Development of human brain cortical network architecture during infancy

The brain’s mature functional network architecture has been extensively studied but the early emergence of the brain’s network organization remains largely unknown. In this study, leveraging a large sample (143 subjects) with longitudinal rsfMRI scans (333 datasets), we aimed to characterize the important developmental process of the brain’s functional network architecture during the first two years of life. Based on spatial independent component analysis and longitudinal linear mixed effect modeling, our results unveiled the detailed topology and growth trajectories of nine cortical functional networks. Within networks, our findings clearly separated the brains networks into two categories: primary networks were topologically adult-like in neonates while higher-order networks were topologically incomplete and isolated in neonates but demonstrated consistent synchronization during the first two years of life (connectivity increases 0.13~0.35). Between networks, our results demonstrated both network-level connectivity decreases (−0.02~−0.64) and increases (0.05~0.18) but decreasing connections (n=14) dominated increasing ones (n=5). Finally, significant sex differences were observed with boys demonstrating faster network-level connectivity increases among the two frontoparietal networks (growth rate was 1.63e-4 per day for girls and 2.69e-4 per day for boys, p<1e-4). Overall, our study delineated the development of the whole brain functional architecture during the first two years of life featuring significant changes of both within- and between-network interactions

Quality control in resting-state fMRI: the benefits of visual inspection

Background: A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or “scrubbing” volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection. Results: The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts. Conclusion: Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis

Novel Biomarkers of Physical Activity Maintenance in Midlife Women: Preliminary Investigation

The precision health initiative is leading the discovery of novel biomarkers as important indicators of biological processes or responses to behavior, such as physical activity. Neural biomarkers identified by magnetic resonance imaging (MRI) hold promise to inform future research, and ultimately, for transfer to the clinical setting to optimize health outcomes. This study investigated resting-state and functional brain biomarkers between midlife women who were maintaining physical activity in accordance with the current national guidelines and previously acquired age-matched sedentary controls. Approval was obtained from the Human Subjects Committee. Participants included nondiabetic, healthy weight to overweight (body mass index 19–29.9 kg/m2) women (n = 12) aged 40–64 years. Control group data were used from participants enrolled in our previous functional MRI study and baseline resting-state MRI data from a subset of sedentary (week) midlife women who were enrolled in a 9-month exercise intervention conducted in our imaging center. Differential activation of the inferior frontal gyrus (IFG) and greater connectivity with the dorsolateral prefrontal cortex (dlPFC) was identified between physically active women and sedentary controls. After correcting for multiple comparisons, these differences in biomarkers of physical activity maintenance did not reach statistical significance. Preliminary evidence in this small sample suggests that neural biomarkers of physical activity maintenance involve activations in the brain region associated with areas involved in implementing goal-directed behavior. Specifically, activation of the IFG and connectivity with the dlPFC is identified as a neural biomarker to explain and predict long-term physical activity maintenance for healthy aging. Future studies should evaluate these biomarker links with relevant clinical correlations

Trait anxiety is associated with attentional brain networks

Trait anxiety is a well-established risk factor for anxiety and depressive disorders, yet its neural correlates are not clearly understood. In this study, we investigated the neural correlates of trait anxiety in a large sample (n = 179) of individuals who completed the trait and state versions of the State-Trait Anxiety Inventory and underwent resting-state functional magnetic resonance imaging. We used independent component analysis to characterize individual resting-state networks (RSNs), and multiple regression analyses to assess the relationship between trait anxiety and intrinsic connectivity. Trait anxiety was significantly associated with intrinsic connectivity in different regions of three RSNs (dorsal attention network, default mode network, and auditory network) when controlling for state anxiety. These RSNs primarily support attentional processes. Notably, when state anxiety was not controlled for, a different pattern of results emerged, highlighting the importance of considering this factor in assessing the neural correlates of trait anxiety. Our findings suggest that trait anxiety is uniquely associated with resting-state brain connectivity in networks mainly supporting attentional processes. Moreover, controlling for state anxiety is crucial when assessing the neural correlates of trait anxiety. These insights may help refine current neurobiological models of anxiety and identify potential targets for neurobiologically-based interventions

Functional connectivity in healthy subjects is nonlinearly modulated by the COMT and DRD2 polymorphisms in a functional system-dependent manner

The dopamine system is known to modulate brain function in an inverted U-shaped manner. Recently, the functional networks of the brain were categorized into two systems, a "control system" and a "processing system." However, it remains unclear whether the inverted U-shaped model of dopaminergic modulation could be applied to both of these functional systems. The catechol-O-methyltransferase (COMT) and dopamine D2 receptor (DRD2) were genotyped in 258 healthy young human subjects. The local and long-range functional connectivity densities (FCDs) of each voxel were calculated and compared in a voxel-wise manner using a two-way (COMT and DRD2 genotypes) analysis of covariance. The resting-state functional connectivity analysis was performed to determine the functional networks to which brain regions with significant FCD differences belonged. Significant COMT × DRD2 interaction effects were found in the local FCDs of the superior portion of the right temporal pole (sTP) and left lingual gyrus (LG) and in the long-range FCDs of the right putamen and left medial prefrontal cortex (MPFC). Post hoc tests showed nonlinear relationships between the genotypic subgroups and FCD. In the control system, the sTP and putamen, components of the salience network, showed a U-shaped modulation by dopamine signaling. In the processing system, however, the MPFC of the default-mode network and the LG of the visual network showed an inverted U-shaped modulation by the dopamine system. Our findings suggest an interaction between COMT and DRD2 genotypes and show a functional system-dependent modulation of dopamine signaling

