Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis.

Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity "similarity index" for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.

Hierarchical Principal Components for Data-Driven Multiresolution fMRI Analyses.

Understanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems. Current neuroimaging methods, such as independent component analysis (ICA), cannot fully capture this hierarchy since they are limited to a single spatial scale. In this manuscript, we introduce multiresolution hierarchical principal components analysis (hPCA) and compare it to ICA using simulated fMRI datasets. Furthermore, we describe a parametric statistical filtering method developed to focus analyses on biologically relevant features. Lastly, we apply hPCA to the Human Connectome Project (HCP) to demonstrate its ability to estimate a hierarchy from real fMRI data. hPCA accurately estimated spatial maps and time series from networks with diverse hierarchical structures. Simulated hierarchies varied in the degree of branching, such as two-way or three-way subdivisions, and the total number of levels, with varying equal or unequal subdivision sizes at each branch. In each case, as well as in the HCP, hPCA was able to reconstruct a known hierarchy of networks. Our results suggest that hPCA can facilitate more detailed and comprehensive analyses of the brain's network of networks and the multiscale regional specializations underlying neural processing and cognition.

Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review.

OBJECTIVE: To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field. BACKGROUND: Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis. METHODS/RESULTS: The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field. CONCLUSION: Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.

Hippocampal, basal ganglia and olfactory connectivity contribute to cognitive impairments in Parkinson's disease.

Cognitive impairment is increasingly recognized as a characteristic feature of Parkinson's disease (PD), yet relatively little is known about its underlying neurobiology. Previous investigations suggest that dementia in PD is associated with subcortical atrophy, but similar studies in PD with mild cognitive impairment have been mixed. Variability in cognitive phenotypes and diversity of PD symptoms suggest that a common neuropathological origin results in a multitude of impacts within the brain. These direct and indirect impacts of disease pathology can be investigated using network analysis. Functional connectivity, for instance, may be more sensitive than atrophy to decline in specific cognitive domains in the PD population. Fifty-eight participants with PD underwent a neuropsychological test battery and scanning with structural and resting state functional MRI in a comprehensive whole-brain association analysis. To investigate atrophy as a potential marker of impairment, structural gray matter atrophy was associated with cognitive scores in each cognitive domain using voxel-based morphometry. To investigate connectivity, large-scale networks were correlated with voxel time series and associated with cognitive scores using distance covariance. Structural atrophy was not associated with any cognitive domain, with the exception of visuospatial measures in primary sensory and motor cortices. In contrast, functional connectivity was associated with attention, executive function, language, learning and memory, visuospatial, and global cognition in the bilateral hippocampus, left putamen, olfactory cortex, and bilateral anterior temporal poles. These preliminary results suggest that cognitive domain-specific networks in PD are distinct from each other and could provide a network signature for different cognitive phenotypes.

Stable Meta-Networks, Noise, and Artifacts in the Human Connectome: Low- to High-Dimensional Independent Components Analysis as a Hierarchy of Intrinsic Connectivity Networks.

Connectivity within the human connectome occurs between multiple neuronal systems-at small to very large spatial scales. Independent component analysis (ICA) is potentially a powerful tool to facilitate multi-scale analyses. However, ICA has yet to be fully evaluated at very low (10 or fewer) and ultra-high dimensionalities (200 or greater). The current investigation used data from the Human Connectome Project (HCP) to determine the following: (1) if larger networks, or meta-networks, are present at low dimensionality, (2) if nuisance sources increase with dimensionality, and (3) if ICA is prone to overfitting. Using bootstrap ICA, results suggested that, at very low dimensionality, ICA spatial maps consisted of Visual/Attention and Default/Control meta-networks. At fewer than 10 components, well-known networks such as the Somatomotor Network were absent from results. At high dimensionality, nuisance sources were present even in denoised high-quality data but were identifiable by correlation with tissue probability maps. Artifactual overfitting occurred to a minor degree at high dimensionalities. Basic summary statistics on spatial maps (maximum cluster size, maximum component weight, and average weight outside of maximum cluster) quickly and easily separated artifacts from gray matter sources. Lastly, by using weighted averages of bootstrap stability, even ultra-high dimensional ICA resulted in highly reproducible spatial maps. These results demonstrate how ICA can be applied in multi-scale analyses, reliably and accurately reproducing the hierarchy of meta-networks, large-scale networks, and subnetworks, thereby characterizing cortical connectivity across multiple spatial scales.

