Pairwise Functional Connectivity Research Articles

EEG rhythmic and arrhythmic spectral components and functional connectivity at resting state may predict the development of synucleinopathies in idiopathic REM sleep behavior disorder.

Idiopathic/isolated REM-sleep behavior disorder (iRBD) often precedes the onset of synucleinopathies. Here, we investigated whether baseline resting-state EEG advanced spectral power and functional connectivity differ between iRBD patients who converted towards a synucleinopathy at follow-up and those who did not. Eighty-one participants with iRBD (66.89±6.91 years) underwent a baseline resting-state EEG recording, a neuropsychological assessment and a neurological examination. We estimated EEG power spectral density using standard analyses and derived spectral estimates of rhythmic and arrhythmic components. Global and pairwise EEG functional connectivity analyses were computed using the weighted phase-lag index (wPLI). Pixel-based permutation tests were used to compare groups. After a mean follow-up of 5.01±2.76 years, 34 patients were diagnosed with a synucleinopathy (67.81±7.34 years) and 47 remained disease-free (65.53±7.09 years). Among patients who converted, 22 were diagnosed with Parkinson's disease and 12 with dementia with Lewy bodies. As compared to patients who did not convert, patients who converted exhibited at baseline higher relative theta standard power, steeper slopes of the arrhythmic component and higher theta rhythmic power mostly in occipital regions. Furthermore, patients who converted showed higher beta global wPLI but lower alpha wPLI between left temporal and occipital regions. Analyses of resting-state EEG rhythmic and arrhythmic components and functional connectivity suggest an imbalanced excitatory-to-inhibitory activity within large-scale networks, which is associated with later development of a synucleinopathy in iRBD patients.

Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging.

Pathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia. Two hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction). The right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub. The thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.

Online Estimating Pairwise Neuronal Functional Connectivity in Brain-Machine Interface.

Neurons respond to external stimuli and form functional networks through pairwise interactions. A neural encoding model can describe a single neuron's behavior, and brain-machine interfaces (BMIs) provide a platform to investigate how neurons adapt, functionally connect, and encode movement. Movement modulation and pairwise functional connectivity are modeled as high-dimensional tuning states, estimated from neural spike train observations. However, accurate estimation of this neural state vector can be challenging as pairwise neural interactions are highly dimensional, change in different temporal scales from movement, and could be non-stationary. We propose an Adam-based gradient descent method to online estimate high-dimensional pairwise neuronal functional connectivity and single neuronal tuning adaptation simultaneously. By minimizing negative log-likelihood based on point process observation, the proposed method adaptively adjusts the learning rate for each dimension of the neural state vectors by employing momentum and regularizer. We test the method on real recordings of two rats performing the brain control mode of a two-lever discrimination task. Our results show that our method outperforms existing methods, especially when the state is sparse. Our method is more stable and faster for an online scenario regardless of the parameter initializations. Our method provides a promising tool to track and build the time-variant functional neural connectivity, which dynamically forms the functional network and results in better brain control.

Hypoexcitation and connectome disintegration via whole‐brain semi‐empirical modeling in neurodegeneration

