Functional Neuroimaging Modalities Research Articles

Abnormal resting-state hyperconnectivity in schizophrenia: A whole-head near-infrared spectroscopy study

Near-infrared spectroscopy (NIRS) is a noninvasive functional neuroimaging modality that can detect changes in blood oxygenation levels by tracking cortical neural activity. We recorded the resting-state brain activity of 24 individuals with schizophrenia and 90 healthy controls for 8 min using a whole-head NIRS arrangement and then used partial correlation analysis to estimate the resting-state functional connectivity (RSFC) between 17 cortical regions. We found that the RSFC between the bilateral orbitofrontal cortices (OFCs) and between the right temporal and parietal lobes was significantly higher in patients with schizophrenia than in healthy controls. The RSFC between the bilateral OFCs was positively correlated with negative symptom severity, whereas the RSFC between the right temporal and parietal lobes was positively correlated with the chlorpromazine equivalent for antipsychotics prescribed to patients with schizophrenia. This finding was consistent with that for the RSFC calculated using the anterior 52-channel signals. Our results suggest that NIRS-based RSFC measurements have potential clinical applications.

Task-based functional neuroimaging in infants: a systematic review.

Infancy is characterized by rapid neurological transformations leading to consolidation of lifelong function capabilities. Studying the infant brain is crucial for understanding how these mechanisms develop during this sensitive period. We review the neuroimaging modalities used with infants in stimulus-induced activity paradigms specifically, for the unique opportunity the latter provide for assessment of brain function. Conducted a systematic review of literature published between 1977-2021, via a comprehensive search of four major databases. Standardized appraisal tools and inclusion/exclusion criteria were set according to the PRISMA guidelines. Two-hundred and thirteen papers met the criteria of the review process. The results show clear evidence of overall cumulative growth in the number of infant functional neuroimaging studies, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to be the most utilized and fastest growing modalities with behaving infants. However, there is a high level of exclusion rates associated with technical limitations, leading to limited motor control studies (about ) in this population. Although the use of functional neuroimaging modalities with infants increases, there are impediments to effective adoption of existing technologies with this population. Developing new imaging modalities and experimental designs to monitor brain activity in awake and behaving infants is vital.

Optimization of Signal Space Separation for Optically Pumped Magnetometer in Magnetoencephalography.

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.

Studying the Swallow using Surface Electroencephalography: A Systematic Review

Background: Swallowing is an important function for life sustenance but our understanding of its neural organization in the human cortex is not yet fully explored. Electroencephalography is a functional neuroimaging modality that already has and could further contribute to that direction. In this review, we have sought to gather and present findings from studies that used the EEG on healthy individuals for the investigation of the cortical mechanisms that moderate deglutition. Methods and Materials: Four databases were searched for studies that used the EEG as their main research modality on healthy individuals and administered various stimuli for them to swallow. The risk of bias assessment of the studies was conducted using the NIH rating scale for observational studies. Results: Our search yielded 393 studies in total and nine of them were included in the final discussion. The risk of bias assessment showed good quality of the studies. The nine studies were presented in a table following an adapted PICOS outline depicting the population, the comparisons, the means of measurement and the outcomes. Conclusions: Researchers studied the morphology of the signal before, during and after the swallow and its discrepancies in frequency in relation to stimuli alterations. Discrepancies in methodology and concordance with previous research are discussed.

BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

Brain diseases, including tumors and mental and neurological disorders, seriously threaten the health and well-being of millions of people worldwide. Structural and functional neuroimaging modalities are commonly used by physicians to aid the diagnosis of brain diseases. In clinical settings, specialist doctors typically fuse the magnetic resonance imaging (MRI) data with other neuroimaging modalities for brain disease detection. As these two approaches offer complementary information, fusing these neuroimaging modalities helps physicians accurately diagnose brain diseases. Typically, fusion is performed between a functional and a structural neuroimaging modality. Because the functional modality can complement the structural modality information, thus improving the performance for the diagnosis of brain diseases by specialists. However, analyzing the fusion of neuroimaging modalities is difficult for specialist doctors. Deep Learning (DL) is a branch of artificial intelligence that has shown superior performances compared to more conventional methods in tasks such as brain disease detection from neuroimaging modalities. This work presents a comprehensive review paper in the field of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and Autoencoders (AEs). First, neuroimaging modalities and the need for fusion are discussed. Then, review papers published in the field of neuroimaging multimodalities using AI techniques are explored. Moreover, fusion levels based on DL methods, including input, layer, and decision, with related studies conducted on diagnosing brain diseases, are discussed. Other sections present the most important challenges for diagnosing brain diseases from the fusion of neuroimaging modalities. In the discussion section, the details of previous research on the fusion of neuroimaging modalities based on MRI and DL models are reported. In the following, the most important future directions include Datasets, DA, imbalanced data, DL models, explainable AI, and hardware resources are presented. Finally, the main findings of this study are presented in the conclusion section.

Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional neuroimaging modality that has been widely used to investigate functional connectomes in the brain. Since noise and artifacts generated by non-neuronal physiological activities are predominant in raw rs-fMRI data, effective noise removal is one of the most important preprocessing steps prior to any subsequent analysis. For rs-fMRI denoising, a common trend is to decompose rs-fMRI data into multiple components and then regress out noise-related components. Therefore, various machine learning techniques have been used in such analyses with predefined procedures and manually engineered features. However, the lack of a universal definition of a noise-related source or artifact complicates manual feature engineering. Manual feature selection can result in the failure to capture unknown types of noise. Furthermore, the possibility that the hand-crafted features will only work for the broader population (e.g., healthy adults) but not for “outliers” (e.g., infants or subjects that belong to a disease cohort) is quite high. In practice, we have limited knowledge of which features should be extracted; thus, multi-classifier assembly must be implemented to improve performance, although this process is quite time-consuming. However, in real rs-fMRI applications, fast and accurate automatic identification of noise-related components on different datasets is critical. To solve this problem, we propose a novel, automatic, and end-to-end deep learning framework dedicated to noise-related component identification via a faster and more effective multi-layer feature extraction strategy that learns deeply embedded spatio-temporal features of the components. In this study, we achieved remarkable performance on various rs-fMRI datasets, including multiple adult rs-fMRI datasets from different rs-fMRI studies and an infant rs-fMRI dataset, which is quite heterogeneous and differs from that of adults. Our proposed framework also dramatically increases the noise detection speed owing to its inherent ability for deep learning (< 1s for single-component classification). It can be easily integrated into any preprocessing pipeline, even those that do not use standard procedures but depend on alternative toolboxes.

Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements.

.SignificanceFunctional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications.AimConsidering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach.ApproachFirst, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method.ResultsUsing fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson’s correlation coefficient (). We found that the proposed method showed improvements in performance in SNR and with strong stability.ConclusionsThese results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.

A systematic review of resting-state and task-based fmri in juvenile myoclonic epilepsy.

Functional neuroimaging modalities have enhanced our understanding of juvenile myoclonic epilepsy (JME) underlying neural mechanisms. Due to its non-invasive, sensitive and analytical nature, functional magnetic resonance imaging (fMRI) provides valuable insights into relevant functional brain networks and their segregation and integration properties. We systematically reviewed the contribution of resting-state and task-based fMRI to the current understanding of the pathophysiology and the patterns of seizure propagation in JME Altogether, despite some discrepancies, functional findings suggest that corticothalamo-striato-cerebellar network along with default-mode network and salience network are the most affected networks in patients with JME. However, further studies are required to investigate the association between JME's main deficiencies, e.g., motor and cognitive deficiencies and fMRI findings. Moreover, simultaneous electroencephalography-fMRI (EEG-fMRI) studies indicate that alterations of these networks play a role in seizure modulation but fall short of identifying a causal relationship between altered functional properties and seizure propagation. This review highlights the complex pathophysiology of JME, which necessitates the design of more personalized diagnostic and therapeutic strategies in this group.

