Fully exploratory network independent component analysis of the 1000 functional connectomes database

The Contribution of Cognitive Control Networks in Word Selection Processing in Parkinson's Disease: Novel Insights from a Functional Connectivity Study

Parkinson's disease (PD) patients are impaired in word production when the word has to be selected among competing alternatives requiring higher attentional resources. In PD, word selection processes are correlated with the structural integrity of the inferior frontal gyrus, which is critical for response selection, and the uncinate fasciculus, which is necessary for processing lexical information. In early PD, we investigated the role of the main cognitive large-scale networks, namely the salience network (SN), the central executive networks (CENs), and the default mode network (DMN), in word selection. Eighteen PD patients and sixteen healthy controls were required to derive nouns from verbs or generate verbs from nouns. Participants also underwent a resting-state functional MRI. Functional connectivity (FC) was examined using independent component analysis. Functional seeds for the SN, CENs, and DMN were defined as spheres, centered at the local activation maximum. Correlations were calculated between the FC of each functional seed and word production. A significant association between SN connectivity and task performance and, with less evidence, between CEN connectivity and the task requiring selection among a larger number of competitors, emerged in the PD group. These findings suggest the involvement of the SN and CEN in word selection in early PD, supporting the hypothesis of impaired executive control.

Investigating functional brain connectivity patterns associated with two hypnotic states

While there's been clinical success and growing research interest in hypnosis, neurobiological underpinnings induced by hypnosis remain unclear. In this fMRI study (which is part of a larger hypnosis project) with 50 hypnosis-experienced participants, we analyzed neural and physiological responses during two hypnosis states, comparing them to non-hypnotic control conditions and to each other. An unbiased whole-brain analysis (multi-voxel- pattern analysis, MVPA), pinpointed key neural hubs in parieto-occipital-temporal areas, cuneal/precuneal and occipital cortices, lingual gyri, and the occipital pole. Comparing directly both hypnotic states revealed depth-dependent connectivity changes, notably in left superior temporal/supramarginal gyri, cuneus, planum temporale, and lingual gyri. Multi-voxel- pattern analysis (MVPA) based seeds were implemented in a seed-to-voxel analysis unveiling region-specific increases and decreases in functional connectivity patterns. Physiologically, the respiration rate significantly slowed during hypnosis. Summarized, these findings foster fresh insights into hypnosis-induced functional connectivity changes and illuminate further knowledge related with the neurobiology of altered consciousness.

Resting-state functional connectivity is modulated by cognitive reserve in early Parkinson's disease

Background

Fronto-striatal disconnection is thought to be at the basis of dysexecutive symptoms in patients with Parkinson's disease (PD). Multiple reserve-related processes may offer resilience against functional decline. Among these, cognitive reserve (CR) refers to the adaptability of cognitive processes.

Objective

To test the hypothesis that functional connectivity of pathways associated with executive dysfunction in PD is modulated by CR.

Methods

Twenty-six PD patients and 24 controls underwent resting-state functional magnetic resonance imaging. Functional connectivity was explored with independent component analysis and seed-based approaches. The following networks were selected from the outcome of the independent component analysis: default-mode (DMN), left and right fronto-parietal (l/rFPN), salience (SalN), sensorimotor (SMN), and occipital visual (OVN). Seed regions were selected in the substantia nigra and in the dorsolateral and ventromedial prefrontal cortex for the assessment of seed-based functional connectivity maps. Educational and occupational attainments were used as CR proxies.

Results

Compared with their counterparts with high CR, PD individuals with low CR had reduced posterior DMN functional connectivity in the anterior cingulate and basal ganglia, and bilaterally reduced connectivity in fronto-parietal regions within the networks defined by the dorsolateral and ventrolateral prefrontal seeds. Hyper-connectivity was detected within medial prefrontal regions when comparing low-CR PD with low-CR controls.

Conclusion

CR may exert a modulatory effect on functional connectivity in basal ganglia and executive-attentional fronto-parietal networks. In PD patients with low CR, attentional control networks seem to be downregulated, whereas higher recruitment of medial frontal regions suggests compensation via an upregulation mechanism. This upregulation might contribute to maintaining efficient cognitive functioning when posterior cortical function is progressively reduced.

Functional Brain Connectivity Patterns Associated with Visual Hallucinations in Dementia with Lewy Bodies

BACKGROUND

The presence of recurrent, complex visual hallucinations (VH) is among the core clinical features of dementia with Lewy bodies (DLB). It has been proposed that VH arise from a disrupted organization of functional brain networks. However, studies are still limited, especially investigating the resting-state functional brain features underpinning VH in patients with dementia.