AbstractBackgroundNeuroimaging biomarkers are intensively investigated in Alzheimer disease (AD) and the behavioral variant frontotemporal dementia (bvFTD). However, advanced ND biomarkers (i.e., PET and plasma) are expensive or not widely available/validated in underrepresented regions. EEG emerges as a promising alternative, due to its low‐cost, non‐invasiveness, portability, and wide availability in clinical research, although they lack mechanistic explications and robust modeling approaches. The aim of our work was to characterize the functional alterations in AD and bvFTD by combining EEG source‐level metaconnectivity (that captures high‐order interactions), anatomical priors, whole‐brain modeling, and a perturbational approach.MethodDemographic‐matched data were collected via the Dementia Latin American Consortium (ReDLat; AD = 31, bvFTD = 18, Control = 46). From EEG source level recordings and using machine learning classifiers, we compared the pairwise functional connectivity with high‐order correlations captured by metaconnectivity. Then, neural mass modeling was used to propose biophysical mechanisms specifically ascribed to AD and bvFTD. Finally, a perturbational approach was employed to characterize the brain trajectories involved in the healthy and pathological states.ResultsUsing metaconnectivity, we found specific subnetworks of brain regions compromised in AD and bvFTD, yielding an excellent accuracy in classifying each group of patients. Brain dynamics turned more “viscous” (uncoordinated) in patients and were associated with multimodal disease progression (years of disease, cognitive impairment, atrophy). Then a whole‐brain Jansen & Rit model simulated the functional disturbances in AD and bvFTD. The DTI connectome disintegration (reduced structural integration) and hypoexcitability (altered excitation/inhibition balance) triggered the metaconnectivity dynamics observed in AD and bvFTD. Finally, in‐silico stimulation identified key regions involved in the transition from disease (AD and bvFTD models) to a healthy brain dynamics.ConclusionOur work suggests new possible EEG markers for characterizing AD and bvFTD, linking the functional disturbances in patients to alterations in the connectome and E/I balance, and evidencing possible anatomical targets to restore the normal brain function.

Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex

One of the most well-established tools for modeling the brain is the functional connectivity network, which is constructed from pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are considered and potentially higher-order structures are missed. Here, we explore how multivariate information theory reveals higher-order dependencies in the human brain. We begin with a mathematical analysis of the O-information, showing analytically and numerically how it is related to previously established information theoretic measures of complexity. We then apply the O-information to brain data, showing that synergistic subsystems are widespread in the human brain. Highly synergistic subsystems typically sit between canonical functional networks, and may serve an integrative role. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of shadow structure that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent an under-explored space that, accessible with tools of multivariate information theory, may offer novel scientific insights.

Default mode network overshadow executive control network in coma emergence and awakening prediction of patients with sTBI

ObjectiveWe aimed to explore the pathogenesis of traumatic coma related to functional connectivity (FC) within the default mode network (DMN), within the executive control network (ECN) and between the DMN and ECN and to investigate its capacity for predicting awakening. MethodsWe carried out resting-state functional magnetic resonance imaging (fMRI) examinations on 28 traumatic coma patients and 28 age-matched healthy controls. DMN and ECN nodes were split into regions of interest (ROIs), and node-to-node FC analysis was conducted on individual participants. To identify coma pathogenesis, we compared the pairwise FC differences between coma patients and healthy controls. Meanwhile, we divided the traumatic coma patients into different subgroups based on their clinical outcome scores at 6 months postinjury. Considering the awakening prediction, we calculated the area under the curve (AUC) to evaluate the predictive ability of changed FC pairs. ResultsWe found a massive pairwise FC alteration in the patients with traumatic coma compared to the healthy controls [45% (33/74) pairwise FC located in the DMN, 27% (20/74) pairwise FC located in the ECN, and 28% (21/74) pairwise FC located between the DMN and ECN]. Moreover, in the awake and coma groups, there were 67% (12/18) pairwise FC alterations located in the DMN and 33% (6/18) pairwise FC alterations located between the DMN and ECN. We also indicated that pairwise FC that showed a predictive value of 6-month awakening was mainly located in the DMN rather than in the ECN. Specifically, decreased FC between the right superior frontal gyrus and right parahippocampal gyrus (in the DMN) showed the highest predictive ability (AUC = 0.827). ConclusionIn the acute phase of severe traumatic brain injury (sTBI), the DMN plays a more prominent role than the ECN and the DMN-ECN interaction in the emergence of traumatic coma and the prediction of 6-month awakening.

Functional alteration due to structural damage is network dependent: insight from multiple sclerosis.

