Connectivity Analysis is Essential to Understand Neurological Disorders

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

Data-driven tensor independent component analysis for model-based connectivity neurofeedback

Neurodegenerative diseases, including Alzheimer's disease, are characterised by selective neuronal vulnerability with regional, laminar, cellular and neurotransmitter specificity. The regional losses of neurons and their synapses are associated with neurophysiological changes and cognitive decline. Hypotheses related to these mechanisms can be tested and compared by dynamic causal modelling (DCM) of human neuroimaging data, including magnetoencephalography (MEG). In this paper, we use DCM of cross-spectral densities to model changes between baseline and follow-up data in cortical regions of the default mode network, to characterise longitudinal changes in cortical microcircuits and their connectivity underlying resting-state MEG. Twenty-nine people with amyloid-positive mild cognitive impairment and Alzheimer's disease early dementia were studied at baseline and after an average interval of 16 months. To study longitudinal changes induced by Alzheimer's disease, we evaluate three complementary sets of DCM: (i) with regional specificity, of the contributions of neurons to measurements to accommodate regional variability in disease burden; (ii) with dual parameterisation of excitatory neurotransmission, motivated by preclinical and clinical evidence of distinct effects of disease on AMPA versus NMDA type glutamate receptors; and (iii) with constraints to test specific clinical hypothesis about the effects of disease-progression. Bayesian model selection at the group level confirmed evidence for regional specificity of the effects of Alzheimer's disease, with evidence for selective changes in NMDA neurotransmission, and progressive changes in connectivity within and between Precuneus and medial prefrontal cortex. Moreover, alterations in effective connectivity vary in accordance with individual differences in cognitive decline during follow-up. These applications of DCM enrich the mechanistic understanding of the pathophysiology of human Alzheimer's disease and inform experimental medicine studies of novel therapies. More generally, longitudinal DCM provides a potential platform for natural history and interventional studies of neurodegenerative and neuropsychiatric diseases, with selective neuronal vulnerability.

In the current study we focus on the modeling of functional processes of brain tissue using functional Magnetic Resonance Imaging (fMRI) data. A brain connectivity analysis of Restless Legs Syndrome (RLS) is presented. Thirteen un-medicated patients with late-onset RLS and six healthy subjects are studied using structural and functional brain images. We compare functional connectivity analysis methods, according to their dependency on models or data, as well as to model effective connectivity. An Independent Component Analysis (ICA) method is implemented and all spontaneously activated areas in resting-state condition in both patients and healthy subjects are recorded and be compared with previous studies. Functional connectivity correlation matrices of both RLS and control subjects are extracted and these functional connectivity measures were compared using a seed-based analysis method. We model the brain tissue, based on the influence that one region exerts over another, using a spectral Dynamic Causal Model (DCM) analysis, which has not yet been implemented for RLS data. Finally, a Bayesian Model Selection is chosen in order to compare the winning model that effectively describes the data. The benefits of each methodology are presented.

The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.

The neuronal mechanisms underlying arithmetic calculations are not well understood but the differences between mental addition and subtraction could be particularly revealing. Using fMRI and dynamic causal modeling (DCM), this study aimed to identify the distinct neuronal architectures engaged by the cognitive processes of simple addition and subtraction. Our results revealed significantly greater activation during subtraction in regions along the dorsal pathway, including the left inferior frontal gyrus (IFG), middle portion of dorsolateral prefrontal cortex (mDLPFC), and supplementary motor area (SMA), compared with addition. Subsequent analysis of the underlying changes in connectivity - with DCM - revealed a common circuit processing basic (numeric) attributes and the retrieval of arithmetic facts. However, DCM showed that addition was more likely to engage (numeric) retrieval-based circuits in the left hemisphere, while subtraction tended to draw on (magnitude) processing in bilateral parietal cortex, especially the right intraparietal sulcus (IPS). Our findings endorse previous hypotheses about the differences in strategic implementation, dominant hemisphere, and the neuronal circuits underlying addition and subtraction. Moreover, for simple arithmetic, our connectivity results suggest that subtraction calls on more complex processing than addition: auxiliary phonological, visual, and motor processes, for representing numbers, were engaged by subtraction, relative to addition. Hum Brain Mapp 38:3210-3225, 2017. © 2017 Wiley Periodicals, Inc.

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The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development.

Brain functional modeling, what do we measure with fMRI data?

