Predicting neurologic recovery after severe acute brain injury using resting-state networks.

OBJECTIVE: There is a lack of reliable tools used to predict functional recovery in unresponsive patients following a severe brain injury. The objective of the study is to evaluate the prognostic utility of resting-state functional magnetic resonance imaging for predicting good neurologic recovery in unresponsive patients with severe brain injury in the intensive-care unit. METHODS: Each patient underwent a 5.5-min resting-state scan and ten resting-state networks were extracted via independent component analysis. The Glasgow Outcome Scale was used to classify patients into good and poor outcome groups. The Nearest Centroid classifier used each patient's ten resting-state network values to predict best neurologic outcome within 6 months post-injury. RESULTS: Of the 25 patients enrolled (mean age = 43.68, range = [19-69]; GCS ≤ 9; 6 females), 10 had good and 15 had poor outcome. The classifier correctly and confidently predicted 8/10 patients with good and 12/15 patients with poor outcome (mean = 0.793, CI = [0.700, 0.886], Z = 2.843, p = 0.002). The prediction performance was largely determined by three visual (medial: Z = 3.11, p = 0.002; occipital pole: Z = 2.44, p = 0.015; lateral: Z = 2.85, p = 0.004) and the left frontoparietal network (Z = 2.179, p = 0.029). DISCUSSION: Our approach correctly identified good functional outcome with higher sensitivity (80%) than traditional prognostic measures. By revealing preserved networks in the absence of discernible behavioral signs, functional connectivity may aid in the prognostic process and affect the outcome of discussions surrounding withdrawal of life-sustaining measures.

Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study.

Significance: Brain fingerprinting refers to identifying participants based on their functional patterns. Despite its success with functional magnetic resonance imaging (fMRI), brain fingerprinting with functional near-infrared spectroscopy (fNIRS) still lacks adequate validation. Aim: We investigated how fNIRS-specific acquisition features (limited spatial information and nonneural contributions) influence resting-state functional connectivity (rsFC) patterns at the intra-subject level and, therefore, brain fingerprinting. Approach: We performed multiple simultaneous fNIRS and fMRI measurements in 29 healthy participants at rest. Data were preprocessed following the best practices, including the removal of motion artifacts and global physiology. The rsFC maps were extracted with the Pearson correlation coefficient. Brain fingerprinting was tested with pairwise metrics and a simple linear classifier. Results: Our results show that average classification accuracy with fNIRS ranges from 75% to 98%, depending on the number of runs and brain regions used for classification. Under the right conditions, brain fingerprinting with fNIRS is close to the 99.9% accuracy found with fMRI. Overall, the classification accuracy is more impacted by the number of runs and the spatial coverage than the choice of the classification algorithm. Conclusions: This work provides evidence that brain fingerprinting with fNIRS is robust and reliable for extracting unique individual features at the intra-subject level once relevant spatiotemporal constraints are correctly employed.

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy.

Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.

Functional near-infrared spectroscopy for speech protocols: characterization of motion artifacts and guidelines for improving data analysis.

Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.

Association between hemodynamic activity and motor performance in six-month-old full-term and preterm infants: a functional near-infrared spectroscopy study.

This study aimed to assess task-induced activation in motor cortex and its association with motor performance in full-term and preterm born infants at six months old. A cross-sectional study of 73 six-month-old infants was conducted (35 full-term and 38 preterm infants). Motor performance was assessed using the Bayley Scales of Infant Development third edition-Bayley-III. Brain hemodynamic activity during motor task was measured by functional near-infrared spectroscopy (fNIRS). Motor performance was similar in full-term and preterm infants. However, differences in hemodynamic response were identified. Full terms showed a more homogeneous unilateral and contralateral activated area, whereas in preterm-born the activation response was predominantly bilateral. The full-term group also exhibited a shorter latency for the hemodynamic response than the preterm group. Hemodynamic activity in the left sensorimotor region was positively associated with motor performance measured by Bayley-III. The results highlight the adequacy of fNIRS to assess differences in task-induced activation in sensorimotor cortex between groups. The association between motor performance and the hemodynamic activity require further investigation and suggest that fNIRS can become a suitable auxiliary tool to investigate aspects of neural basis on early development of motor abilities.

Resting state connectivity patterns with near-infrared spectroscopy data of the whole head.

Resting state cerebral dynamics has been a useful approach to explore the brain's functional organization. In this study, we employed graph theory to deeply investigate resting state functional connectivity (rs-FC) as measured by near-infrared spectroscopy (NIRS). Our results suggest that network parameters are very similar across time and subjects. We also identified the most frequent connections between brain regions and the main hubs that participate in the spontaneous activity of brain hemodynamics. Similar to previous findings, we verified that symmetrically located brain areas are highly connected. Overall, our results introduce new insights in NIRS-based functional connectivity at rest.