OBJECTIVE

The aim of the present pilot study was to investigate whether there were any alterations in functional connectivity associated with VH in DLB.

METHODS

Seed-based analyses and independent component analysis (ICA) of resting-state fMRI scans were carried out to explore differences in functional connectivity between DLB patients with and without VH.

RESULTS

Seed-based analyses reported decreased connectivity of the lateral geniculate nucleus, the superior parietal lobule and the putamen with the medial frontal gyrus in DLB patients with VH. Visual areas showed a pattern of both decreased and increased functional connectivity. ICA revealed between-group differences in the default mode network (DMN).

CONCLUSION

Functional connectivity analyses suggest dysfunctional top-down and bottom-up processes and DMN-related alterations in DLB patients with VH. This impairment might foster the generation of false visual images that are misinterpreted, ultimately resulting in VH.

Regional Strength of Large-Scale Functional Brain Networks is Associated with Regional Volumes in Older Adults and in Alzheimer's Disease

Background: The association between regional volumes and resting-state functional networks was tested within the default-mode network (DMN), influenced by Alzheimer pathology, salience network (SalN), not under similar pathological influence, and sensorimotor network (SMN), usually spared by pathology. Methods: A total of 148 participants, with Alzheimer's disease (AD) dementia, mild cognitive impairment (MCI), and healthy controls underwent multimodal brain magnetic resonance imaging (MRI). Functional network identification was achieved with group-level independent-component analysis of functional MRI (fMRI) scans. T1 weighted images were also analyzed. Ten regions of interest (ROI) were defined in core hubs of the three networks. Gray-matter volume/functional network strength association was tested within-ROI and cross-ROI in each group by using partial-correlation models and ROI-to-ROI, ROI-to-voxel, and voxel-to-voxel correlations. Results: In controls, a negative association was found between right inferior-parietal volumes and SMN expression in the left precentral gyrus, as revealed by ROI-to-ROI models. In AD, DMN expression was positively associated with the volume of the left insula and the right inferior parietal lobule, and SalN expression was positively associated with volume of the left inferior parietal lobule. ROI-to-voxel models revealed significant associations between the volume of the posterior cingulate cortex and SMN expression in sensorimotor and premotor regions. No significant findings emerged in the MCI nor from voxel-to-voxel analyses. Discussion: Regional volumes of main network hubs are significantly associated with hemodynamic network expression, although patterns are intricate and dependent on diagnostic status. Since distinct networks are differentially influenced by Alzheimer pathology, it appears that pathology plays a significant role in influencing the association between regional volumes and regional functional network strength.

Functional connectivity in default mode network correlates with severity of hypoxemia in obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA)-associated hypoxemia, sleep fragmentation, and cerebral vascular dysfunction are implicated in cognitive dysfunction. Functional connectivity within default mode network (DMN) is a possible mechanism underlying the cognitive impairment. The aim of this study was to investigate the impact of hypoxemia and sleep fragmentation on functional connectivity and on cognitive performance in patients with OSA.

METHODS

Twenty-eight patients with OSA were included (mean age = 58.0 ± 8.5 years). We correlated the functional connectivity in DMN with cognitive performances and further analyzed the relationship of functional connectivity in DMN with hypoxemia severity, as revealed by apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and nadir SaO₂ (%), and with degree of sleep fragmentation, as shown by sleep efficiency and wake after sleep onset.

RESULTS

Functional connectivity in DMN was associated with AHI, ODI, and nadir SaO₂ (%) (p < .05) and was not associated with sleep fragmentation measures (p > .05). Functional connectivity that was associated with AHI, ODI, and nadir SaO₂ (%) was in the areas of bilateral middle temporal gyri, bilateral frontal pole, and bilateral hippocampus and was positively correlated with Cognitive Abilities Screening Instrument (CASI) total score (ρ = 0.484; p = .012), CASI-List-generating, CASI-Attention, and composite score of CASI-List-generating plus CASI-Attention (p < .05).

CONCLUSION

Functional connectivity in DMN is implicated in impairment of global cognitive function and of attention in OSA patients. The functional connectivity in the DMN is associated with hypoxemia rather than with sleep fragmentation.