Altered between-network connectivity in individuals prone to obesity.

OBJECTIVE: Investigating intrinsic brain functional connectivity may help identify the neurobiology underlying cognitive patterns and biases contributing to obesity propensity. To address this, the current study used a novel whole-brain, data-driven approach to examine functional connectivity differences in large-scale network interactions between obesity-prone (OP) and obesity-resistant (OR) individuals. METHODS: OR (N = 24) and OP (N = 25) adults completed functional magnetic resonance imaging (fMRI) during rest. Large-scale brain networks were identified using independent component analysis (ICA). Voxel-specific between-network connectivity analysis assessed correlations between ICA component time series' and individual voxel time series, identifying regions strongly connected to many networks, i.e., "hubs". RESULTS: Significant group differences in between-network connectivity (OP vs. OR; FDR-corrected) were observed in bilateral basal ganglia (left: q = 0.009; right: q = 0.010) and right dorsolateral prefrontal cortex (dlPFC; q = 0.026), with OP>OR. Basal ganglia differences were largely driven by a more strongly negative correlation with a lateral sensorimotor network in OP, with dlPFC differences driven by a more strongly negative correlation with an inferior visual network in OP. CONCLUSIONS: Greater between-network connectivity was observed in the basal ganglia and dlPFC in OP, driven by stronger associations with lateral sensorimotor and inferior visual networks, respectively. This may reflect a disrupted balance between goal-directed and habitual control systems and between internal/external monitoring processes.

Autism Spectrum Disorder Symptoms are Associated with Connectivity Between Large-Scale Neural Networks and Brain Regions Involved in Social Processing.

The neurobiology of autism spectrum disorder remains poorly understood. The present study addresses this knowledge gap by examining the relationship between functional brain connectivity and Autism Diagnostic Observation Schedule (ADOS) scores using publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 107). This relationship was tested across all brain voxels, without a priori assumptions, using a novel statistical approach. ADOS scores were primarily associated with decreased connectivity to right temporoparietal junction, right anterior insula, and left fusiform gyrus (p < 0.05, corrected). Seven large-scale brain networks influenced these associations. Findings largely encompassed brain regions involved in processing socially relevant information, highlighting the importance of these processes in autism spectrum disorder.

Association of Working Memory With Distributed Executive Control Networks in Schizophrenia.

OBJECTIVE: Working memory impairments represent a core cognitive deficit in schizophrenia, predictive of patients' daily functioning, and one that is unaffected by current treatments. To address this, working memory is included in the MATRICS Consensus Cognitive Battery (MCCB), a standardized cognitive battery designed to facilitate drug development targeting cognitive symptoms. However, the neurobiology underlying these deficits in MCCB working memory is currently unknown, mirroring the poor understanding in general of working memory deficits in schizophrenia. METHODS: Twenty-eight participants with schizophrenia were administered working memory tests from the MCCB and examined with resting-state functional MRI. Intrinsic connectivity networks were estimated with independent component analysis. Each voxel's time series was correlated with each network time series, creating a feature vector for voxel-level connectivity analysis. This feature vector was associated with working memory by using the distance covariance statistic. RESULTS: The neurobiology of MCCB working memory tests largely followed the multicomponent model of working memory but revealed unexpected differences. The dorsolateral prefrontal cortex was not associated with working memory. The central executive system was instead associated with delocalized right and left executive control networks. The phonologic loop within the multicomponent model, a subsystem involved in storing linguistic information, was associated with connectivity to the left temporoparietal junction and inferior frontal gyrus. However, connections to the language network did not predict working memory test performance. CONCLUSIONS: These results provide supporting evidence for the multicomponent model of working memory in terms of the biology underlying MCCB findings.

Alpha7 Nicotinic Receptors as Therapeutic Targets in Schizophrenia.

