Changes in the interaction of resting-state neural networks from adolescence to adulthood.
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ABSTRACT:
SUBMITTER: Stevens MC
PROVIDER: S-EPMC6788906 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional magnetic resonance imaging data from three cohorts of healthy older adults (total N = 563; age = 55-95 years) and a younger-adult cohort (N = 128; age = 18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (P = 2.3 × 10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.
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Project description:Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation ("modularity") of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors.
| S-EPMC3643719 | biostudies-other
Project description:Resting-state functional connectivity MRI (rs-fcMRI) analyses have identified intrinsic neural networks supporting domain-general cognitive functions including language, attention, executive control and memory. The brain, however, also has a domain-specific organization, including regions that contribute to perceiving and knowing about others (the 'social' system) or manipulable objects designed to perform specific functions (the 'tool' system). These 'social' and 'tool' systems, however, might not constitute intrinsic neural networks per se, but rather only come online as needed to support retrieval of domain-specific information during social- or tool-related cognitive tasks. To address this issue, we functionally localized two regions in lateral temporal cortex activated when subjects perform social- and tool conceptual tasks. We then compared the strength of the correlations with these seed regions during rs-fcMRI. Here, we show that the 'social' and 'tool' neural networks are maintained even when subjects are not engaged in social- and tool-related information processing, and so constitute intrinsic domain-specific neural networks.
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Project description:BackgroundExternalizing behavior has been attributed, in part, to decreased frontolimbic control over amygdala activation. However, little is known about developmental trajectories of frontoamygdalar functional connectivity and its relation to externalizing behavior. The present study addresses this gap by examining longitudinal associations between adolescent and adult externalizing behavior and amygdala-anterior cingulate cortex (ACC) and amygdala-orbitofrontal cortex (OFC) resting-state functional connectivity in a sample of 111 typically developing participants aged 11-23 at baseline.MethodsParticipants completed two-to-four data waves spaced approximately two years apart, resulting in a total of 309 data points. At each data wave, externalizing behavior was measured using the Externalizing Behavior Broadband Scale from the Achenbach Youth/Adult Self-Report questionnaire. Resting-state fMRI preprocessing was performed using FSL. Amygdala functional connectivity was examined using AFNI. The longitudinal association between externalizing behavior and amygdala-ACC/OFC functional connectivity was examined using linear mixed effect models in R.ResultsExternalizing behavior was associated with increased amygdala-ACC and amygdala-OFC resting-state functional connectivity across adolescence and young adulthood. For amygdala-ACC connectivity, externalizing behavior at baseline primarily drove this association, whereas for amygdala-OFC functional connectivity, change in externalizing behavior relative to baseline drove the main effect of externalizing behavior on amygdala-OFC functional connectivity. No evidence was found for differential developmental trajectories of frontoamygdalar connectivity for different levels of externalizing behavior (i.e., age-by-externalizing behavior interaction effect).ConclusionsHigher externalizing behavior is associated with increased resting-state attunement between the amygdala and ACC/OFC, perhaps indicating a generally more vigilant state for neural networks important for emotional processing and control.
| S-EPMC8359311 | biostudies-literature
Project description:Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which manifest as structured spatial patterns of temporal correlations between distant brain regions. Here, we investigated whether such "physiological networks"-sets of segregated brain regions that exhibit similar responses following slow changes in systemic physiology-resemble patterns associated with large-scale networks typically attributed to remotely synchronized neuronal activity. By analyzing a large group of subjects from the 3T Human Connectome Project (HCP) database, we demonstrate brain-wide and noticeably heterogenous dynamics tightly coupled to either respiratory variation or heart rate changes. We show, using synthesized data generated from physiological recordings across subjects, that these physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the "physiological networks" seem to mimic the neuronal networks. Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent "physiological connectivity" cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses. Our results challenge previous notions that physiological confounds are either localized to large veins or globally coherent across the cortex, therefore emphasizing the necessity to consider potential physiological contributions in fMRI-based functional connectivity studies. The rich spatiotemporal patterns carried by such "physiological" dynamics also suggest great potential for clinical biomarkers that are complementary to large-scale neuronal networks.
| S-EPMC7165049 | biostudies-literature
Project description:Background:Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the proper training of deep neural networks requires thousands of exemplars. Collecting this amount of data is not feasible in typical neuroimaging experiments. A handy solution to this problem, which has largely fallen outside the scope of deep learning applications in neuroimaging, is to repurpose deep networks that have already been trained on large datasets by fine-tuning them to target datasets/tasks with fewer exemplars. Here, we investigated how this method, called transfer learning, can aid age category classification and regression based on brain functional connectivity patterns derived from resting-state functional magnetic resonance imaging. We trained a connectome-convolutional neural network on a larger public dataset and then examined how the knowledge learned can be used effectively to perform these tasks on smaller target datasets collected with a different type of scanner and/or imaging protocol and pre-processing pipeline. Results:Age classification on the target datasets benefitted from transfer learning. Significant improvement (?9%-13% increase in accuracy) was observed when the convolutional layers' weights were initialized based on the values learned on the public dataset and then fine-tuned to the target datasets. Transfer learning also appeared promising in improving the otherwise poor prediction of chronological age. Conclusions:Transfer learning is a plausible solution to adapt convolutional neural networks to neuroimaging data with few exemplars and different data acquisition and pre-processing protocols.
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Project description:The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.