Repetitive transcranial magnetic stimulation improves Parkinson's freezing of gait via normalizing brain connectivity

Robust, effective treatments for Parkinson's freezing of gait remain elusive. Our previous study revealed beneficial effects of high-frequency rTMS over the supplementary motor area. The present study aims to explore the neural mechanisms of rTMS treatments utilizing novel exploratory multivariate approaches. We first conducted a resting-state functional MRI study with a group of 40 Parkinson's disease patients with freezing of gait, 31 without freezing of gait, and 30 normal controls. A subset of 30 patients with freezing of gait (verum group: N = 20; sham group: N = 10) who participated the aforementioned rTMS study underwent another scan after the treatments. Using the baseline scans, the imaging biomarkers for freezing of gait and Parkinson's disease were developed by contrasting the connectivity profiles of patients with freezing of gait to those without freezing of gait and normal controls, respectively. These two biomarkers were then interrogated to assess the rTMS effects on connectivity patterns. Results showed that the freezing of gait biomarker was negatively correlated with Freezing of Gait Questionnaire score (r = -0.6723, p < 0.0001); while the Parkinson's disease biomarker was negatively correlated with MDS-UPDRS motor score (r = -0.7281, p < 0.0001). After the rTMS treatment, both the freezing of gait biomarker (0.326 ± 0.125 vs. 0.486 ± 0.193, p = 0.0071) and Parkinson's disease biomarker (0.313 ± 0.126 vs. 0.379 ± 0.155, p = 0.0378) were significantly improved in the verum group; whereas no significant biomarker changes were found in the sham group. Our findings indicate that high-frequency rTMS over the supplementary motor area confers the beneficial effect jointly through normalizing abnormal brain functional connectivity patterns specifically associated with freezing of gait, in addition to normalizing overall disrupted connectivity patterns seen in Parkinson's disease.

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

Age and hippocampal volume predict distinct parts of default mode network activity

Group comparison studies have established that activity in the posterior part of the default-mode network (DMN) is down-regulated by both normal ageing and Alzheimer’s disease (AD). In this study linear regression models were used to disentangle distinctive DMN activity patterns that are more profoundly associated with either normal ageing or a structural marker of neurodegeneration. 312 datasets inclusive of healthy adults and patients were analysed. Days of life at scan (DOL) and hippocampal volume were used as predictors. Group comparisons confirmed a significant association between functional connectivity in the posterior cingulate/retrosplenial cortex and precuneus and both ageing and AD. Fully-corrected regression models revealed that DOL significantly predicted DMN strength in these regions. No such effect, however, was predicted by hippocampal volume. A significant positive association was found between hippocampal volumes and DMN connectivity in the right temporo-parietal junction (TPJ). These results indicate that postero-medial DMN down-regulation may not be specific to neurodegenerative processes but may be more an indication of brain vulnerability to degeneration. The DMN-TPJ disconnection is instead linked to the volumetric properties of the hippocampus, may reflect early-stage regional accumulation of pathology and might be of aid in the clinical detection of abnormal ageing.

Neurovascular Unit: Basic and Clinical Imaging with Emphasis on Advantages of Ferumoxytol

Physiological and pathological processes that increase or decrease the central nervous system's need for nutrients and oxygen via changes in local blood supply act primarily at the level of the neurovascular unit (NVU). The NVU consists of endothelial cells, associated blood-brain barrier tight junctions, basal lamina, pericytes, and parenchymal cells, including astrocytes, neurons, and interneurons. Knowledge of the NVU is essential for interpretation of central nervous system physiology and pathology as revealed by conventional and advanced imaging techniques. This article reviews current strategies for interrogating the NVU, focusing on vascular permeability, blood volume, and functional imaging, as assessed by ferumoxytol an iron oxide nanoparticle.

Reliable local dynamics in the brain across sessions are revealed by whole-brain modeling of resting state activity

Resting state fMRI is a tool for studying the functional organization of the human brain. Ongoing brain activity at "rest" is highly dynamic, but procedures such as correlation or independent component analysis treat functional connectivity (FC) as if, theoretically, it is stationary and therefore the fluctuations observed in FC are thought as noise. Consequently, FC is not usually used as a single-subject level marker and it is limited to group studies. Here we develop an imaging-based technique capable of reliably portraying information of local dynamics at a single-subject level by using a whole-brain model of ongoing dynamics that estimates a local parameter, which reflects if each brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting-state sessions of one single subject and single resting-state sessions from a group of 50 participants we demonstrate that brain dynamics can be quantified consistently with respect to group dynamics using a scanning time of 20 min. We show that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger heterogeneity in its values compared to other lobules. Nevertheless, frontal regions and hubs showed higher consistency within the same subject while the inter-session variability found in primary visual and motor areas was only as high as the one found across subjects. The framework presented here can be used to study functional brain dynamics at group and, more importantly, at individual level, opening new avenues for possible clinical applications.