Little is known about how the brain's functional organization changes over time with respect to structural damage. Using multiple sclerosis as a model of structural damage, we assessed how much functional connectivity (FC) changed within and between preselected resting-state networks (RSNs) in 122 subjects (72 with multiple sclerosis and 50 healthy controls). We acquired the structural, diffusion, and functional MRI to compute functional connectomes and structural disconnectivity profiles. Change in FC was calculated by comparing each multiple sclerosis participant's pairwise FC to controls, while structural disruption (SD) was computed from abnormalities in diffusion MRI via the Network Modification tool. We used an ordinary least squares regression to predict the change in FC from SD for 9 common RSNs. We found clear differences in how RSNs functionally respond to structural damage, namely that higher-order networks were more likely to experience changes in FC in response to structural damage (default mode R2 = 0.160-0.207, P < 0.001) than lower-order sensory networks (visual network 1 R2 = 0.001-0.007, P = 0.157-0.387). Our findings suggest that functional adaptability to structural damage depends on how involved the affected network is in higher-order processing.

A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection.

Alzheimer's disease is an irreversible neurological disease, therefore prompt diagnosis during its early stage, i.e., early mild cognitive impairment (MCI), is crucial for effective treatment. In this paper, we propose an automatic diagnosis method, a few-shot learning-based pairwise functional connectivity (FC) similarity measure method, to detect early MCI. We first employ a sliding window strategy to generate a dynamic functional connectivity network (FCN) using each subject's rs-fMRI data. Then, normal controls (NCs) and early MCI patients are distinguished by measuring the similarity between the dynamic FC series of corresponding brain regions of interest (ROIs) pairs in different subjects. However, previous studies have shown that FC patterns in different ROI-pairs contribute differently to disease classification. To enable the FCs of different ROI-pairs to make corresponding contributions to disease classification, we adopt a self-attention mechanism to weight the FC features. We evaluated the suggested strategy using rs-fMRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results point to the viability of our approach for detecting MCI at an early stage.

The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis

Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.

The posterior middle temporal gyrus serves as a hub in syntactic comprehension: A model on the syntactic neural network

Neuroimaging studies have revealed a distributed neural network involving multiple fronto-temporal regions that are active during syntactic processing. Here, we investigated how these regions work collaboratively to support syntactic comprehension by examining the behavioral relevance of the global functional integration of the syntax network (SN). We found that individuals with a stronger resting-state within-network integration in the left posterior middle temporal gyrus (lpMTG) were better at syntactic comprehension. Furthermore, the pair-wise functional connectivity between the lpMTG and the Broca’s area, the middle frontal gyrus, and the angular and supramarginal gyri was positively correlated with participants’ syntactic processing ability. In short, our study reveals the behavioral significance of intrinsic functional integration of the SN in syntactic comprehension, and provides empirical evidence for the hub-like role of the lpMTG. We proposed a neural model for syntactic comprehension highlighting the hub of the SN and its interactions with other regions in the network.

Changes in pairwise functional connectivity associated with changes in cognitive performance in cognitively normal older individuals: A two-year observational study

Neurobiological substrates of cognitive decline in cognitively normal older individuals have been investigated by resting-state functional magnetic resonance imaging, but little is known about the relationship between longitudinal changes in the whole brain. In this study, we examined two-year changes in functional connectivity among 80 gray matter areas and investigated the relationship to two-year changes in cognitive performance. A cross-validated permutation variable importance measure was applied to select features related to a change in cognitive performance. Age-corrected changes in eleven pairs of functional connections were selected as important features, all related to brain areas that belong to the default mode network. A linear regression model with cross-validation demonstrated a mean correlation coefficient of 0.55 between measured and predicted changes in the cognitive composite score. These results suggest that intra- and inter-network connections in the default mode network are associated with cognitive changes over two years among cognitively normal individuals.

Aberrant functional connectivity between anterior cingulate cortex and left insula in association with therapeutic response to biologics in inflammatory arthritis