Utility of SPECT Functional Neuroimaging of Pain

Functional neuroimaging modalities vary in spatial and temporal resolution. One major limitation of most functional neuroimaging modalities is that only neural activation taking place inside the scanner can be imaged. This limitation makes functional neuroimaging in many clinical scenarios extremely difficult or impossible. The most commonly used radiopharmaceutical in Single Photon Emission Tomography (SPECT) functional brain imaging is Technetium 99 m-labeled Ethyl Cysteinate Dimer (ECD). ECD is a lipophilic compound with unique pharmacodynamics. It crosses the blood brain barrier and has high first pass extraction by the neurons proportional to regional brain perfusion at the time of injection. It reaches peak activity in the brain 1 min after injection and is then slowly cleared from the brain following a biexponential mode. This allows for a practical imaging window of 1 or 2 h after injection. In other words, it freezes a snapshot of brain perfusion at the time of injection that is kept and can be imaged later. This unique feature allows for designing functional brain imaging studies that do not require the patient to be inside the scanner at the time of brain activation. Functional brain imaging during severe burn wound care is an example that has been extensively studied using this technique. Not only does SPECT allow for imaging of brain activity under extreme pain conditions in clinical settings, but it also allows for imaging of brain activity modulation in response to analgesic maneuvers whether pharmacologic or non-traditional such as using virtual reality analgesia. Together with its utility in extreme situations, SPECTS is also helpful in investigating brain activation under typical pain conditions such as experimental controlled pain and chronic pain syndromes.

Cognitive Dysfunction in Systemic Lupus Erythematosus: Immunopathology, Clinical Manifestations, Neuroimaging and Management.

Cognitive dysfunction (CD) is a common yet often clinically subtle manifestation that considerably impacts the health-related quality of life in patients with systemic lupus erythaematosus (SLE). Given the inconsistencies in CD assessment and challenges in its attribution to SLE, the reported prevalence of CD differs widely, ranging from 3 to 88%. The clinical presentation of CD in SLE is non-specific and may manifest concurrently with overt neuropsychiatric illness such as psychosis or mood disorders or as isolated impairment of attention, working memory, executive dysfunction or processing speed. Despite the lack of standardized and sensitive neuropsychological tests and validated diagnostic biomarkers of CD in SLE, significant progress has been made in identifying pathogenic neural pathways and neuroimaging. Furthermore, several autoantibodies, cytokines, pro-inflammatory mediators and metabolic factors have been implicated in the pathogenesis of CD in SLE. Abrogation of the integrity of the blood-brain barrier (BBB) and ensuing autoantibody-mediated neurotoxicity, complement and microglial activation remains the widely accepted mechanism of SLE-related CD. Although several functional neuroimaging modalities have consistently demonstrated abnormalities that correlate with CD in SLE patients, a consensus remains to be reached as to their clinical utility in diagnosing CD. Given the multifactorial aetiology of CD, a multi-domain interventional approach that addresses the risk factors and disease mechanisms of CD in a concurrent fashion is the favourable therapeutic direction. While cognitive rehabilitation and exercise training remain important, specific pharmacological agents that target microglial activation and maintain the BBB integrity are potential candidates for the treatment of SLE-related CD.

Abnormality of Resting-State Functional Connectivity in Major Depressive Disorder: A Study With Whole-Head Near-Infrared Spectroscopy.