Neurodevelopmental correlates of the emerging adult self

The self-concept – the set of beliefs that a person has about themselves – shows significant development from adolescence to early adulthood, in parallel with brain development over the same period. We sought to investigate how age-related changes in self-appraisal processes corresponded with brain network segregation and integration in healthy adolescents and young adults. We scanned 88 participants (46 female), aged from 15 to 25 years, as they performed a self-appraisal task. We first examined their patterns of activation to self-appraisal, and replicated prior reports of reduced dorsomedial prefrontal cortex activation with older age, with similar reductions in precuneus, right anterior insula/operculum, and a region extending from thalamus to striatum. We used independent component analysis to identify distinct anterior and posterior components of the default mode network (DMN), which were associated with the self-appraisal and rest-fixation parts of the task, respectively. Increasing age was associated with reduced functional connectivity between the two components. Finally, analyses of task-evoked interactions between pairs of nodes within the DMN identified a subnetwork that demonstrated reduced connectivity with increasing age. Decreased network integration within the DMN appears to be an important higher-order maturational process supporting the emerging adult self.

A study of within-subject reliability of the brain's default-mode network

Objective

Resting-state functional magnetic resonance imaging (fMRI) is promising for Alzheimer’s disease (AD). This study aimed to examine short-term reliability of the default-mode network (DMN), one of the main haemodynamic patterns of the brain.

Materials and methods

Using a 1.5 T Philips Achieva scanner, two consecutive resting-state fMRI runs were acquired on 69 healthy adults, 62 patients with mild cognitive impairment (MCI) due to AD, and 28 patients with AD dementia. The anterior and posterior DMN and, as control, the visual-processing network (VPN) were computed using two different methodologies: connectivity of predetermined seeds (theory-driven) and dual regression (data-driven). Divergence and convergence in network strength and topography were calculated with paired t tests, global correlation coefficients, voxel-based correlation maps, and indices of reliability.

Results

No topographical differences were found in any of the networks. High correlations and reliability were found in the posterior DMN of healthy adults and MCI patients. Lower reliability was found in the anterior DMN and in the VPN, and in the posterior DMN of dementia patients.

Discussion

Strength and topography of the posterior DMN appear relatively stable and reliable over a short-term period of acquisition but with some degree of variability across clinical samples.

fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network

Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state. Methods: We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later. Results: Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 < |r| < 0.346, p < 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time. Discussion: We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated.

A robust dissociation among the language, multiple demand, and default mode networks: Evidence from inter-region correlations in effect size

Complex cognitive processes, including language, rely on multiple mental operations that are carried out by several large-scale functional networks in the frontal, temporal, and parietal association cortices of the human brain. The central division of cognitive labor is between two fronto-parietal bilateral networks: (a) the multiple demand (MD) network, which supports executive processes, such as working memory and cognitive control, and is engaged by diverse task domains, including language, especially when comprehension gets difficult; and (b) the default mode network (DMN), which supports introspective processes, such as mind wandering, and is active when we are not engaged in processing external stimuli. These two networks are strongly dissociated in both their functional profiles and their patterns of activity fluctuations during naturalistic cognition. Here, we focus on the functional relationship between these two networks and a third network: (c) the fronto-temporal left-lateralized "core" language network, which is selectively recruited by linguistic processing. Is the language network distinct and dissociated from both the MD network and the DMN, or is it synchronized and integrated with one or both of them? Recent work has provided evidence for a dissociation between the language network and the MD network. However, the relationship between the language network and the DMN is less clear, with some evidence for coordinated activity patterns and similar response profiles, perhaps due to the role of both in semantic processing. Here we use a novel fMRI approach to examine the relationship among the three networks: we measure the strength of activations in different language, MD, and DMN regions to functional contrasts typically used to identify each network, and then test which regions co-vary in their contrast effect sizes across 60 individuals. We find that effect sizes correlate strongly within each network (e.g., one language region and another language region, or one DMN region and another DMN region), but show little or no correlation for region pairs across networks (e.g., a language region and a DMN region). Thus, using our novel method, we replicate the language/MD network dissociation discovered previously with other approaches, and also show that the language network is robustly dissociated from the DMN, overall suggesting that these three networks contribute to high-level cognition in different ways and, perhaps, support distinct computations. Inter-individual differences in effect sizes therefore do not simply reflect general differences in vascularization or attention, but exhibit sensitivity to the functional architecture of the brain. The strength of activation in each network can thus be probed separately in studies that attempt to link neural variability to behavioral or genetic variability.