BackgroundBrain activity is reported to be associated with individual pain susceptibility and inflammatory status, possibly contributing to disease activity assessment in inflammatory arthritis (IA) including rheumatoid arthritis (RA) and spondyloarthritis (SpA). However, what alteration of brain function associated with disease activity and therapeutic effectiveness in IA remains unclear. We aimed to identify the alterations of brain functional connectivity (FC) shared in both RA and SpA, and evaluate its relationship to anti-rheumatic treatment response using functional magnetic resonance imaging (MRI). Patients and methodsStructural and resting-state functional MRI data were acquired from patients with IA, patients with osteoarthritis (OA) and heathy controls (HCs). Two datasets were adopted to derive (51 IA, 56 OA, and 17 HCs) and validate (31 IA) the observations. 33 IA patients in the derivation dataset and all the patients in validation dataset required biological treatment and were clinically evaluated before and after therapy. Via whole-brain pair-wise FC analyses, we analyzed IA-specific FC measures relevant to therapeutic response to biologics. ResultsThe value of FC between left insular cortex (IC) and anterior cingulate cortex (ACC) was significantly low in IA patients compared with OA patients and HCs. We demonstrated that the FC between left anterior long insular gyrus as a subdivision of IC and ACC was significantly associated with therapeutic response to biologics regarding the improvement of patients’ global assessment (PGA) in both derivation and validation datasets. ConclusionDisease-specific resting-state FC provides a means to assess the therapeutic improvement of PGA and would be a clinical decision-making tool with predictability for treatment response in both RA and SpA.

State-Dependent Synchrony and Functional Connectivity in the Primary and Secondary Whisker Somatosensory Cortices.

Synchronized activity plays an important role in sensory coding and memory and is a hallmark of functional network connectivity. However, the effect of sensory activation on synchronization and cortical functional connectivity is largely unknown. In this study, we investigated the effect of whisker activation on synchronization and functional connectivity of the primary (wS1) and secondary (wS2) whisker somatosensory cortices at the single-cell level. The results showed that during the spontaneous pre-stimulus state, neurons tended to be functionally connected with nearby neurons which shared similar tuning characteristics. Whisker activation using either ramp-and-hold stimulation or artificial whisking against sandpaper has significantly reduced the average overall pairwise synchronization and functional connectivity within the wS1 barrel and wS2 cortices. Whisker stimulation disconnected approximately a third of neuronal pairs that were functionally connected during the unstimulated state. Nearby neurons with congruent tuning properties were more likely to remain functionally connected during whisker activation. The findings of this study indicated that cortical somatosensory networks are organized in non-random small world networks composed of neurons sharing relatively similar tuning properties. Sensory whisker activation intensifies these properties and further subdivides the cortical network into smaller more functionally uniform subnetworks, which possibly serve to increase the computational capacity of the network.

Hippocampal-Prefrontal Connectivity Prior to the COVID-19 Pandemic Predicts Stress Reactivity

BackgroundBy adolescence, foundational cognitive and affective neurobehavioral processes specialize based on environmental demands, such as stress, to determine the basis of adult trajectories. The ongoing COVID-19 pandemic has increased stress for everyone, particularly adolescents who face unique stressors such as restrictions in socialization and education. However, variability in brain processes supporting stress reactivity is not well understood. Here, we leverage pre-pandemic brain development studies to identify how maturity of prefrontal connectivity with the amygdala and hippocampus (HPC) is associated with response to COVID-19. We hypothesized that age-related changes in connectivity of affective and cognitive brain systems may underlie the emotional response of adolescents during the pandemic. MethodsIn this study, 10- to 31-year-old participants (n = 111) completed resting-state functional magnetic resonance imaging scans prior to the pandemic and then completed a questionnaire 9 months into the pandemic measuring worry, COVID-related stress, sadness, perceived stress, and positive affect. Associations between pairwise functional connectivity of HPC/amygdala subregions with prefrontal cortex subdivisions and affective reactivity during the pandemic were examined. ResultsRegression analyses indicated that both worry and COVID-19–related stress increased with age (false discovery rate–corrected p < .05). Furthermore, greater connectivity between the anterior ventromedial prefrontal cortex and posterior HPC was associated with greater worry and COVID-19–related stress (p < .05 corrected), which was primarily driven by individuals younger than 18 years. ConclusionsTaken together, our results indicate that increases in stress reactivity to the COVID-19 pandemic across the transition to adulthood are driven by maturation of posterior HPC–ventromedial prefrontal cortex coupling, which integrates stress response and emotional memory processing.

