Alterations of directional connectivity among resting-state networks in Alzheimer disease

Whole-Brain Dynamics Disruptions in the Progression of Alzheimer's Disease: Understanding the Influence of Amyloid-Beta and Tau

INTRODUCTION

Alzheimer's disease (AD) affects brain structure and function along its evolution, but brain network dynamic changes remain largely unknown.

METHODS

To understand how AD shapes brain activity, we investigated the spatiotemporal dynamics and resting state functional networks using the intrinsic ignition framework, which characterizes how an area transmits neuronal activity to others, resulting in different degrees of integration. Healthy participants, MCI, and AD patients were scanned using resting state fMRI. Mixed effects models were used to assess the impact of ABeta and tau, at the regional and whole-brain levels.

RESULTS

Dynamic complexity is progressively reduced, with Healthy participants showing higher metastability (i.e., a more complex dynamical regime over time) than observed in the other stages, while AD subjects showed the lowest.

DISCUSSION

Our study provides further insight into how AD modulates brain network dynamics along its evolution, progressively disrupting the whole-brain and resting state network dynamics.

Involvement of the intrinsic functional network of the red nucleus in complex behavioral processing

Previous studies suggested the possibility that the red nucleus (RN) is involved in other cognitive functions than motion per se, even though such functions have yet to be clarified. We investigated the activation of RN during several tasks and its intrinsic functional network associated with social cognition and musical practice. The tasks included finger tapping, n-back, and memory recall tasks. Region of interest for RN was identified through those tasks, anatomical information of RN, and a brain atlas. The intrinsic functional network was identified for RN by an analysis of connectivity between RN and other regions typically involved in seven known resting state functional networks with RN used as the seed region. Association of the RN network with a psychological trait of the interpersonal reactivity index and musical training years revealed subnetworks that included empathy related regions or music practice related regions. These social or highly coordinated motor activity represent the most complex functions ever known to involve the RN, adding further evidence for the multifunctional roles of RN. These discoveries may lead to a new direction of investigations to clarify probable novel roles for RN in high-level human behavior.

The whole-brain voxel-based morphometry study in early stage of T2DM patients

AIM

This study aimed to investigate the alterations in whole-brain gray matter density in early stage type 2 diabetes mellitus (T2DM) patients with cognitive impairment using magnetic resonance imaging.

METHODS

Thirty-six cases of early stage T2DM patients with cognitive impairment (T2DM-CI), 34 cases of early stage T2DM patients without cognitive impairment (T2DM) and 30 cases of healthy controls (HC) were enrolled. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores were used to identify the cognitive impairment. The whole-brain gray matter density was analyzed using 3D-T1 BRAVO imaging, using the voxel-based morphometry method on T1 structure imaging of two groups.

RESULTS

The correlation analysis of total gray matter density with MMSE and MoCA scores in the T2DM-CI group was performed. There were no significant differences in MMSE and MoCA scores between the HC and T2DM groups. However, the MMSE and MoCA scores in the T2DM-CI group were significantly reduced compared with the T2DM group. There were no significant differences in age, gender, education, body mass index (BMI) or blood pressure among the three groups. Voxel-based morphometry (VBM) results showed that the density of left triangle part of inferior frontal gyrus, orbital part of inferior frontal gyrus and opercular part of inferior frontal gyrus and left insula in the T2DM-CI group decreased compared with the T2DM group. Correlation analysis results showed that there was a significant positive correlation between total gray matter density and scores of MMSE and MoCA scores in the T2DM-CI group.

CONCLUSION

In conclusion, total gray matter density is positively correlated with the scores of MMSE and MoCA in T2DM patients, which may be an early sign of cognitive impairment in patients with T2DM.

Abnormal functional connectivity and effective connectivity between the default mode network and attention networks in patients with alcohol-use disorder

BACKGROUND

Patients with alcohol-use disorder (AUD) demonstrate dysfunctional cerebral network connectivity. However, limited studies have investigated attention systems in AUD.

