度方面。一种测量方法通过计算相邻体素间BOLD信号的平均统计独立性或体素间相似性（IVS）来量化这种粒度，并已表明前海马体比后海马体处理的信息粒度更粗。然而，在发育样本和健康衰老样本的研究中，该测量方法得出了相反的结果，这些研究在功能磁共振成像采集参数和海马体分割方法上也存在差异。为了协调这些发现，我们在两次单独的静息态功能磁共振成像采集中测量了IVS，并在一个由大量年轻成年男女组成的样本中比较了许多最广泛使用的分割方法的结果（采集1，n = 233；采集2，n = 176）。在不同的采集和分割中发现了相互矛盾的结果，我们认为有必要采用数据驱动的方法来进行海马体分割。为此，我们实施了组掩蔽独立成分分析，以识别海马体的功能亚单位，最显著的是将前海马体分为单独的前内侧、前外侧和后前外侧成分。测量这些成分之间的IVS显示，沿前海马体的内侧-外侧轴IVS降低，但从前向后增加。我们得出结论，体素间相似性深受分割的影响，基于功能的分割方法可能会对海马体进行更复杂、更可靠的表征。沿粒度的层次尺度处理信息对于许多被认为最具人类特征的认知壮举至关重要。最近，有人假设海马体长轴各部分的结构、皮质连接性和明显的功能特性变化是这种信息处理层次梯度的基础。然而，我们在此表明，分割方法本身的选择会极大地影响海马体粒度的一种特定测量，基于功能的分割方法揭示了前海马体内的梯度以及长轴上的非线性形式的梯度。这些结果表明，分割问题是海马体研究中的一个关键问题，并重新定向了对现有结果的解释。