Visual association learning induces global network reorganization

It has been proposed that visual learning is accomplished not only by neural plasticity in the visual cortex, but also by complex interactions between bottom-up and top-down processes that may induce global network reorganization. Here, we applied a multivariate analysis to functional connectivity (FC) patterns across the brain to investigate how visual association learning was achieved through large-scale network reorganization. Participants were trained to associate a set of artificial line-drawing objects with English letters. After three consecutive days of training, participants underwent a functional magnetic resonance imaging scan in which they were presented with the trained stimuli, untrained stimuli, and English words. By calculating pairwise FC between 189 nodes of 10 well-established networks across the brain, we found that the visual association learning induced changes in the global FC pattern when viewing the trained stimuli, rendering it more similar to the FC pattern when viewing English words. Critically, the learning-induced global FC pattern differences were mainly driven by the FC related to the high-level networks involved in attention and cognitive control, suggesting the modification of top-down processes during learning. In sum, our study provides one of the first evidence revealing global network reorganization induced by visual learning and sheds new light on the network mechanisms of top-down influences in learning.

Changes in Pairwise Functional Connectivity Associated with Changes in Cognitive Performance in Cognitively Normal Older Individuals: A Two-Year Observational Study

Changes in Pairwise Functional Connectivity Associated with Changes in Cognitive Performance in Cognitively Normal Older Individuals: A Two-Year Observational Study

Identification of common neural substrates with connectomic abnormalities in four major psychiatric disorders: A connectome-wide association study.

Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR). This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330×4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders. The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity. These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

Influence of music liking on EEG based emotion recognition

In studies of emotions, music is usually used to induce the emotions that are measured on the arousal-valence/arousal-valence-dominance scales. However, the influence of music liking (that depends on individual preference and appraisal) on the induced emotions is often ignored. This work presents a novel study on the influence of liking on arousal, valence, and dominance using a signal processing and pattern recognition framework. Emotion recognition was performed using a feature-level fusion of three features together with feature selection method and a classifier. The features were derived from wavelet decomposition of EEG, pairwise functional connectivity, and graph-theoretic measures that reflect characteristics of an individual electrode, pair of electrodes, and topological properties of the brain networks, respectively. Here, classification is done between the high/low categories of each of the arousal, valence, and dominance scales under three different cases of music liking. The study shows that the classification performances of arousal, valence, and dominance were 22.50%, 14.87%, and 19.44% above the empirical chance-level, respectively. The fusion framework gave up to 5% relative improvement over individual features. The study indicates that liking influences classification performance and also the temporal dynamics of emotional experience across these scales. We observe an inverted U relationship between the level of liking and arousal and dominance classification performance. We also analyzed the feature and electrode usage and specific aspects of brain activity at different levels of liking. This reveals the importance of high-frequency bands and hemispheric features in emotion recognition.

Eigenvector alignment: Assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease.

Eigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer's disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network's dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain's functional networks develop and adapt when challenged by disease processes such as AD.

Altered Resting-State Functional Connectivity Between Awake and Isoflurane Anesthetized Marmosets.

The common marmoset (Callithrix jacchus) is a New World primate that is becoming increasingly popular as a preclinical model. To assess functional connectivity (FC) across the marmoset brain, resting-state functional MRI (RS-fMRI) is often performed under isoflurane anesthesia to avoid the effects of motion, physiological stress, and training requirements. In marmosets, however, it remains unclear how isoflurane anesthesia affects patterns of FC. Here, we investigated the effects of isoflurane on FC when delivered with either medical air or 100% pure oxygen, two canonical methods of inhalant isoflurane anesthesia delivery. The results demonstrated that when delivered with either medical air or 100% oxygen, isoflurane globally decreased FC across resting-state networks that were identified in awake marmosets. Generally, although isoflurane globally decreased FC in resting-state networks, the spatial structure of the networks was preserved. Outside of the context of RS networks, we indexed pair-wise functional connectivity between regions across the brain and found that isoflurane substantially altered interhemispheric and thalamic FC. Taken together, these findings indicate that RS-fMRI under isoflurane anesthesia is useful to evaluate the global structure of functional networks, but may obfuscate important nodes of some network components when compared to data acquired in fully awake marmosets.