PurposeTo investigate epilepsy-induced changes in effective connectivity between the non-epileptic amygdalo-hippocampal complex (AHC) and the rest of the brain in patients with unilateral mesiotemporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS).MethodsThirty-three patients with unilateral MTLE associated with HS (20 females, mean age: 36 years, 19 left HS) and 33 adult controls matched for age and gender underwent 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET). Right-HS patients' FDG-PET data were flipped to obtain a left–epileptic–focus–lateralized group of patients. Voxels of interest (VOI) were selected within the cytoarchitectonic probabilistic maps of the non-epileptic AHC (probability level = 100%, SPM8 Anatomy toolbox v1.7). Patients and controls were compared using VOI metabolic activity as covariate of interest to search for epilepsy-induced changes in the contribution of the non-epileptic AHC to the level of metabolic activity in other brain areas. Age, gender, duration of epilepsy, seizure type and frequency were used as covariates of no-interest for connectivity analyses.Key findingsSignificant decrease in effective connectivity was found between the non-epileptic AHC and ventral prefrontal cortical areas bilaterally, as well as with the temporal pole and the posterior cingulate cortex contralateral to HS. Significant increase in connectivity was found between the non-epileptic AHC and midline structures, such as the anterior cingulate and dorsal medial prefrontal cortices, as well as the temporo-parietal junction bilaterally. Connectivity analyses also revealed a preserved positive connectivity between the non-epileptic and the epileptic AHC in the patients' group.SignificanceThis study evidences epilepsy-induced changes in connectivity between the non-epileptic AHC and some limbic and default mode network areas. These changes in connectivity probably account for emotional, cognitive and decision-making impairments frequently observed in MTLE patients. The preserved neurometabolic connectivity between the non-epileptic and the epileptic AHC in MTLE patients is pivotal to explain the epilepsy-induced changes found in this study.

Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.

In this study, we examined regions in the left and right hemisphere language network that were altered in terms of the underlying neural activation and effective connectivity subsequent to language rehabilitation. Eight persons with chronic post-stroke aphasia and eight normal controls participated in the current study. Patients received a 10 week semantic feature-based rehabilitation program to improve their skills. Therapy was provided on atypical examples of one trained category while two control categories were monitored; the categories were counterbalanced across patients. In each fMRI session, two experimental tasks were conducted: (a) picture naming and (b) semantic feature verification of trained and untrained categories. Analysis of treatment effect sizes revealed that all patients showed greater improvements on the trained category relative to untrained categories. Results from this study show remarkable patterns of consistency despite the inherent variability in lesion size and activation patterns across patients. Across patients, activation that emerged as a function of rehabilitation on the trained category included bilateral IFG, bilateral SFG, LMFG, and LPCG for picture naming; and bilateral IFG, bilateral MFG, LSFG, and bilateral MTG for semantic feature verification. Analysis of effective connectivity using Dynamic Causal Modeling (DCM) indicated that LIFG was the consistently significantly modulated region after rehabilitation across participants. These results indicate that language networks in patients with aphasia resemble normal language control networks and that this similarity is accentuated by rehabilitation.

Event-related potential and oscillatory cortical activities of artistic methodology in information visualization design in human–computer interface

Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all mTBI patients experience a diverse array of chronic symptoms which persist long after the acute injury. Hence, there is an urgent need for better understanding of the white matter and gray matter pathologies associated with mTBI to map which specific brain systems are impacted and identify courses of intervention. Previous works have linked mTBI to disruptions in white matter pathways and cortical surface abnormalities. Herein, we examine these hypothesized links in an exploratory study of joint structural connectivity and cortical surface changes associated with mTBI and its chronic symptoms. Briefly, we consider a cohort of 12 mTBI and 26 control subjects. A set of 588 cortical surface metrics and 4,753 structural connectivity metrics were extracted from cortical surface regions and diffusion weighted magnetic resonance imaging in each subject. Principal component analysis (PCA) was used to reduce the dimensionality of each metric set. We then applied independent component analysis (ICA) both to each PCA space individually and together in a joint ICA approach. We identified a stable independent component across the connectivity-only and joint ICAs which presented significant group differences in subject loadings (p<0.05, corrected). Additionally, we found that two mTBI symptoms, slowed thinking and forgetfulness, were significantly correlated (p<0.05, corrected) with mTBI subject loadings in a surface-only ICA. These surface-only loadings captured an increase in bilateral cortical thickness.