Assessment of the impact of shared brain imaging data on the scientific literature

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally.

Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm

The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events.

Up-regulation of DMN Connectivity in Mild Cognitive Impairment Via Network-based Cognitive Training

BACKGROUND

Previous work designed a network-based protocol of cognitive training. This programme exploits a mechanism of induced task-oriented co-activation of multiple regions that are part of the default mode network (DMN), to induce functional rewiring and increased functional connectivity within this network.

OBJECTIVE

In this study, the programme was administered to patients with a diagnosis of mild cognitive impairment to test its effects in a clinical sample.

METHOD

Twenty-three patients with mild cognitive impairment (mean age: 73.74 years, standard deviation 5.13, female/male ratio 13/10) allocated to the experimental condition, underwent one month of computerised training, while fourteen patients (mean age: 73.14 years, standard deviation 6.16, female/ male ratio 7/7) assigned to the control condition underwent a regime of intense social engagement. Patients were in the prodromal stage of Alzheimer's disease (AD) as confirmed by clinical follow ups for at least two years. The DMN was computed at baseline and retest, together with other, control patterns of connectivity, grey matter maps and neuropsychological profiles.

RESULTS

A condition-by-timepoint interaction indicating increased connectivity triggered by the programme was found in left parietal DMN regions. No decreases as well as no changes in the other networks or morphology were found. Although between-condition cognitive changes did not reach statistical significance, they correlated positively with changes in DMN connectivity in the left parietal region, supporting the hypothesis that parietal changes were beneficial.

CONCLUSION

This programme of cognitive training up-regulates a pattern of connectivity which is pathologically down-regulated in AD. We argue that, when cognitive interventions are conceptualised as tools to induce co-activation repeatedly, they can lead to clinically relevant improvements in brain functioning, and can be of aid in support of pharmacological and other interventions in the earliest stages of AD.

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.

Signal sampling for efficient sparse representation of resting state FMRI data

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain's signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain's signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority.

Coupling of Action-Perception Brain Networks during Musical Pulse Processing: Evidence from Region-of-Interest-Based Independent Component Analysis

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model (GLM) approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach (independent component analysis; ICA). Participants' (18 musicians and 18 controls) functional magnetic resonance imaging (fMRI) responses were acquired while listening to music. A targeted region of interest (ROI) related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i.e., under a varying number of assumed independent sources, to avoid relying on prior model order assumptions. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory (perception) and motor (action) areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders. Results revealed (a) a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and (b) group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing.

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks.

Resting-state networks distinguish locked-in from vegetative state patients

PURPOSE

Locked-in syndrome and vegetative state are distinct outcomes from coma. Despite their differences, they are clinically difficult to distinguish at the early stage and current diagnostic tools remain insufficient. Since some brain functions are preserved in locked-in syndrome, we postulated that networks of spontaneously co-activated brain areas might be present in locked-in patients, similar to healthy controls, but not in patients in a vegetative state.

METHODS

Five patients with locked-in syndrome, 12 patients in a vegetative state and 19 healthy controls underwent a resting-state fMRI scan. Individual spatial independent component analysis was used to separate spontaneous brain co-activations from noise. These co-activity maps were selected and then classified by two raters as either one of eight resting-state networks commonly shared across subjects or as specific to a subject.

RESULTS

The numbers of spontaneous co-activity maps, total resting-state networks, and resting-state networks underlying high-level cognitive activity were shown to differentiate controls and locked-in patients from patients in a vegetative state. Analyses of each common resting-state network revealed that the default mode network accurately distinguished locked-in from vegetative-state patients. The frontoparietal network also had maximum specificity but more limited sensitivity.

CONCLUSIONS

This study reinforces previous reports on the preservation of the default mode network in locked-in syndrome in contrast to vegetative state but extends them by suggesting that other networks might be relevant to the diagnosis of locked-in syndrome. The aforementioned analysis of fMRI brain activity at rest might be a step in the development of a diagnostic biomarker to distinguish locked-in syndrome from vegetative state.

Brain Network Organization and Social Executive Performance in Frontotemporal Dementia

OBJECTIVES

Behavioral variant frontotemporal dementia (bvFTD) is characterized by early atrophy in the frontotemporoinsular regions. These regions overlap with networks that are engaged in social cognition-executive functions, two hallmarks deficits of bvFTD. We examine (i) whether Network Centrality (a graph theory metric that measures how important a node is in a brain network) in the frontotemporoinsular network is disrupted in bvFTD, and (ii) the level of involvement of this network in social-executive performance.