While current treatments for schizophrenia often provide much relief for positive symptoms such as hallucinations, other symptoms, particularly cognitive deficits, persist and contribute to substantial suffering and reduced quality of life for patients. In searching for novel therapeutic avenues to treat cognitive deficits in schizophrenia, recent work is exploring nicotinic receptor neurobiology. Supported by a large body of evidence, with contributions from studies of smoking behaviors, genetics, receptor distribution and function, animal models and nicotinic effects on illness symptoms, the alpha7 nicotinic receptor has emerged as a potential therapeutic target. Despite promise in early clinical trials, however, no drug targeting nicotinic systems has succeeded in larger phase 3 trials. Following a brief review of nicotinic receptor biology and the evidence that has led to pursuit of alpha7 nicotinic agonism as a therapeutic strategy, this review will provide an update on the status of recent trials, discuss potential issues that may have contributed to negative outcomes, and point to new directions and promising advances in developing alpha7 nicotinic receptor-based treatment for cognitive symptoms in schizophrenia. IMPLICATIONS: By examining alpha7 nicotinic receptor biology and recent efforts to target the receptor in clinical trials, it is hoped that investigators will be motivated to explore novel, promising directions focusing on the receptor as a strategy to treat cognitive symptoms in schizophrenia.

Nicotinic modulation of salience network connectivity and centrality in schizophrenia.

Although functional abnormalities of the salience network are associated with schizophrenia, the acute effects of nicotine on its function and network dynamics during the resting state in patients are poorly understood. In this study, the effects of a 7 mg nicotine patch (vs. placebo) on salience network connectivity were examined in 17 patients with schizophrenia and 19 healthy subjects. We hypothesized abnormal connectivity between the salience network and other major networks (e.g. executive network) in patients under placebo administration and amelioration of this difference after nicotine. We also examined effects of nicotine on betweenness centrality (a measure of the influence of a region on information transfer throughout the brain) and local efficiency (a measure of local information transfer) of the network. A hybrid independent component analysis (ICA)/seed-based connectivity approach was implemented in which the salience network was extracted by ICA and cortical network peaks (anterior cingulate cortex (ACC), left and right insula) were used as seeds for whole-brain seed-to-voxel connectivity analysis. Significant drug X diagnosis interactions were observed between the ACC seed and superior parietal lobule and ventrolateral prefrontal cortex. A significant interaction effect was also observed between the left insula seed and middle cingulate cortex. During placebo conditions, abnormal connectivity predicted negative symptom severity and lower global functioning in patients. A significant drug X diagnosis interaction was also observed for betweenness centrality of the ACC. These results suggest that nicotine may target abnormalities in functional connectivity between salience and executive network areas in schizophrenia as well as affect the ability of the salience network to act as an integrator of global signaling in the disorder.

Levodopa modulates small-world architecture of functional brain networks in Parkinson's disease.

BACKGROUND: PD is associated with disrupted connectivity to a large number of distributed brain regions. How the disease alters the functional topological organization of the brain, however, remains poorly understood. Furthermore, how levodopa modulates network topology in PD is largely unknown. The objective of this study was to use resting-state functional MRI and graph theory to determine how small-world architecture is altered in PD and affected by levodopa administration. METHODS: Twenty-one PD patients and 20 controls underwent functional MRI scanning. PD patients were scanned off medication and 1 hour after 200 mg levodopa. Imaging data were analyzed using 226 nodes comprising 10 intrinsic brain networks. Correlation matrices were generated for each subject and converted into cost-thresholded, binarized adjacency matrices. Cost-integrated whole-brain global and local efficiencies were compared across groups and tested for relationships with disease duration and severity. RESULTS: Data from 2 patients and 4 controls were excluded because of excess motion. Patients off medication showed no significant changes in global efficiency and overall local efficiency, but in a subnetwork analysis did show increased local efficiency in executive (P = 0.006) and salience (P = 0.018) networks. Levodopa significantly decreased local efficiency (P = 0.039) in patients except within the subcortical network, in which it significantly increased local efficiency (P = 0.007). CONCLUSIONS: Levodopa modulates global and local efficiency measures of small-world topology in PD, suggesting that degeneration of nigrostriatal neurons in PD may be associated with a large-scale network reorganization and that levodopa tends to normalize the disrupted network topology in PD. © 2016 International Parkinson and Movement Disorder Society.