PURPOSE

To assess functional (FC) and effective connectivity (EC) in the dorsal (DAN) and ventral attention networks (VAN) and default mode network (DMN) in patients with AUD using resting-state functional magnetic resonance imaging (rs-fMRI).

MATERIAL AND METHODS

MRI and rs-fMRI data were obtained from 28 men with AUD and 30 age-matched healthy controls. Independent component analysis was used to identify and extract network data, for comparison between the two groups. Effective connectivity was evaluated using Granger causality analysis (GCA) by selecting significantly different brain areas as regions of interest (ROI). Signed-path coefficients between ROIs were computed in bivariate mode.

RESULTS

In patients with AUD, FC decreased in the left superior parietal gurus (SPG) and left interparietal sulcus (IPS, in DAN); FC decreased in the right superior frontal gyrus (SPG) and right middle frontal gyrus (MFG, in DMN). GCA values indicated that the DMN exerts a positive causal effect on the DAN (P = 0.007/0.027), which consequently exerts a negative causal effect on the DMN (P = 0.032). Signed-path coefficients from the right MFG to the left IPS correlated negatively with MAST scores (P = 0.015).

CONCLUSION

We found novel inter-network connectivity dysfunction in patients with AUD, which indicates abnormal causal relations between resting-state DAN and DMN. Thus, patients with AUD may have abnormal top-down attention modulation and cognition.

Alzheimer's disease patients activate attention networks in a short-term memory task

Network functioning during cognitive tasks is of major interest in Alzheimer's disease (AD). Cognitive functioning in AD includes variable performance in short-term memory (STM). In most studies, the verbal STM functioning in AD patients has been interpreted within the phonological loop subsystem of Baddeley's working memory model. An alternative account considers that domain-general attentional processes explain the involvement of frontoparietal networks in verbal STM beside the functioning of modality-specific subsystems. In this study, we assessed the functional integrity of the dorsal attention network (involved in task-related attention) and the ventral attention network (involved in stimulus-driven attention) by varying attentional control demands in a STM task. Thirty-five AD patients and twenty controls in the seventies performed an fMRI STM task. Variation in load (five versus two items) allowed the dorsal (DAN) and ventral attention networks (VAN) to be studied. ANOVA revealed that performance decreased with increased load in both groups. AD patients performed slightly worse than controls, but accuracy remained above 70% in all patients. Statistical analysis of fMRI brain images revealed DAN activation for high load in both groups. There was no between-group difference or common activation for low compared to high load conditions. Psychophysiological interaction showed a negative relationship between the DAN and the VAN for high versus low load conditions in patients. In conclusion, the DAN remained activated and connected to the VAN in mild AD patients who succeeded in performing an fMRI verbal STM task. DAN was necessary for the task, but not sufficient to reach normal performance. Slightly lower performance in early AD patients compared to controls might be related to maintained bottom-up attention to distractors, to decrease in executive functions, to impaired phonological processing or to reduced capacity in serial order processing.

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Patients with early AD succeeded in performing an fMRI short-term memory task.

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Dorsal attention network activation did not differ between patients and controls.

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Dorsal and ventral attention networks remained connected in high load task in AD.

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DAN was necessary for the task, but not sufficient to reach normal performance.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

Diminished neural network dynamics in amnestic mild cognitive impairment

Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity. While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others. For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias. Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.

Brain gray matter changes in type 2 diabetes mellitus: A meta-analysis of whole-brain voxel-based morphometry study

AIMS

We aimed to identify alterations in global gray matter volumes (GMV) and consistent regional abnormalities in T2DM patients via meta-analysis.

METHODS

A systematic search for relevant studies indexed in the PubMed and Embase databases was conducted. A quantitative meta-analysis of volumetric and whole-brain VBM data was conducted using STATA v.12.0 and AES-SDM software packages, respectively.

RESULTS

A total of 15 volumetric studies and five VBM studies of GM in T2DM patients vs. healthy controls (HCs) were identified. The volumetric meta-analysis showed that the GMV of patients with T2DM is lower than in HCs (SMD = -0.56, 95% CI = -0.81 to -0.31, P 0.01). The whole-brain VBM meta-analysis revealed GM reductions in the left superior temporal gyrus, the right middle temporal gyrus, the right rolandic operculum, and the left fusiform gyrus in T2DM patients compared with HCs. Meta-regression analysis showed that Mini-Mental State Examination (MMSE) scores have a positive relationship with GMV in the right insula.