METHODS

Patients with probable bvFTD, healthy controls, and frontoinsular stroke patients underwent functional MRI resting-state recordings and completed social-executive behavioral measures.

RESULTS

Relative to the controls and the stroke group, the bvFTD patients presented decreased Network Centrality. In addition, this measure was associated with social cognition and executive functions. To test the specificity of these results for the Network Centrality of the frontotemporoinsular network, we assessed the main areas from six resting-state networks. No group differences or behavioral associations were found in these networks. Finally, Network Centrality and behavior distinguished bvFTD patients from the other groups with a high classification rate.

CONCLUSIONS

bvFTD selectively affects Network Centrality in the frontotemporoinsular network, which is associated with high-level social and executive profile.

Metabolic spatial connectivity in amyotrophic lateral sclerosis as revealed by independent component analysis

OBJECTIVES

Positron emission tomography (PET) and volume of interest (VOI) analysis have recently shown in amyotrophic lateral sclerosis (ALS) an accuracy of 93% in differentiating patients from controls. The aim of this study was to disclose by spatial independent component analysis (ICA) the brain networks involved in ALS pathological processes and evaluate their discriminative value in separating patients from controls.

EXPERIMENTAL DESIGN

Two hundred fifty-nine ALS patients and 40 age- and sex-matched control subjects underwent brain 18F-2-fluoro-2-deoxy-D-glucose PET (FDG-PET). Spatial ICA of the preprocessed FDG-PET images was performed. Intensity values were converted to z-scores and binary masks were used as data-driven VOIs. The accuracy of this classifier was tested versus a validated system processing intensity signals in 27 brain meta-VOIs. A support vector machine was independently applied to both datasets and the 'leave-one-out' technique verified the general validity of results.

PRINCIPAL OBSERVATIONS

The 8 components selected as pathophysiologically meaningful discriminated patients from controls with 99.0% accuracy, the discriminating value of bilateral cerebellum/midbrain alone representing 96.3%. Among the meta-VOIs, right temporal lobe alone reached an accuracy of 93.7%.

CONCLUSIONS

Spatial ICA identified in a very large cohort of ALS patients distinct spatial networks showing a high discriminatory value, improving substantially on the previously obtained accuracy. The cerebellar/midbrain component accounted for the highest accuracy in separating ALS patients from controls. Spatial ICA and multivariate analysis perform better than univariate semi-quantification methods in identifying the neurodegenerative features of ALS and pave the way for inclusion of PET in clinical trials and early diagnosis.

Identification of Voxels Confounded by Venous Signals Using Resting-State fMRI Functional Connectivity Graph Community Identification

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6±5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remaining differences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needing any additional data beyond what is usually acquired (and exported) in standard fMRI experiments.

Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI

PURPOSE

To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance.

MATERIALS AND METHODS

Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression.

RESULTS

Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50.

CONCLUSION

Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks.

Time-frequency dynamics of the sum of intra- and extracerebral hemodynamic functional connectivity during resting-state and respiratory challenges assessed by multimodal functional near-infrared spectroscopy

Monitoring respiratory processes is important for evaluating neuroimaging data, given their influence on time-frequency dynamics of intra- and extracerebral hemodynamics. Here we investigated the time-frequency dynamics of the sum of intra- and extracerebral hemodynamic functional connectivity states during hypo- and hypercapnia by using three different respiratory challenge tasks (i.e., hyperventilation, breath-holding, and rebreathing) compared to resting-state. The sum of intra- and extracerebral hemodynamic responses were assessed using functional near-infrared spectroscopy (fNIRS) within two regions of interest (i.e., the dorsolateral and the medial prefrontal cortex). Time-frequency fNIRS analysis was performed based on wavelet transform coherence to quantify functional connectivity in terms of positive and negative phase-coupling within each region of interest. Physiological measures were assessed in the form of partial end-tidal carbon dioxide, heart rate, arterial tissue oxygen saturation, and respiration rate. We found that the three respiration challenges modulated time-frequency dynamics differently with respect to resting-state: 1) Hyperventilation and breath-holding exhibited inverse patterns of positive and negative phase-coupling. 2) In contrast, rebreathing had no significant effect. 3) Low-frequency oscillations contributed to a greater extent to time-frequency dynamics compared to high-frequency oscillations. The results highlight that there exist distinct differences in time-frequency dynamics of the sum of intra- and extracerebral functional connectivity not only between hypo- (hyperventilation) and hypercapnia but also between different states of hypercapnia (breath-holding versus rebreathing). This suggests that a multimodal assessment of intra-/extracerebral and systemic physiological changes during respiratory challenges compared to resting-state may have potential use in the differentiation between physiological and pathological respiratory behavior accompanied by the psycho-physiological state of a human.