Exercise-related changes in between-network connectivity in overweight/obese adults.

Understanding how exercise affects communication across the brain in overweight/obese individuals may provide insight into mechanisms of weight loss and maintenance. In the current study, we examined the effects of a 6-month exercise program in 11 overweight/obese individuals (mean BMI: 33.6±1.4mg/kg(2); mean age: 38.2±3.2years) on integrative brain "hubs," which are areas with high levels of connectivity to multiple large-scale networks thought to play an important role in multimodal integration among brain regions. These integrative hubs were identified with a recently developed between-network connectivity (BNC) metric, using functional magnetic resonance imaging (fMRI). BNC utilizes a multiple regression analysis approach to assess relationships between the time series of large-scale functionally-connected brain networks (identified using independent components analysis) and the time series of each individual voxel in the brain. This approach identifies brain regions with high between-network interaction, i.e., areas with high levels of connectivity to many large-scale networks. Changes in BNC following exercise were determined using paired t-tests, with results considered significant at a whole-brain level if they exceeded a voxel-wise threshold of p<0.01 and cluster-level family-wise error (FWE) correction for multiple comparisons of p<0.05. Following the intervention, BNC in the posterior cingulate cortex (PCC) was significantly reduced (p<0.001). The changes driving the observed effects were explored using Granger causality, finding significant reductions in both outgoing causal flow from the PCC to a number of networks (p<0.05; language network, visual network, sensorimotor network, left executive control network, basal ganglia network, posterior default mode network), in addition to reductions in ingoing causal flow to the PCC from a number of networks (p<0.05; ventral default mode network, language network, sensorimotor network, basal ganglia network). Change in BNC was related to changes in aerobic fitness level (VO2 max; p=0.008) and perceived hunger (Three Factor Eating Questionnaire; p=0.040). Overall, the impact of exercise on communication between large-scale networks may contribute to individual responsivity to exercise.

Targeting Functional Biomarkers in Schizophrenia with Neuroimaging.

Many of the most debilitating symptoms for psychiatric disorders such as schizophrenia remain poorly treated. As such, the development of novel treatments is urgently needed. Unfortunately, the costs associated with high failure rates for investigational compounds as they enter clinical trials has led to pharmaceutical companies downsizing or eliminating research programs needed to develop these drugs. One way of increasing the probability of success for investigational compounds is to incorporate alternative methods of identifying biological targets in order to more effectively screen new drugs. A promising method of accomplishing this goal for psychiatric drugs is to use functional magnetic resonance imaging (fMRI). fMRI investigates neural circuits, shedding light on the biology that generates symptoms such as hallucinations. Once identified, relevant neural circuits can be targeted with pharmacologic interventions and the response to these drugs measured with fMRI. This review describes the early use of fMRI in this context, and discusses the alpha7 nicotinic receptor agonist 3-(2,4-dimethoxybenzylidene) anabaseine (DMXB-A), as an example of the potential value of fMRI for psychiatric drug development.

Between-network connectivity occurs in brain regions lacking layer IV input.

To better understand the cortical circuitry underlying connectivity between large-scale neural networks, we develop a novel, data-driven approach to identify potential integration subregions. Between-network connectivity (BNC) associated with any anatomical region is the amount of connectivity between that point and all large-scale networks, as measured using simple and multiple correlations. It is straightforward to calculate and applicable to functional networks identified using independent components analysis. We calculated BNC for all fMRI voxels within the brain and compared the results to known regional cytoarchitectural patterns. Based on previous observations of the relationship between macroscopic connectivity and microscopic cytoarchitecture, we predicted that areas with high BNC will be located in paralimbic subregions with an undifferentiated laminar structure. Results suggest that the anterior insula and dorsal posterior cingulate cortices play prominent roles in information integration. Cytoarchitecturely, these areas show agranular or dysgranular cytologies with absent or disrupted cortical layer IV. Since layer IV is the primary recipient of feed-forward thalamocortical connections, and due to the exclusive nature of driving connections to this layer, we suggest that the absence of cortical layer IV might allow for information to be exchanged across networks, and is an organizational characteristic of brain-subregions serving as inter-network communication hubs.