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Project description:Previous cross-sectional imaging studies found differences in brain structure and in resting state networks between presymptomatic carriers of mutations in C9orf72 (C9+) and healthy controls. We carried out a prospective longitudinal study of clinical and resting state functional imaging in a cohort of 15 presymptomatic C9+ carriers to determine whether differences in resting state connectivity prior to developing symptoms reflect static developmental differences or ongoing low-grade degenerative changes. Presymptomatic C9+ carriers were scanned at baseline with follow-up scanning at 6- and 18-months and compared to a cohort of 14 healthy controls scanned longitudinally. Resting state networks associated with manifest disease were visualized by comparing 27 symptomatic C9+ carriers to 34 healthy controls. Motor, salience, thalamic, and speech production networks were visualized using a seed-based analysis. Neurofilament light chain was measured in serum obtained at the time of the scans. Neither clinical measures of motor, cognitive, and behavioral function nor neurofilament levels changed over follow-up in presymptomatic C9+ carriers. In thalamic networks, there was a reduction in connectivity in presymptomatic carriers at all timepoints with a constant difference compared to healthy controls. In contrast, precuneus/posterior cingulate regions exhibited declining functional connectivity compared to controls over the 18-month follow-up, particularly in motor networks. These were regions that also exhibited reduced functional connectivity in symptomatic C9+ carriers. Reduced connectivity over time also occurred in small regions of frontal and temporal cortex within salience and thalamic networks in presymptomatic C9+ carriers. A few areas of increased connectivity occurred, including cortex near the motor seed and within the speech production network. Overall, changes in functional connectivity over time favor the explanation of ongoing low-grade alterations in presymptomatic C9+ carriers in most networks, with the exception of thalamic networks where functional connectivity reductions were stable over time. The loss of connectivity to parietal cortex regions in several different networks may be a distinct feature of C9orf72-related degeneration. Longitudinal studies of carriers who phenoconvert will be important to determine the prognostic significance of presymptomatic functional connectivity alterations.
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Project description:Alzheimer's disease (AD) is a neurodegenerative disorder that prominently affects cerebral connectivity. Assessing the functional connectivity at rest, recent functional MRI (fMRI) studies reported on the existence of resting-state networks (RSNs). RSNs are characterized by spatially coherent, spontaneous fluctuations in the blood oxygen level-dependent signal and are made up of regional patterns commonly involved in functions such as sensory, attention, or default mode processing. In AD, the default mode network (DMN) is affected by reduced functional connectivity and atrophy. In this work, we analyzed functional and structural MRI data from healthy elderly (n = 16) and patients with amnestic mild cognitive impairment (aMCI) (n = 24), a syndrome of high risk for developing AD. Two questions were addressed: (i) Are any RSNs altered in aMCI? (ii) Do changes in functional connectivity relate to possible structural changes? Independent component analysis of resting-state fMRI data identified eight spatially consistent RSNs. Only selected areas of the DMN and the executive attention network demonstrated reduced network-related activity in the patient group. Voxel-based morphometry revealed atrophy in both medial temporal lobes (MTL) of the patients. The functional connectivity between both hippocampi in the MTLs and the posterior cingulate of the DMN was present in healthy controls but absent in patients. We conclude that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration. We interpret our finding as a proof of principle, demonstrating that functional brain disorders can be characterized by functional-disconnectivity profiles of RSNs.
| S-EPMC2141850 | biostudies-other
Project description:Mixed findings exist in studies comparing brain responses to reward in adolescents and adults. Here we examined the trajectories of brain response, functional connectivity and task-modulated network properties during reward processing with a large-sample longitudinal design. Participants from the IMAGEN study performed a Monetary Incentive Delay task during fMRI at timepoint 1 (T1; n = 1304, mean age=14.44 years old) and timepoint 2 (T2; n = 1241, mean age=19.09 years). The Alcohol Use Disorders Identification Test (AUDIT) was administrated at both T1 and T2 to assess a participant's alcohol use during the past year. Voxel-wise linear mixed effect models were used to compare whole brain response as well as functional connectivity of the ventral striatum (VS) during reward anticipation (large reward vs no-reward cue) between T1 and T2. In addition, task-modulated networks were constructed using generalized psychophysiological interaction analysis and summarized with graph theory metrics. To explore alcohol use in relation to development, participants with no/low alcohol use at T1 but increased alcohol use to hazardous use level at T2 (i.e., participants with AUDIT≤2 at T1 and ≥8 at T2) were compared against those with consistently low scores (i.e., participants with AUDIT≤2 at T1 and ≤7 at T2). Across the whole sample, lower brain response during reward anticipation was observed at T2 compared with T1 in bilateral caudate nucleus, VS, thalamus, midbrain, dorsal anterior cingulate as well as left precentral and postcentral gyrus. Conversely, greater response was observed bilaterally in the inferior and middle frontal gyrus and right precentral and postcentral gyrus at T2 (vs. T1). Increased functional connectivity with VS was found in frontal, temporal, parietal and occipital regions at T2. Graph theory metrics of the task-modulated network showed higher inter-regional connectivity and topological efficiency at T2. Interactive effects between time (T1 vs. T2) and alcohol use group (low vs. high) on the functional connectivity were observed between left middle temporal gyrus and right VS and the characteristic shortest path length of the task-modulated networks. Collectively, these results demonstrate the utility of the MID task as a probe of typical brain response and network properties during development and of differences in these features related to adolescent drinking, a reward-related behaviour associated with heightened risk for future negative health outcomes.
| S-EPMC8668439 | biostudies-literature