CONCLUSIONS

The results showed a reduced volume of whole and regional GM in T2DM patients, which may indicate a risk for dementia. Further longitudinal research is needed to confirm GM changes, cognitive dysfunction, and their relationship in T2DM.

Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD.

Angiotensin II, hypertension and angiotensin II receptor antagonism: Roles in the behavioural and brain pathology of a mouse model of Alzheimer's disease

Elevated angiotensin II causes hypertension and contributes to Alzheimer's disease by affecting cerebral blood flow. Angiotensin II receptor blockers may provide candidates to reduce (vascular) risk factors for Alzheimer's disease. We studied effects of two months of angiotensin II-induced hypertension on systolic blood pressure, and treatment with the angiotensin II receptor blockers, eprosartan mesylate, after one month of induced hypertension in wild-type C57bl/6j and AβPPswe/PS1ΔE9 (AβPP/PS1/Alzheimer's disease) mice. AβPP/PS1 showed higher systolic blood pressure than wild-type. Subsequent eprosartan mesylate treatment restored this elevated systolic blood pressure in all mice. Functional connectivity was decreased in angiotensin II-infused Alzheimer's disease and wild-type mice, and only 12 months of Alzheimer's disease mice showed impaired cerebral blood flow. Only angiotensin II-infused Alzheimer's disease mice exhibited decreased spatial learning in the Morris water maze. Altogether, angiotensin II-induced hypertension not only exacerbated Alzheimer's disease-like pathological changes such as impairment of cerebral blood flow, functional connectivity, and cognition only in Alzheimer's disease model mice, but it also induced decreased functional connectivity in wild-type mice. However, we could not detect hypertension-induced overexpression of Aβ nor increased neuroinflammation. Our findings suggest a link between midlife hypertension, decreased cerebral hemodynamics and connectivity in an Alzheimer's disease mouse model. Eprosartan mesylate treatment restored and beneficially affected cerebral blood flow and connectivity. This model could be used to investigate prevention/treatment strategies in early Alzheimer's disease.

The Effects of Age, from Young to Middle Adulthood, and Gender on Resting State Functional Connectivity of the Dopaminergic Midbrain

Dysfunction of the dopaminergic ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) is implicated in psychiatric disorders including attention-deficit/ hyperactivity disorder (ADHD), addiction, schizophrenia and movement disorders such as Parkinson's disease (PD). Although the prevalence of these disorders varies by age and sex, the underlying neural mechanism is not well understood. The objective of this study was to delineate the distinct resting state functional connectivity (rsFC) of the VTA and SNc and examine the effects of age, from young to middle-adulthood, and sex on the rsFC of these two dopaminergic structures in a data set of 250 healthy adults (18-49 years of age, 104 men). Using blood oxygenation level dependent (BOLD) signals, we correlated the time course of the VTA and SNc to the time courses of all other brain voxels. At a corrected threshold, paired t-test showed stronger VTA connectivity to bilateral angular gyrus and superior/middle and orbital frontal regions and stronger SNc connectivity to the insula, thalamus, parahippocampal gyrus (PHG) and amygdala. Compared to women, men showed a stronger VTA/SNc connectivity to the left posterior orbital gyrus. In linear regressions, men but not women showed age-related changes in VTA/SNc connectivity to a number of cortical and cerebellar regions. Supporting shared but also distinct cerebral rsFC of the VTA and SNc and gender differences in age-related changes from young and middle adulthood in VTA/SNc connectivity, these new findings help advance our understanding of the neural bases of many neuropsychiatric illnesses that implicate the dopaminergic systems.

Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

Sex and Age Effects of Functional Connectivity in Early Adulthood

Functional connectivity (FC) in resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to find coactivating regions in the human brain. Despite its widespread use, the effects of sex and age on resting FC are not well characterized, especially during early adulthood. Here we apply regression and graph theoretical analyses to explore the effects of sex and age on FC between the 116 AAL atlas parcellations (a total of 6670 FC measures). rs-fMRI data of 494 healthy subjects (203 males and 291 females; age range: 22–36 years) from the Human Connectome Project were analyzed. We report the following findings. (1) Males exhibited greater FC than females in 1352 FC measures (1025 survived Bonferroni correction; \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$p < 7.49{ \rm{E}} - 6$$ \end{document}). In 641 FC measures, females exhibited greater FC than males but none survived Bonferroni correction. Significant FC differences were mainly present in frontal, parietal, and temporal lobes. Although the average FC values for males and females were significantly different, FC values of males and females exhibited large overlap. (2) Age effects were present only in 29 FC measures and all significant age effects showed higher FC in younger subjects. Age and sex differences of FC remained significant after controlling for cognitive measures. (3) Although sex \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\times$$ \end{document} age interaction did not survive multiple comparison correction, FC in females exhibited a faster cross-sectional decline with age. (4) Male brains were more locally clustered in all lobes but the cerebellum; female brains had a higher clustering coefficient at the whole-brain level. Our results indicate that although both male and female brains show small-world network characteristics, male brains were more segregated and female brains were more integrated. Findings of this study further our understanding of FC in early adulthood and provide evidence to support that age and sex should be controlled for in FC studies of young adults.

Functional-structural degeneration in dorsal and ventral attention systems for Alzheimer's disease, amnestic mild cognitive impairment

Growing evidence of attention related failures in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) has already been proposed by previous studies. However, previous studies lacked of systematic investigation on the functional and structural substrates for attention function for patients with AD and aMCI. In this work, we investigated the functional connectivity and gray matter density in dorsal and ventral attention networks (DAN, VAN) of normal participants (n = 15) and patients with aMCI (n = 12) and AD (n = 16) by applying group independent component analysis (ICA) and voxel-based morphometry (VBM) analysis. Using ICA, we extracted the functional patterns of DAN and VAN which are respectively responsible for the "top-down" attention process and "bottom-up" process. One-way analysis of variance (ANOVA) revealed significant group-differed functional connectivity in bilateral frontal eye fields (FEF) area and intraparietal sulcus (IPS) area, as well as posterior cingulate cortex and precuneus in the dorsal system. With regard to the ventral system, group-effects were significantly focused in right orbital superior/middle frontal gyrus, right inferior parietal lobule, angular gyrus, and supramarginal gyrus around the temporal-parietal junction area. Post hoc cluster-level comparisons revealed totally impaired functional substrates for both attentional networks for patients with AD, whereas selectively impaired attention systems for patients with aMCI with impaired functional patent of DAN but preserved functional pattern of VAN. Correspondingly, VBM analysis revealed gray matter loss in right ventral and dorsal frontal cortex was in the AD group, whereas preserved gray matter density was in aMCI, even a little extent of expansion of gray matter density in several participants. Using multivariate regression analysis we found discrepant couplings of functional-structural degenerations between both patient groups. Specifically, positive coupling of structural-functional degeneration was found in right dorsal and ventral frontal cortex in the AD group, whereas inverse coupling in dorsal frontal cortex was found in the aMCI group. These findings suggested discrepant functional-structural degenerations in both attention systems between both patient groups, widening avenues to better understanding the attentional deficits in patients with aMCI and AD.

Thalamic Functional Connectivity in Mild Traumatic Brain Injury: Longitudinal Associations With Patient-Reported Outcomes and Neuropsychological Tests

OBJECTIVES

(1) To examine differences in patient-reported outcomes, neuropsychological tests, and thalamic functional connectivity (FC) between patients with mild traumatic brain injury (mTBI) and individuals without mTBI and (2) to determine longitudinal associations between changes in these measures.

DESIGN

Prospective observational case-control study.

SETTING

Academic medical center.