Near-infrared spectroscopy (NIRS) is a functional neuroimaging modality that has advantages in clinical usage. Previous functional magnetic resonance imaging (fMRI) studies have found that the resting-state functional connectivity (RSFC) of the default mode network (DMN) is increased, while the RSFC of the cognitive control network (CCN) is reduced in patients with major depressive disorder (MDD) compared with healthy controls. This study tested whether the NIRS-based RSFC measurements can detect the abnormalities in RSFC that have been associated with MDD in previous fMRI studies. We measured 8 min of resting-state brain activity in 34 individuals with MDD and 78 age- and gender-matched healthy controls using a whole-head NIRS system. We applied a previously established partial correlation analysis for estimating RSFCs between the 17 cortical regions. We found that MDD patients had a lower RSFC between the left dorsolateral prefrontal cortex and the parietal lobe that comprise the CCN, and a higher RSFC between the right orbitofrontal cortex and ventrolateral prefrontal cortex, compared to those in healthy controls. The RSFC strength of the left CCN was negatively correlated with the severity of depressive symptoms and the dose of antipsychotic medication and positively correlated with the level of social functioning. The results of this study suggest that NIRS-based measurements of RSFCs have potential clinical applications.

Construction and validation of a database of head models for functional imaging of the neonatal brain.

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age‐specific head models. Although an age‐appropriate population‐level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual‐level datasets at ages 29–44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra‐cerebral tissue against manual segmentation. We have also conducted a leave‐one‐out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best‐matching individual from the database in place of a subject‐specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.

Monitoring of Stimulus Evoked Murine Somatosensory Cortex Hemodynamic Activity With Volumetric Multi-Spectral Optoacoustic Tomography.

Sensory stimulation is an attractive paradigm for studying brain activity using various optical-, ultrasound- and MRI-based functional neuroimaging methods. Optoacoustics has been recently suggested as a powerful new tool for scalable mapping of multiple hemodynamic parameters with rich contrast and previously unachievable spatio-temporal resolution. Yet, its utility for studying the processing of peripheral inputs at the whole brain level has so far not been quantified. We employed volumetric multi-spectral optoacoustic tomography (vMSOT) to non-invasively monitor the HbO, HbR, and HbT dynamics across the mouse somatosensory cortex evoked by electrical paw stimuli. We show that elevated contralateral activation is preserved in the HbO map (invisible to MRI) under isoflurane anesthesia. Brain activation is shown to be predominantly confined to the somatosensory cortex, with strongest activation in the hindpaw region of the contralateral sensorimotor cortex. Furthermore, vMSOT detected the presence of an initial dip in the contralateral hindpaw region in the delta HbO channel. Sensorimotor cortical activity was identified over all other regions in HbT and HbO but not in HbR. Pearson’s correlation mapping enabled localizing the response to the sensorimotor cortex further highlighting the ability of vMSOT to bridge over imaging performance deficiencies of other functional neuroimaging modalities.

Transcranial photoacoustic imaging of NMDA-evoked focal circuit dynamics in the rat hippocampus

Objective. We report the transcranial functional photoacoustic (fPA) neuroimaging of N-methyl-D-aspartate (NMDA) evoked neural activity in the rat hippocampus. Concurrent quantitative electroencephalography (qEEG) and microdialysis were used to record real-time circuit dynamics and excitatory neurotransmitter concentrations, respectively. Approach. We hypothesized that location-specific fPA voltage-sensitive dye (VSD) contrast would identify neural activity changes in the hippocampus which correlate with NMDA-evoked excitatory neurotransmission. Main results. Transcranial fPA VSD imaging at the contralateral side of the microdialysis probe provided NMDA-evoked VSD responses with positive correlation to extracellular glutamate concentration changes. qEEG validated a wide range of glutamatergic excitation, which culminated in focal seizure activity after a high NMDA dose. We conclude that transcranial fPA VSD imaging can distinguish focal glutamate loads in the rat hippocampus, based on the VSD redistribution mechanism which is sensitive to the electrophysiologic membrane potential. Significance. Our results suggest the future utility of this emerging technology in both laboratory and clinical sciences as an innovative functional neuroimaging modality.

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics.

Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are currently limited to single- or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities: Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction; Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with 'cloud-like' appearance and augmented by cross-sectional scalp maps indicating local maxima. These publicly available tools were designed specifically for visualisation of M/EEG spatiotemporal statisticalparametric maps, however, we also demonstrate alternate use cases of posterior probability maps and weight maps produced by machine learning classifiers. In principle, the methods employed here are transferrable to visualisation of any spatiotemporal image.

Cortical haemodynamic response measured by functional near infrared spectroscopy during a verbal fluency task in patients with major depression and borderline personality disorder.

BackgroundFunctional near infrared spectroscopy (fNIRS) provides a direct and quantitative assessment of cortical haemodynamic function during a cognitive task. This functional neuroimaging modality may be used to elucidate the pathophysiology of psychiatric disorders, and identify neurophysiological differences between co-occurring psychiatric disorders. However, fNIRS research on borderline personality disorder (BPD) has been limited. Hence, this study aimed to compare cerebral haemodynamic function in healthy controls (HC), patients with major depressive disorder (MDD) and patients with BPD.MethodsfNIRS signals during a verbal fluency task designed for clinical assessment was recorded for all participants. Demographics, clinical history and symptom severity were also noted.FindingsCompared to HCs (n = 31), both patient groups (MDD, n = 31; BPD, n = 31) displayed diminished haemodynamic response in the frontal, temporal and parietal cortices. Moreover, haemodynamic response in the right frontal cortex is markedly lower in patients with MDD compared to patients with BPD.InterpretationNormal cortical function in patients with BPD is disrupted, but not as extensively as in patients with MDD. These results provide further neurophysiological evidence for the distinction of patients with MDD from patients with BPD.

Mild Propofol Sedation Reduces Frontal Lobe and Thalamic Cerebral Blood Flow: An Arterial Spin Labeling Study

Mechanisms of anesthetic drug-induced sedation and unconsciousness are still incompletely understood. Functional neuroimaging modalities provide a window to study brain function changes during anesthesia allowing us to explore the sequence of neuro-physiological changes associated with anesthesia. Cerebral perfusion change under an assumption of intact neurovascular coupling is an indicator of change in large-scale neural activity. In this experiment, we have investigated resting state cerebral blood flow (CBF) changes in the human brain during mild sedation, with propofol. Arterial spin labeling (ASL) provides a non-invasive, reliable, and robust means of measuring cerebral blood flow (CBF) and can therefore be used to investigate central drug effects. Mild propofol sedation-related CBF changes were studied at rest (n = 15), in a 3 T MR scanner using a PICORE-QUIPSS II ASL technique. CBF was reduced in bilateral paracingulate cortex, premotor cortex, Broca’s areas, right superior frontal gyrus and also the thalamus. This cerebral perfusion study demonstrates that propofol induces suppression of key cortical (frontal lobe) and subcortical (thalamus) regions during mild sedation.

Identification of neurobehavioural symptom groups based on shared brain mechanisms.

Most psychopathological disorders develop in adolescence. The biological basis for this development is poorly understood. To enhance diagnostic characterization and develop improved targeted interventions, it is critical to identify behavioural symptom groups that share neural substrates. We ran analyses to find relationships between behavioural symptoms and neuroimaging measures of brain structure and function in adolescence. We found two symptom groups, consisting of anxiety/depression and executive dysfunction symptoms, respectively, that correlated with distinct sets of brain regions and inter-regional connections, measured by structural and functional neuroimaging modalities. We found that the neural correlates of these symptom groups were present before behavioural symptoms had developed. These neural correlates showed case-control differences in corresponding psychiatric disorders, depression and attention deficit hyperactivity disorder in independent clinical samples. By characterizing behavioural symptom groups based on shared neural mechanisms, our results provide a framework for developing a classification system for psychiatric illness that is based on quantitative neurobehavioural measures.

Metastable brain waves

Traveling patterns of neuronal activity—brain waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.