Default mode network maturation and environmental adversities during childhood

Default mode network (DMN) plays a central role in cognition and brain disorders. It has been shown that adverse environmental conditions impact neurodevelopment, but how these conditions impact in DMN maturation is still poorly understood. This article reviews representative neuroimaging functional studies addressing the interactions between DMN development and environmental factors, focusing on early life adversities, a critical period for brain changes. Studies focused on this period of life offer a special challenge: to disentangle the neurodevelopmental connectivity changes from those related to environmental conditions. We first summarized the literature on DMN maturation, providing an overview of both typical and atypical development patterns in childhood and early adolescence. Afterward, we focused on DMN changes associated with chronic exposure to environmental adversities during childhood. This summary suggests that changes in DMN development could be a potential allostatic neural feature associated with an embodiment of environmental circumstances. Finally, we discuss about some key methodological issues that should be considered in paradigms addressing environmental adversities and open questions for future investigations

Preschool Executive Function Predicts Childhood Resting State Functional Connectivity and ADHD and Depression

Background: Measures of executive function (EF), such as the Behavior Rating Inventory of Executive Function, distinguish children with Attention-Deficit/Hyperactivity Disorder (ADHD) from control children, but less work has examined relationships to depression or brain network organization. This study examined whether early childhood EF predicted a new onset or worsening of ADHD and/or depression, and examined how early childhood EF related to functional connectivity of brain networks at school age. Methods: Participants were 247 children, enrolled at ages 3-6, from a prospective study of emotion development. The BRIEF Global Executive Composite (BRIEF-GEC) was used as the measure of EF in early childhood to predict subsequent ADHD and depression diagnoses and symptoms across school age. Resting state fMRI network analyses examined global efficiency in the frontal-parietal, cingulo-opercular, salience, and default mode networks and six ‘hub’ seed regions selected to examine seed-based connectivity. Results: Early childhood BRIEF-GEC predicted worsening and new onsets of ADHD and depression across school age. Increasing EF deficits predicted increased global efficiency in the salience network and altered connectivity with four regions for the dorsal anterior cingulate hub and one region with the insula hub. This altered connectivity was related to increasing ADHD and depression symptoms. Conclusions: Early executive deficits may be an early common liability for risk of developing ADHD and/or depression and were associated with altered functional connectivity in networks and hub regions relevant to executive processes. Future work could help clarify whether the same subtypes of EF deficits are implicated in the development of both disorders

Frequency-Dependent Relationship Between Resting-State fMRI and Glucose Metabolism in the Elderly

Both glucose metabolism and resting-state fMRI (RS-fMRI) signal reflect hemodynamic features. The objective of this study was to investigate their relationship in the resting-state in healthy elderly participants (n = 18). For RS-fMRI signal, regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), and degree of centrality (DC) maps were generated in multiple frequency bands. Glucose uptake was acquired with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). Linear correlation of each pair of the FDG-PET and RS-fMRI metrics was explored both in across-voxel way and in across-subject way. We found a significant across-voxel correlation between the FDG-PET and BOLD-fMRI metrics. However, only a small portion of voxels showed significant across-subject correlation between FDG-PET and BOLD-fMRI metrics. All these results were similar across all frequency bands of RS-fMRI data. The current findings indicate that FDG-PET and RS-fMRI metrics share similar spatial pattern (significant across-voxel correlation) but have different underlying physiological importance (non-significant across-subject correlation). Specifically, FDG-PET measures the mean glucose metabolism over tens of minutes, while RS-fMRI measures the dynamic characteristics. The combination of FDG-PET and RS-fMRI provides complementary information to reveal the underlying mechanisms of the brain activity and may enable more comprehensive interpretation of clinical PET-fMRI studies. Future studies would attempt to reduce the artifacts of RS-fMRI and to analyze the dynamic feature of PET signal

Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., scrubbing ) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8-24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population

Altered spontaneous brain activity in Down Syndrome and its relation with cognitive outcome

Although Down syndrome (DS) is the most common genetic cause of neurodevelopmental delay, few neuroimaging studies have explored this population. This investigation aimed to study whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences in spontaneous brain activity among young people with DS and controls and to correlate these results with cognitive outcomes. The sample comprised 18 persons with DS (age mean = 28.67, standard deviation = 4.18) and 18 controls (age mean = 28.56, standard deviation = 4.26). fALFF and ReHo analyses were performed, and the results were correlated with other cognitive variables also collected (KBIT-2 and verbal fluency test). Increased activity was found in DS using fALFF in areas involving the frontal and temporal lobes and left cerebellum anterior lobe. Decreased activity in DS was found in the left parietal and occipital lobe, the left limbic lobe and the left cerebellum posterior lobe. ReHo analysis showed increased activity in certain DS areas of the left frontal lobe and left rectus, as well as the inferior temporal lobe. The areas with decreased activity in the DS participants were regions of the frontal lobe and the right limbic lobe. Altered fALFF and ReHo were found in the DS population, and this alteration could predict the cognitive abilities of the participants. To our knowledge, this is the first study to explore regional spontaneous brain activity in a population with DS. Moreover, this study suggests the possibility of using fALFF and ReHo as biomarkers of cognitive function, which is highly important given the difficulties in cognitively evaluating this population to assess dementia. More research is needed, however, to demonstrate its utility