PARTICIPANTS

A sample (N=24) of 13 patients with mTBI (mean age, 39.3±14.0y; 4 women [31%]) and 11 age- and sex-matched controls without mTBI (mean age, 37.6±13.3y; 4 women [36%]).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Resting state FC (3T magnetic resonance imaging scanner) was examined between the thalamus and the default mode network, dorsal attention network, and frontoparietal control network. Patient-reported outcomes included pain (Brief Pain Inventory), depressive symptoms (Patient Health Questionnaire-9), posttraumatic stress disorder ([PTSD] Checklist - Civilian Version), and postconcussive symptoms (Rivermead Post-Concussion Symptoms Questionnaire). Neuropsychological tests included the Delis-Kaplan Executive Function System Tower test, Trails B, and Hotel Task. Assessments occurred at 6 weeks and 4 months after hospitalization in patients with mTBI and at a single visit for controls.

RESULTS

Student t tests found increased pain, depressive symptoms, PTSD symptoms, and postconcussive symptoms; decreased performance on Trails B; increased FC between the thalamus and the default mode network; and decreased FC between the thalamus and the dorsal attention network and between the thalamus and the frontoparietal control network in patients with mTBI as compared with controls. The Spearman correlation coefficient indicated that increased FC between the thalamus and the dorsal attention network from baseline to 4 months was associated with decreased pain and postconcussive symptoms (corrected P<.05).

CONCLUSIONS

Findings suggest that alterations in thalamic FC occur after mTBI, and improvements in pain and postconcussive symptoms are correlated with normalization of thalamic FC over time.

Aberrant intra- and inter-network connectivity architectures in Alzheimer's disease and mild cognitive impairment

Alzheimer's disease (AD) patients and those with high-risk mild cognitive impairment are increasingly considered to have dysfunction syndromes. Large-scale network studies based on neuroimaging techniques may provide additional insight into AD pathophysiology. The aim of the present study is to evaluate the impaired network functional connectivity with the disease progression. For this purpose, we explored altered functional connectivities based on previously well-defined brain areas that comprise the five key functional systems [the default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL), sensorimotor network (SMN)] in 35 with AD and 27 with mild cognitive impairment (MCI) subjects, compared with 27 normal cognitive subjects. Based on three levels of analysis, we found that intra- and inter-network connectivity were impaired in AD. Importantly, the interaction between the sensorimotor and attention functions was first attacked at the MCI stage and then extended to the key functional systems in the AD individuals. Lower cognitive ability (lower MMSE scores) was significantly associated with greater reductions in intra- and inter-network connectivity across all patient groups. These profiles indicate that aberrant intra- and inter-network dysfunctions might be potential biomarkers or predictors of AD progression and provide new insight into AD pathophysiology.

Network Disruption and Cerebrospinal Fluid Amyloid-Beta and Phospho-Tau Levels in Mild Cognitive Impairment

Synaptic dysfunction is a core deficit in Alzheimer's disease, preceding hallmark pathological abnormalities. Resting-state magnetoencephalography (MEG) was used to assess whether functional connectivity patterns, as an index of synaptic dysfunction, are associated with CSF biomarkers [i.e., phospho-tau (p-tau) and amyloid beta (Aβ42) levels]. We studied 12 human subjects diagnosed with mild cognitive impairment due to Alzheimer's disease, comparing those with normal and abnormal CSF levels of the biomarkers. We also evaluated the association between aberrant functional connections and structural connectivity abnormalities, measured with diffusion tensor imaging, as well as the convergent impact of cognitive deficits and CSF variables on network disorganization. One-third of the patients converted to Alzheimer's disease during a follow-up period of 2.5 years. Patients with abnomal CSF p-tau and Aβ42 levels exhibited both reduced and increased functional connectivity affecting limbic structures such as the anterior/posterior cingulate cortex, orbitofrontal cortex, and medial temporal areas in different frequency bands. A reduction in posterior cingulate functional connectivity mediated by p-tau was associated with impaired axonal integrity of the hippocampal cingulum. We noted that several connectivity abnormalities were predicted by CSF biomarkers and cognitive scores. These preliminary results indicate that CSF markers of amyloid deposition and neuronal injury in early Alzheimer's disease associate with a dual pattern of cortical network disruption, affecting key regions of the default mode network and the temporal cortex. MEG is useful to detect early synaptic dysfunction associated with Alzheimer's disease brain pathology in terms of functional network organization.