Reduced brain resting-state network specificity in infants compared with adults.

PURPOSE: Infant resting-state networks do not exhibit the same connectivity patterns as those of young children and adults. Current theories of brain development emphasize developmental progression in regional and network specialization. We compared infant and adult functional connectivity, predicting that infants would exhibit less regional specificity and greater internetwork communication compared with adults. PATIENTS AND METHODS: Functional magnetic resonance imaging at rest was acquired in 12 healthy, term infants and 17 adults. Resting-state networks were extracted, using independent components analysis, and the resulting components were then compared between the adult and infant groups. RESULTS: Adults exhibited stronger connectivity in the posterior cingulate cortex node of the default mode network, but infants had higher connectivity in medial prefrontal cortex/anterior cingulate cortex than adults. Adult connectivity was typically higher than infant connectivity within structures previously associated with the various networks, whereas infant connectivity was frequently higher outside of these structures. Internetwork communication was significantly higher in infants than in adults. CONCLUSION: We interpret these findings as consistent with evidence suggesting that resting-state network development is associated with increasing spatial specificity, possibly reflecting the corresponding functional specialization of regions and their interconnections through experience.

Functional magnetic resonance imaging of intrinsic brain networks for translational drug discovery.

Developing translational biomarkers is a priority for psychiatry research. Task-independent functional brain imaging is a relatively novel technique that allows examination of the brain's intrinsic networks, defined as functionally and (often) structurally connected populations of neurons whose properties reflect fundamental neurobiological organizational principles of the central nervous system. The ability to study the activity and organization of these networks has opened a promising new avenue for translational investigation, because they can be analogously examined across species and disease states. Interestingly, imaging studies have revealed shared spatial and functional characteristics of the intrinsic network architecture of the brain across species, including mice, rats, non-human primates, and humans. Using schizophrenia as an example, we show how intrinsic networks may show similar abnormalities in human diseases and animal models of these diseases, supporting their use as biomarkers in drug development.

Nicotine increases cerebellar activity during finger tapping.

Nicotine improves performance on several cognitive and sensorimotor tasks. The neuronal mechanisms associated with these changes in performance are, however, largely unknown. Functional magnetic resonance imaging (fMRI) was used to examine the effect of nicotine on neuronal response in nineteen healthy subjects while they performed an auditory-paced finger tapping task. Subjects performed the task, after receiving either a nicotine patch or placebo treatment, in a single blind, crossover design. Compared to placebo, nicotine treatment increased response in the cerebellar vermis. Increased vermal activity, in the absence of changes in other task-related regions suggests specificity in nicotine's effects.

Nicotine increases brain functional network efficiency.

Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function.

Altered default network activity in obesity.

The regulation of energy intake is a complex process involving the integration of homeostatic signals and both internal and external sensory inputs. To better understand the neurobiology of this process and how it may be dysfunctional in obesity, this study examined activity of the brain's "default network" in reduced-obese (RO) as compared to lean individuals. The default network is a group of functionally connected brain regions thought to play an important role in internally directed cognitive activity and the interplay between external and internal sensory processing. Functional magnetic resonance imaging was performed in 24 lean and 18 RO individuals in the fasted state after 2 days of eucaloric energy intake and after 2 days of 30% overfeeding in a counterbalanced design. Scanning was performed while subjects passively viewed images of food and nonfood objects. Independent component analysis was used to identify the default network component. In the eucaloric state, greater default network activity was observed in RO compared to lean individuals in the lateral inferior parietal and posterior cingulate cortices. Activity was positively correlated with appetite. Overfeeding resulted in increased default network activity in lean but not RO individuals. These findings suggest that the function of the default network, a major contributor to intrinsic neuronal activity, is altered in obesity and/or obese-prone individuals. Future studies of the network's function and its relationship to other brain networks may improve our understanding of the mechanisms and treatment of obesity.