Statistical approaches to functional neuroimaging data

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout

BACKGROUND

Dual energy computed tomography (DECT) allows direct visualization of monosodium urate crystal deposition in gout. However, DECT urate volume data are often highly skewed (mostly small volumes with the remainder considerably larger), making statistical analyses challenging in longitudinal research. The aim of this study was to explore the ability of various analysis methods to normalise DECT urate volume data and determine change in DECT urate volumes over time.

METHODS

Simulated datasets containing baseline and year 1 DECT urate volumes for 100 people with gout were created from two randomised controlled trials. Five methods were used to transform the DECT urate volume data prior to analysis: log-transformation, Box-Cox transformation, log(X-(min(X)-1)) transformation; inverse hyperbolic sine transformation, and rank order. Linear regression analyses were undertaken to determine the change in DECT urate volume between baseline and year 1. Cohen's d were calculated as a measure of effect size for each data treatment method. These analyses were then tested in a validation clinical trial dataset containing baseline and year 1 DECT urate volumes from 91 people with gout.

RESULTS

No data treatment method successfully normalised the distribution of DECT urate volumes. For both simulated and validation data sets, significant reductions in DECT urate volumes were observed between baseline and Year 1 across all data treatment methods and there were no significant differences in Cohen's d effect sizes.

CONCLUSIONS

Normalising highly skewed DECT urate volume data is challenging. Adopting commonly used transformation techniques may not significantly improve the ability to determine differences in measures of central tendency when comparing the change in DECT urate volumes over time.

Dual-task related frontal cerebral blood flow changes in older adults with mild cognitive impairment: A functional diffuse correlation spectroscopy study

Introduction

In a worldwide aging population with a high prevalence of motor and cognitive impairment, it is paramount to improve knowledge about underlying mechanisms of motor and cognitive function and their interplay in the aging processes.

Methods

We measured prefrontal cerebral blood flow (CBF) using functional diffuse correlation spectroscopy during motor and dual-task. We aimed to compare CBF changes among 49 older adults with and without mild cognitive impairment (MCI) during a dual-task paradigm (normal walk, 2- forward count walk, 3-backward count walk, obstacle negotiation, and heel tapping). Participants with MCI walked slower during the normal walk and obstacle negotiation compared to participants with normal cognition (NC), while gait speed during counting conditions was not different between the groups, therefore the dual-task cost was higher for participants with NC. We built a linear mixed effects model with CBF measures from the right and left prefrontal cortex.

Results

MCI (n = 34) showed a higher increase in CBF from the normal walk to the 2-forward count walk (estimate = 0.34, 95% CI [0.02, 0.66], p = 0.03) compared to participants with NC, related to a right- sided activation. Both groups showed a higher CBF during the 3-backward count walk compared to the normal walk, while only among MCI, CFB was higher during the 2-forward count walk.

Discussion

Our findings suggest a differential prefrontal hemodynamic pattern in older adults with MCI compared to their NC counterparts during the dual-task performance, possibly as a response to increasing attentional demand.

An integrated cluster-wise significance measure for fMRI analysis

Cluster-wise inference is widely used in fMRI analysis. The cluster-level statistic is often obtained by counting the number of intra-cluster voxels which surpass a voxel-level statistical significance threshold. This measure can be sub-optimal regarding the power and false-positive error rate because the suprathreshold voxel count neglects the voxel-wise significance levels and ignores the dependence between voxels. This article aims to provide a new Integrated Cluster-wise significance Measure (ICM) for cluster-level significance determination in cluster-wise fMRI analysis by integrating cluster extent, voxel-level significance (e.g., p values), and activation dependence between within-cluster voxels. We develop a computationally efficient strategy for ICM based on probabilistic approximation theories. Consequently, the computational load for ICM-based cluster-wise inference (e.g., permutation tests) is affordable. We validate the proposed method via extensive simulations and then apply it to two fMRI data sets. The results demonstrate that ICM can improve the power with well-controlled family-wise error (FWE).

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple ‌Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

An analysis framework for the integration of broadband NIRS and EEG to assess neurovascular and neurometabolic coupling

With the rapid growth of optical-based neuroimaging to explore human brain functioning, our research group has been developing broadband Near Infrared Spectroscopy (bNIRS) instruments, a technological extension to functional Near Infrared Spectroscopy (fNIRS). bNIRS has the unique capacity of monitoring brain haemodynamics/oxygenation (measuring oxygenated and deoxygenated haemoglobin), and metabolism (measuring the changes in the redox state of cytochrome-c-oxidase). When combined with electroencephalography (EEG), bNIRS provides a unique neuromonitoring platform to explore neurovascular coupling mechanisms. In this paper, we present a novel pipeline for the integrated analysis of bNIRS and EEG signals, and demonstrate its use on multi-channel bNIRS data recorded with concurrent EEG on healthy adults during a visual stimulation task. We introduce the use of the Finite Impulse Response functions within the General Linear Model for bNIRS and show its feasibility to statistically localize the haemodynamic and metabolic activity in the occipital cortex. Moreover, our results suggest that the fusion of haemodynamic and metabolic measures unveils additional information on brain functioning over haemodynamic imaging alone. The cross-correlation-based analysis of interrelationships between electrical (EEG) and haemodynamic/metabolic (bNIRS) activity revealed that the bNIRS metabolic signal offers a unique marker of brain activity, being more closely coupled to the neuronal EEG response.