SIGNIFICANCE STATEMENT

In this preliminary study, we used magnetoencephalography and an integrative approach to explore the impact of CSF biomarkers, neuropsychological scores, and white matter structural abnormalities on neural function in mild cognitive impairment. Disruption in functional connectivity between several pairs of cortical regions associated with abnormal levels of biomarkers, cognitive deficits, or with impaired axonal integrity of hippocampal tracts. Amyloid deposition and tau protein-related neuronal injury in early Alzheimer's disease are associated with synaptic dysfunction and a dual pattern of cortical network disorganization (i.e., desynchronization and hypersynchronization) that affects key regions of the default mode network and temporal areas.

Information Flow Between Resting-State Networks

The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method--addressing differences in IF between RSNs for any generic data--can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t.

CONTROLS

The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls.

Alterations of Functional Connectivity Among Resting-State Networks in Hypothyroidism

Hypothyroidism affects brain functioning as suggested by various neuroimaging studies. The primary focus of the present study was to examine whether hypothyroidism would impact connectivity among resting-state networks (RSNs) using resting-state functional magnetic resonance imaging (rsfMRI). Twenty-two patients with hypothyroidism and 22 healthy controls were recruited and scanned using rsfMRI. The data were analysed using independent component analysis and a dual regression approach that was applied on five RSNs that were identified using fsl software (http://fsl.fmrib.ox.ac.uk). Hypothyroid patients showed significantly decreased functional connectivity in the regions of the right frontoparietal network (frontal pole), the medial visual network (lateral occipital gyrus, precuneus cortex and cuneus) and the motor network (precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus) compared to healthy controls. The reduced functional connectivity in the right frontoparietal network, the medial visual network and the motor network suggests neurocognitive alterations in hypothyroid patients in the corresponding functions. However, the study would be further continued to investigate the effects of thyroxine treatment and correlation with neurocognitive scores. The findings of the present study provide further interesting insights into our understanding of the action of thyroid hormone on the adult human brain.

Bayesian Models for fMRI Data Analysis

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This paper provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatio-temporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as EEG/MEG and DTI data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics.

Altered causal connectivity of resting state brain networks in amnesic MCI

Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI) have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE), and California verbal learning test (CVLT) scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

Bayesian network analysis reveals alterations to default mode network connectivity in individuals at risk for Alzheimer's disease

Alzheimer's disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI.

Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer's disease

The salience network (SN) serves to identify salient stimuli and to switch between the central executive network (CEN) and the default-mode network (DMN), both of which are impaired in Alzheimer's disease (AD)/amnestic mild cognitive impairment (aMCI). We hypothesized that both the structural and functional organization of the SN and functional interactions between the SN and CEN/DMN are altered in normal aging and in AD/aMCI. Gray matter volume (GMV) and resting-state functional connectivity (FC) were analyzed from healthy younger (HYC) to older controls (HOC) and from HOC to aMCI and AD patients. All the SN components showed significant differences in the GMV, intranetwork FC, and internetwork FC between the HYC and HOC. Most of the SN components showed differences in the GMV between the HOC and AD and between the aMCI and AD. Compared with the HOC, AD patients exhibited significant differences in intra- and internetwork FCs of the SN, whereas aMCI patients demonstrated differences in internetwork FC of the SN. Most of the GMVs and internetwork FCs of the SN and part of the intranetwork FC of the SN were correlated with cognitive differences in older subjects. Our findings suggested that structural and functional impairments of the SN may occur as early as in normal aging and that functional disconnection between the SN and CEN/ DMN may also be associated with both normal aging and disease progression.