Behavioral relevance of the dynamics of the functional brain connectome

While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC (SFC). In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering-across space, time, and subjects-which allow for group-level inferences of the dynamics. Next, using a large resting-state functional magnetic resonance imaging and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion, and personality traits) as compared with SFC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.

On the generalizability of resting-state fMRI machine learning classifiers

Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.

Comparison of functional network connectivity for passive-listening and active-response narrative comprehension in adolescents

Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14-18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task.

The spectral diversity of resting-state fluctuations in the human brain

In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms) and compared to 20 resting-state datasets from standard, high-TR (1800 ms) EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1–0.25 Hz; 0.25–0.75 Hz; 0.75–1.4 Hz) was computed for both the low-TR and (for the two lower-frequency ranges) the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low-TR EPI sequences for resting-state and potentially also task-related fMRI experiments.

Scanning fast and slow: current limitations of 3 Tesla functional MRI and future potential

Functional MRI at 3T has become a workhorse for the neurosciences, e.g., neurology, psychology, and psychiatry, enabling non-invasive investigation of brain function and connectivity. However, BOLD-based fMRI is a rather indirect measure of brain function, confounded by physiology related signals, e.g., head or brain motion, brain pulsation, blood flow, intermixed with susceptibility differences close or distant to the region of neuronal activity. Even though a plethora of preprocessing strategies have been published to address these confounds, their efficiency is still under discussion. In particular, physiological signal fluctuations closely related to brain supply may mask BOLD signal changes related to "true" neuronal activation. Here we explore recent technical and methodological advancements aimed at disentangling the various components, employing fast multiband vs. standard EPI, in combination with fast temporal ICA. Our preliminary results indicate that fast (TR <0.5 s) scanning may help to identify and eliminate physiologic components, increasing tSNR and functional contrast. In addition, biological variability can be studied and task performance better correlated to other measures. This should increase specificity and reliability in fMRI studies. Furthermore, physiological signal changes during scanning may then be recognized as a source of information rather than a nuisance. As we are currently still undersampling the complexity of the brain, even at a rather coarse macroscopic level, we should be very cautious in the interpretation of neuroscientific findings, in particular when comparing different groups (e.g., age, sex, medication, pathology, etc.). From a technical point of view our goal should be to sample brain activity at layer specific resolution with low TR, covering as much of the brain as possible without violating SAR limits. We hope to stimulate discussion toward a better understanding and a more quantitative use of fMRI.

Estimating brain network activity through back-projection of ICA components to GLM maps

Independent component analysis (ICA) is a data-driven approach frequently used in neuroimaging to model functional brain networks. Despite ICA's increasing popularity, methods for replicating published ICA components across independent datasets have been underemphasized. Traditionally, the task-dependent activation of a component is evaluated by first back-projecting the component to a functional MRI (fMRI) dataset, then performing general linear modeling (GLM) on the resulting timecourse. We propose the alternative approach of back-projecting the component directly to univariate GLM results. Using a sample of 37 participants performing the Multi-Source Interference Task, we demonstrate these two approaches to yield identical results. Furthermore, while replicating an ICA component requires back-projection of component beta-values (βs), components are typically depicted only by t-scores. We show that while back-projection of component βs and t-scores yielded highly correlated results (ρ=0.95), group-level statistics differed between the two methods. We conclude by stressing the importance of reporting ICA component βs, rather than component t-scores, so that functional networks may be independently replicated across datasets.

Opposite modulation of brain functional networks implicated at low vs. high demand of attention and working memory

BACKGROUND

Functional magnetic resonance imaging (fMRI) studies indicate that the brain organizes its activity into multiple functional networks (FNs) during either resting condition or task-performance. However, the functions of these FNs are not fully understood yet.

