Bajracharya Prerana, Mirzaeian Shiva, Fu Zening, Calhoun Vince, Shultz Sarah, Iraji Armin
bioRxiv. 2024 Nov 27:2024.11.27.625681. doi: 10.1101/2024.11.27.625681.
The human brain undergoes remarkable development with the first six postnatal months witnessing the most dramatic structural and functional changes, making this period critical for in-depth research. rsfMRI studies have identified intrinsic connectivity networks (ICNs), including the default mode network, in infants. Although early formation of these networks has been suggested, the specific identification and number of ICNs reported in infants vary widely, leading to inconclusive findings. In adults, ICNs have provided valuable insights into brain function, spanning various mental states and disorders. A recent study analyzed data from over 100,000 subjects and generated a template of 105 multi-scale ICNs enhancing replicability and generalizability across studies. Yet, the presence of these ICNs in infants has not been investigated. This study addresses this significant gap by evaluating the presence of these multi-scale ICNs in infants, offering critical insight into the early stages of brain development and establishing a baseline for longitudinal studies. To accomplish this goal, we employ two sets of analyses. First, we employ a fully data-driven approach to investigate the presence of these ICNs from infant data itself. Towards this aim, we also introduce burst independent component analysis (bICA), which provides reliable and unbiased network identification. The results reveal the presence of these multi-scale ICNs in infants, showing a high correlation with the template (rho > 0.5), highlighting the potential for longitudinal continuity in such studies. We next demonstrate that reference-informed ICA-based techniques can reliably estimate these ICNs in infants, highlighting the feasibility of leveraging the NeuroMark framework for robust brain network extraction. This approach not only enhances cross-study comparisons across lifespans but also facilitates the study of brain changes across different age ranges. In summary, our study highlights the novel discovery that the infant brain already possesses ICNs that are widely observed in older cohorts.
人类大脑在出生后的头六个月经历了显著的发育,这期间见证了最剧烈的结构和功能变化,使得这个时期对于深入研究至关重要。静息态功能磁共振成像(rsfMRI)研究已经在婴儿中识别出了内在连接网络(ICNs),包括默认模式网络。尽管有人提出这些网络在早期就已形成,但婴儿中报告的ICNs的具体识别和数量差异很大,导致研究结果尚无定论。在成年人中,ICNs为大脑功能提供了有价值的见解,涵盖了各种精神状态和疾病。最近的一项研究分析了超过10万名受试者的数据,并生成了一个包含105个多尺度ICNs的模板,提高了跨研究的可重复性和普遍性。然而,这些ICNs在婴儿中的存在尚未得到研究。本研究通过评估婴儿中这些多尺度ICNs的存在来填补这一重大空白,为大脑发育的早期阶段提供关键见解,并为纵向研究建立基线。为了实现这一目标,我们采用了两组分析。首先,我们采用完全数据驱动的方法,从婴儿数据本身来研究这些ICNs的存在。为了实现这一目标,我们还引入了突发独立成分分析(bICA),它提供了可靠且无偏的网络识别。结果揭示了婴儿中存在这些多尺度ICNs,与模板显示出高度相关性(rho > 0.5),突出了此类研究中纵向连续性的潜力。接下来,我们证明基于参考信息的独立成分分析技术可以可靠地估计婴儿中的这些ICNs,突出了利用NeuroMark框架进行稳健脑网络提取的可行性。这种方法不仅增强了跨寿命的跨研究比较,还便于研究不同年龄范围的大脑变化。总之,我们的研究突出了这一新颖发现,即婴儿大脑已经拥有在年龄较大的队列中广泛观察到的ICNs。