Motivation: In many fMRI studies, respiratory signals are often missing or of
poor quality. Therefore, it could be highly beneficial to have a tool to
extract respiratory variation (RV) waveforms directly from fMRI data without
the need for peripheral recording devices.
Goal(s): Investigate the hypothesis that head motion parameters contain
valuable information regarding respiratory patter, which can help machine
learning algorithms estimate the RV waveform.
Approach: This study proposes a CNN model for reconstruction of RV waveforms
using head motion parameters and BOLD signals.
Results: This study showed that combining head motion parameters with BOLD
signals enhances RV waveform estimation.
Impact: It is expected that application of the proposed method will lower the
cost of fMRI studies, reduce complexity, and decrease the burden on
participants as they will not be required to wear a respiratory bellows.

该研究提出了一种使用头部运动参数和 BOLD 信号来重构 RV 波形的 CNN 模型，结果表明将头部运动参数与 BOLD 信号相结合可以增强 RV 波形估计。

基于机器学习的 BOLD fMRI 和头动参数对健康成年人呼吸波动的估计

Machine Learning-based Estimation of Respiratory Fluctuations in a  Healthy Adult Population using BOLD fMRI and Head Motion Parameters

Functional MRI (fMRI) is widely used to examine brain functionality by
detecting alteration in oxygenated blood flow that arises with brain activity.
This work aims to investigate the neurological variation of human brain
responses during viewing of images with varied complexity using fMRI time
series (TS) analysis. Publicly available BOLD5000 dataset is used for this
purpose which contains fMRI scans while viewing 5254 distinct images of diverse
categories, drawn from three standard computer vision datasets: COCO, Imagenet
and SUN. To understand vision, it is important to study how brain functions
while looking at images of diverse complexities. Our first study employs
classical machine learning and deep learning strategies to classify image
complexity-specific fMRI TS, represents instances when images from COCO,
Imagenet and SUN datasets are seen. The implementation of this classification
across visual datasets holds great significance, as it provides valuable
insights into the fluctuations in BOLD signals when perceiving images of
varying complexities. Subsequently, temporal semantic segmentation is also
performed on whole fMRI TS to segment these time instances. The obtained result
of this analysis has established a baseline in studying how differently human
brain functions while looking into images of diverse complexities. Therefore,
accurate identification and distinguishing of variations in BOLD signals from
fMRI TS data serves as a critical initial step in vision studies, providing
insightful explanations for how static images with diverse complexities are
perceived.

通过使用 fMRI 时间序列分析，本研究旨在研究人类大脑对不同复杂度图像的神经变化，通过对公开的 BOLD5000 数据集进行图像分类和时间语义分割的实验，以揭示人类大脑在感知图像时 BOLD 信号的波动和变化。

基于人类视觉的 fMRI 时间序列分析，探究观看复杂度不同图像时 BOLD 响应的变化

Investigating the changes in BOLD responses during viewing of images  with varied complexity: An fMRI time-series based analysis on human vision

In many fMRI studies, respiratory signals are unavailable or do not have
acceptable quality. Consequently, the direct removal of low-frequency
respiratory variations from BOLD signals is not possible. This study proposes a
one-dimensional CNN model for reconstruction of two respiratory measures, RV
and RVT. Results show that a CNN can capture informative features from resting
BOLD signals and reconstruct realistic RV and RVT timeseries. It is expected
that application of the proposed method will lower the cost of fMRI studies,
reduce complexity, and decrease the burden on participants as they will not be
required to wear a respiratory bellows.

本研究提出了一种一维 CNN 模型用于重建两个呼吸参数，RV 和 RVT，结果表明 CNN 可以从静息状态的 BOLD 信号中捕获有用的特征并重建真实的 RV 和 RVT 时间序列，预期这种方法的应用将降低 fMRI 研究的成本、减少复杂性并减轻参与者的负担，因为他们不需要佩戴呼吸器。

使用卷积神经网络 (CNN) 结合 BOLD-fMRI 计算人体连接组发育队列中的单位时间呼吸体积 (RTV) 和呼吸变异度 (RV)

Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and  Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the  Human Connectome Development Cohort