METHODOLOGY/PRINCIPAL FINDINGS

To investigate the operation of these FNs, spatial independent component analysis (sICA) was used to extract FNs from fMRI data acquired from healthy participants performing a visual task with two levels of attention and working memory load. The task-related modulations of extracted FNs were assessed. A group of FNs showed increased activity at low-load conditions and reduced activity at high-load conditions. These FNs together involve the left lateral frontoparietal cortex, insula, and ventromedial prefrontal cortex. A second group of FNs showed increased activity at high-load conditions and reduced activity at low-load conditions. These FNs together involve the intraparietal sulcus, frontal eye field, lateral frontoparietal cortex, insula, and dorsal anterior cingulate, bilaterally. Though the two groups of FNs showed opposite task-related modulations, they overlapped extensively at both the lateral and medial frontoparietal cortex and insula. Such an overlap of FNs would not likely be revealed using standard general-linear-model-based analyses.

CONCLUSIONS

By assessing task-related modulations, this study differentiated the functional roles of overlapping FNs. Several FNs including the left frontoparietal network are implicated in task conditions of low attentional load, while another set of FNs including the dorsal attentional network is implicated in task conditions involving high attentional demands.

What is a representative brain? Neuroscience meets population science

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective--population neuroscience--that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this article, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, for example, subjects with mental disorders or neurodegenerative diseases such as Parkinson's as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation.

Default mode network connectivity and reciprocal social behavior in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) is a genetic mutation associated with disorders of cortical connectivity and social dysfunction. However, little is known about the functional connectivity (FC) of the resting brain in 22q11DS and its relationship with social behavior. A seed-based analysis of resting-state functional magnetic resonance imaging data was used to investigate FC associated with the posterior cingulate cortex (PCC), in (26) youth with 22qDS and (51) demographically matched controls. Subsequently, the relationship between PCC connectivity and Social Responsiveness Scale (SRS) scores was examined in 22q11DS participants. Relative to 22q11DS participants, controls showed significantly stronger FC between the PCC and other default mode network (DMN) nodes, including the precuneus, precentral gyrus and left frontal pole. 22q11DS patients did not show age-associated FC changes observed in typically developing controls. Increased connectivity between PCC, medial prefrontal regions and the anterior cingulate cortex, was associated with lower SRS scores (i.e. improved social competence) in 22q11DS. DMN integrity may play a key role in social information processing. We observed disrupted DMN connectivity in 22q11DS, paralleling reports from idiopathic autism and schizophrenia. Increased strength of long-range DMN connectivity was associated with improved social functioning in 22q11DS. These findings support a 'developmental-disconnection' hypothesis of symptom development in this disorder.

The effect of scan length on the reliability of resting-state fMRI connectivity estimates

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.

Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest

Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal – be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.

RESCALE: Voxel-specific task-fMRI scaling using resting state fluctuation amplitude

The BOLD signal measured in fMRI studies depends not only on neuronal activity, but also on other parameters like tissue vascularization, which may vary between subjects and between brain regions. A correction for variance from vascularization effects can thus lead to improved group statistics by reducing inter-subject variability. The fractional amplitude of low-frequency fluctuations (fALFF) as determined in a resting-state scan has been shown to be dependent on vascularization. Here we present a correction method termed RESCALE (REsting-state based SCALing of parameter Estimates) that uses local information to compute a voxel-wise scaling factor based on the correlation structure of fALFF and task activation parameter estimates from within a cube of 3 × 3 × 3 surrounding that voxel. The scaling method was used on a visuo-motor paradigm and resulted in a consistent increase in t-values in all task-activated cortical regions, with increases in peak t-values of 37.0% in the visual cortex and 12.7% in the left motor cortex. The RESCALE method as proposed herein can be easily applied to all task-based fMRI group studies provided that resting-state data for the same subject group is also acquired.

Identifying the synaptic origin of ongoing neuronal oscillations through spatial discrimination of electric fields

Although intracerebral field potential oscillations are commonly used to study information processing during cognition and behavior, the cellular and network processes underlying such events remain unclear. The limited spatial resolution of standard single-point recordings does not clarify whether field oscillations reflect the activity of one or many afferent presynaptic populations. However, multi-site recording devices now provide high-resolution spatial profiles of local field potentials (LFPs) and when coupled to modern mathematical analyses that discriminate signals with distinct but overlapping spatial distributions, they open the door to better understand these potentials. Here we review recent insights that help disentangle certain pathway-specific activities. Accordingly, some oscillatory patterns can now be viewed as a periodic succession of synchronous synaptic currents that reflect the time envelope of spiking activity in given presynaptic populations. These analyses modify our concept of brain rhythms as abstract entities, molding them into mechanistic representations of network activity and allowing us to work in the time domain, reducing the loss of information inherent to data-chopping frequency treatment.