Multi-aspect testing and ranking inference to quantify dimorphism in the cytoarchitecture of cerebellum of male, female and intersex individuals: a model applied to bovine brains

The dimorphism among male, female and freemartin intersex bovines, focusing on the vermal lobules VIII and IX, was analyzed using a novel data analytics approach to quantify morphometric differences in the cytoarchitecture of digitalized sections of the cerebellum. This methodology consists of multivariate and multi-aspect testing for cytoarchitecture-ranking, based on neuronal cell complexity among populations defined by factors, such as sex, age or pathology. In this context, we computed a set of shape descriptors of the neural cell morphology, categorized them into three domains named size, regularity and density, respectively. The output and results of our methodology are multivariate in nature, allowing an in-depth analysis of the cytoarchitectonic organization and morphology of cells. Interestingly, the Purkinje neurons and the underlying granule cells revealed the same morphological pattern: female possessed larger, denser and more irregular neurons than males. In the Freemartin, Purkinje neurons showed an intermediate setting between males and females, while the granule cells were the largest, most regular and dense. This methodology could be a powerful instrument to carry out morphometric analysis providing robust bases for objective tissue screening, especially in the field of neurodegenerative pathologies.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Comparison of EEG microstates with resting state fMRI and FDG-PET measures in the default mode network via simultaneously recorded trimodal (PET/MR/EEG) data

Simultaneous trimodal positron emission tomography/magnetic resonance imaging/electroencephalography (PET/MRI/EEG) resting state (rs) brain data were acquired from 10 healthy male volunteers. The rs-functional MRI (fMRI) metrics, such as regional homogeneity (ReHo), degree centrality (DC) and fractional amplitude of low-frequency fluctuations (fALFFs), as well as 2-[18F]fluoro-2-desoxy-d-glucose (FDG)-PET standardised uptake value (SUV), were calculated and the measures were extracted from the default mode network (DMN) regions of the brain. Similarly, four microstates for each subject, showing the diverse functional states of the whole brain via topographical variations due to global field power (GFP), were estimated from artefact-corrected EEG signals. In this exploratory analysis, the GFP of microstates was nonparametrically compared to rs-fMRI metrics and FDG-PET SUV measured in the DMN of the brain. The rs-fMRI metrics (ReHO, fALFF) and FDG-PET SUV did not show any significant correlations with any of the microstates. The DC metric showed a significant positive correlation with microstate C (r_s  = 0.73, p = .01). FDG-PET SUVs indicate a trend for a negative correlation with microstates A, B and C. The positive correlation of microstate C with DC metrics suggests a functional relationship between cortical hubs in the frontal and occipital lobes. The results of this study suggest further exploration of this method in a larger sample and in patients with neuropsychiatric disorders. The aim of this exploratory pilot study is to lay the foundation for the development of such multimodal measures to be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.

Neural mechanisms of sensitivity to peer information in young adult cannabis users

Though social influence is a critical factor in the initiation and maintenance of marijuana use, the neural correlates of influence in those who use marijuana are unknown. In this study, marijuana-using young adults (MJ; n = 20) and controls (CON; n = 23) performed a decision-making task in which they made a perceptual choice after viewing the choices of unknown peers via photographs, while they underwent functional magnetic resonance imaging scans. The MJ and CON groups did not show differences in the overall number of choices that agreed with versus opposed group influence, but only the MJ group showed reaction time slowing when deciding against group choices. Longer reaction times were associated with greater activation of frontal regions. The MJ goup, compared to CON, showed significantly greater activation in the caudate when presented with peer information. Across groups, caudate activation was associated with self-reported susceptibility to influence. These findings indicate that young adults who use MJ may exhibit increased effort when confronted with opposing peer influence, as well as exhibit greater responsivity of the caudate to social information. These results not only better define the neural basis of social decisions, but also suggest that marijuana use is associated with exaggerated neural activity during decision making that involves social information.