A biased competition account of attention and memory in Alzheimer's disease

The common view of Alzheimer's disease (AD) is that of an age-related memory disorder, i.e. declarative memory deficits are the first signs of the disease and associated with progressive brain changes in the medial temporal lobes and the default mode network. However, two findings challenge this view. First, new model-based tools of attention research have revealed that impaired selective attention accompanies memory deficits from early pre-dementia AD stages on. Second, very early distributed lesions of lateral parietal networks may cause these attention deficits by disrupting brain mechanisms underlying attentional biased competition. We suggest that memory and attention impairments might indicate disturbances of a common underlying neurocognitive mechanism. We propose a unifying account of impaired neural interactions within and across brain networks involved in attention and memory inspired by the biased competition principle. We specify this account at two levels of analysis: at the computational level, the selective competition of representations during both perception and memory is biased by AD-induced lesions; at the large-scale brain level, integration within and across intrinsic brain networks, which overlap in parietal and temporal lobes, is disrupted. This account integrates a large amount of previously unrelated findings of changed behaviour and brain networks and favours a brain mechanism-centred view on AD.

Resting state networks in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is typically described as a neurologic disorder affecting a cerebral network comprising the hippocampus proper and several anatomically related extrahippocampal regions. A new level of complexity was recently added to the study of this disorder by the evidence that TLE also appears to chronically alter the activity of several brain-wide neural networks involved in the control of higher order brain functions and not traditionally linked to epilepsy. Recently developed brain imaging techniques such as functional magnetic resonance imaging (fMRI) analysis of resting state connectivity, have greatly contributed to these observations by allowing the precise characterization of several brain networks with distinct functional signatures in the resting brain, and therefore also known as "resting state networks." These significant advances in imaging represent an opportunity to investigate the still elusive origins of the disabling cognitive and psychiatric manifestations of TLE, and could have important implications for its pathophysiology and, perhaps, its therapy. Herein we review recent studies in this field by focusing on resting state networks that have been implicated in the pathophysiology of psychiatric disorders and cognitive impairment in patients with epilepsy: the default mode network, the attention network, and the reward/emotion network.

Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network

Analyzing brain network from neuroimages is becoming a promising approach in identifying novel connectivity-based biomarkers for the Alzheimer's disease (AD). In this regard, brain "effective connectivity" analysis, which studies the causal relationship among brain regions, is highly challenging and of many research opportunities. Most of the existing works in this field use generative methods. Despite their success in data representation and other important merits, generative methods are not necessarily discriminative, which may cause the ignorance of subtle but critical disease-induced changes. In this paper, we propose a learning-based approach that integrates the benefits of generative and discriminative methods to recover effective connectivity. In particular, we employ Fisher kernel to bridge the generative models of sparse Bayesian network (SBN) and the discriminative classifiers of SVMs, and convert the SBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. Our method is able to simultaneously boost the discrimination power of both the generative SBN models and the SBN-induced SVM classifiers via Fisher kernel. The proposed method is tested on analyzing brain effective connectivity for AD from ADNI data. It demonstrates significant improvements over the state-of-the-art: classification accuracy increased above 10% by our SBN models, and above 16% by our SBN-induced SVM classifiers with a simple feature selection.

Aberrant functional organization within and between resting-state networks in AD

Altered functional characteristics have been reported in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD); nonetheless, comprehensive analyses of the resting-state networks (RSNs) are rare. This study combined multiple imaging modalities to investigate the functional and structural changes within each RSN and between RSNs in aMCI/AD patients. Eight RSNs were identified from functional MRI data from 35 AD, 18 aMCI and 21 normal control subjects using independent component analysis. We compared functional connectivity (FC) within each RSN and found decreased FC in the several cognitive-related RSNs in AD, including the bilateral precuneus of the precuneus network, the posterior cingulate cortex and left precuneus of the posterior default mode network (DMN), and the left superior parietal lobule of the left frontoparietal network (LFP). We further compared the grey matter volumes and amplitudes of low-frequency fluctuations of these regions and found decreases in these measures in AD. Importantly, we found decreased inter-network connectivity between the visual network and the LFP and between the anterior and posterior DMNs in AD. All indices in aMCI patients were numerically between those of controls and AD patients. These results suggest that the brain networks supporting complex cognitive processes are specifically and progressively impaired over the course of AD, and the FC impairments are present not only within networks but also between networks.