Alonso Sonsoles, Cocchi Luca, Hearne Luke J, Shine James M, Vidaurre Diego
Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Denmark.
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
Hum Brain Mapp. 2025 Mar;46(4):e70157. doi: 10.1002/hbm.70157.
To elucidate the neurobiological basis of cognition, which is dynamic and evolving, various methods have emerged to characterise time-varying functional connectivity (FC) and track the temporal evolution of functional networks. However, given a selection of regions, many of these methods are based on modelling all possible pairwise connections, diluting a potential focus of interest on individual connections. This is the case with the hidden Markov model (HMM), which relies on region-by-region covariance matrices across all pairs of selected regions, assuming that fluctuations in FC occur across all investigated connections; that is, that all connections are locked to the same temporal pattern. To address this limitation, we introduce Targeted Time-Varying FC (T-TVFC), a variant of the HMM that explicitly models the temporal fluctuations between two sets of regions in a targeted fashion, rather than across the entire connectivity matrix. In this study, we apply T-TVFC to both simulated and real-world data. Specifically, we investigate thalamocortical connectivity, hypothesising distinct temporal signatures compared to corticocortical networks. Given the thalamus's role as a critical hub, thalamocortical connections might contain unique information about cognitive processing that could be overlooked in a coarser representation. We tested these hypotheses on high-field functional magnetic resonance data from 60 participants engaged in a reasoning task with varying complexity levels. Our findings demonstrate that the time-varying interactions captured by T-TVFC contain task-related information not detected by more traditional decompositions.
为了阐明动态且不断演变的认知的神经生物学基础，出现了各种方法来表征时变功能连接（FC）并追踪功能网络的时间演变。然而，对于选定的区域，许多这些方法基于对所有可能的成对连接进行建模，从而淡化了对单个连接的潜在关注焦点。隐马尔可夫模型（HMM）就是这种情况，它依赖于所选区域所有成对区域之间逐区域的协方差矩阵，假设FC波动发生在所有研究的连接中；也就是说，所有连接都锁定在相同的时间模式上。为了解决这一局限性，我们引入了靶向时变FC（T-TVFC），它是HMM的一种变体，以靶向方式明确模拟两组区域之间的时间波动，而不是整个连接矩阵。在本研究中，我们将T-TVFC应用于模拟数据和实际数据。具体而言，我们研究丘脑皮质连接，假设与皮质皮质网络相比具有不同的时间特征。鉴于丘脑作为关键枢纽的作用，丘脑皮质连接可能包含有关认知处理的独特信息，而这些信息在更粗略的表征中可能会被忽略。我们在60名参与不同复杂程度推理任务的参与者的高场功能磁共振数据上检验了这些假设。我们的研究结果表明，T-TVFC捕获的时变相互作用包含了更传统分解方法未检测到的与任务相关的信息。