EEG/fMRI contributions to our understanding of genetic generalized epilepsies

The first reports of combined EEG and fMRI used for evaluation of epileptic spikes date back to the mid-90s. At that time, the technique was called EEG-triggered fMRI--the "triggered" corresponded to an epilepsy specialist reviewing live EEG while the patient was located in the scanner; after the spike was identified, a scan was initiated to collect the data. Since then major progress has been made in combined EEG/fMRI data collection and analyses. These advances allow studying the electrophysiology of genetic generalized epilepsies (GGEs) in vivo in greater detail than ever. In addition to continuous data collection, we now have better methods for removing physiologic and fMRI-related artifacts, more advanced understanding of the hemodynamic response functions, and better computational methods to address the questions regarding the origins of the epileptiform discharge generators in patients with GGEs. These advances have allowed us to examine numerous cohorts of children and adults with GGEs while not only looking for spike and wave generators but also examining specific types of GGEs (e.g., juvenile myoclonic epilepsy or childhood absence epilepsy), drug-naïve patients, effects of medication resistance, or effects of epileptiform abnormalities and/or seizures on brain connectivity. While the discussion is ongoing, the prevailing thought is that the GGEs as a group are a network disorder with participation from multiple nodes including the thalami and cortex with the clinical presentation depending on which node of the participating network is affected by the disease process. This review discusses the contributions of EEG/fMRI to our understanding of GGEs.

Statistical comparison of spatial point patterns in biological imaging

In biological systems, functions and spatial organizations are closely related. Spatial data in biology frequently consist of, or can be assimilated to, sets of points. An important goal in the quantitative analysis of such data is the evaluation and localization of differences in spatial distributions between groups. Because of experimental replications, achieving this goal requires comparing collections of point sets, a noticeably challenging issue for which no method has been proposed to date. We introduce a strategy to address this problem, based on the comparison of point intensities throughout space. Our method is based on a statistical test that determines whether local point intensities, estimated using replicated data, are significantly different or not. Repeating this test at different positions provides an intensity comparison map and reveals domains showing significant intensity differences. Simulated data were used to characterize and validate this approach. The method was then applied to two different neuroanatomical systems to evaluate its ability to reveal spatial differences in biological data sets. Applied to two distinct neuronal populations within the rat spinal cord, the method generated an objective representation of the spatial segregation established previously on a subjective visual basis. The method was also applied to analyze the spatial distribution of locus coeruleus neurons in control and mutant mice. The results objectively consolidated previous conclusions obtained from visual comparisons. Remarkably, they also provided new insights into the maturation of the locus coeruleus in mutant and control animals. Overall, the method introduced here is a new contribution to the quantitative analysis of biological organizations that provides meaningful spatial representations which are easy to understand and to interpret. Finally, because our approach is generic and punctual structures are widespread at the cellular and histological scales, it is potentially useful for a large spectrum of applications for the analysis of biological systems.

Mapping tinnitus-related brain activation: an activation-likelihood estimation metaanalysis of PET studies

In tinnitus, PET and other functional imaging modalities have shown functional changes not only in the auditory cortex but also in nonauditory regions such as the limbic, frontal, and parietal areas. Nonetheless, disparities in task dimension among studies, low statistical power due to small sample size, and the intrinsic uncertainty of a modality that measures activity indirectly limit the comprehensive understanding of the results from PET studies. These difficulties prompted us to undertake a metaanalysis of PET studies on tinnitus using a coordinate-based technique (activation-likelihood estimation) to retrieve the most consistent activation areas across different task dimensions and to compare the results with those from other imaging modalities.

METHODS

We performed 2 activation-likelihood estimation metaanalyses on data from 10 studies with 56 foci in which we examined the contrast between tinnitus individuals and controls and the difference in activation between sound stimuli and resting state in tinnitus individuals.

RESULTS

The studies show that the most consistently activated regions in tinnitus subjects, compared with controls, were the left primary and bilateral secondary auditory cortices, left middle and bilateral inferior temporal gyri, left parahippocampal area, left geniculum body, left precuneus, right anterior cingulate cortex, right claustrum, right middle and inferior frontal gyri, and right angular gyrus. The relatively activated area under sound stimuli, compared with resting state, in tinnitus subjects was the secondary auditory cortex. Our study reconfirms the findings of previous quantitative electroencephalography or magnetoencephalography studies because most of the 14 brain areas with significant activation found in our metaanalysis replicate these earlier data. Our results suggest that the areas described in the tinnitus network are solidly replicable regardless of the applied functional imaging technique.

CONCLUSION

This study proves that PET is a useful modality for tinnitus research and solidifies human tinnitus research itself by confirming previously described brain areas involved in the generation and maintenance of tinnitus.

Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

Longitudinal fMRI analysis: A review of methods

Functional magnetic resonance imaging (fMRI) investigations of a longitudinal nature, where participants are scanned repeatedly over time and imaging data are obtained at more than one time-point, are essential to understanding functional changes and development in healthy and pathological brains. The main objective of this paper is to provide a brief summary of common longitudinal analysis approaches, develop an overview of fMRI by introducing how such data manifest, and explore the statistical challenges that arise at the intersection of these two techniques.

Longitudinal fMRI analysis: A review of methods

Functional magnetic resonance imaging (fMRI) investigations of a longitudinal nature, where participants are scanned repeatedly over time and imaging data are obtained at more than one time-point, are essential to understanding functional changes and development in healthy and pathological brains. The main objective of this paper is to provide a brief summary of common longitudinal analysis approaches, develop an overview of fMRI by introducing how such data manifest, and explore the statistical challenges that arise at the intersection of these two techniques.