Individual Variability in Functional Connectivity Architecture of the Human Brain

A wealth of neuroscience evidence demonstrates that diet and nutrition play an important role in structural brain plasticity, promoting the development of gray matter volume and maintenance of white matter integrity across the lifespan. However, the role of nutrition in shaping individual differences in the functional brain connectome remains to be well established. We therefore investigated whether nutrient biomarkers known to have beneficial effects on brain structure (i.e., the omega-3 polyunsaturated fatty acids; ω-3 PUFAs), explain individual differences in functional brain connectivity within healthy older adults (N = 96). Our findings demonstrate that ω-3 PUFAs are associated with individual differences in functional connectivity within regions that support executive function (prefrontal cortex), memory (hippocampus), and emotion (amygdala), and provide key evidence that the influence of these regions on global network connectivity reliably predict general, fluid, and crystallized intelligence. The observed findings not only elucidate the role of ω-3 PUFAs in functional brain plasticity and intelligence, but also motivate future studies to examine their impact on psychological health, aging, and disease.

Individual Differences in White-Matter Microstructure Reflect Variation in Functional Connectivity during Choice

The pathogenesis of Alzheimer's disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.

Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term-equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

#155: Individualized connectivity between rTMS targets and the subgenual cingulate is unrelated to antidepressant response

The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the heteromodal association cortex but less variable in the unimodal cortices. Structural connectivity (SC) is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in SC (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1,724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the SC exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain SC and link to the prediction of individual cognitive function in adult subjects.

An fMRI study of English and Spanish word reading in bilingual adults

<bold>Objective</bold> To observe the differences in brain network activation and functional connectivity of patients with post-stroke cognitive impairment (PSCI) of different severity using functional near-infrared spectroscopy (fNIRS). <bold>Methods</bold> A total of 40 stroke patients in the outpatient and inpatient Rehabilitation Department in the Pudong District of Huashan Hospital, Fudan University from April to November 2020 were recruited and divided into normal group (<italic>n</italic>=14), mild to moderate group (<italic>n</italic>=11) and severe group (<italic>n</italic>=15) according to the mini-mental state examination (MMSE) score. Fugl-Meyer assessment scale (FMA) and functional comprehensive assessment (FCA) were used to evaluate the motor function of upper and lower limbs; 64-channel fNIRS functional imaging device was used to collect the resting-state brain network data of patients. The whole brain functional connectivity was analyzed based on oxyhemoglobin (HbO₂) and deoxyhemoglobin (HbR). Differences in functional connectivity strengths between homologous and heterologous brain networks in dorsal attention network (DAN), sensorimotor network (SEN), ventral attention network (VAN), frontoparietal network (FPN), default mode network (DMN) and visual network (VIS) regions were analyzed. <bold>Results</bold> (1) Motor function: there was no significant difference in FMA and FCA scores in three groups (<italic>P</italic>&gt;0.05). (2) Whole brain functional connectivity and strength of homologous brain network functional connectivity: there was no significant difference in whole brain functional connectivity based on HbO₂ and HbR in three groups (<italic>P</italic>&gt;0.05). Compared with the normal group, there were significant differences in the strength of functional connectivity of the in the SEN and FPN regions of the HbO₂-based homologous brain network in the severe group (<italic>P</italic>&lt;0.05), but there were no significant differences in the regions of interest of the HbR-based homologous brain network in the three groups (<italic>P</italic>&gt;0.05). (3) Functional connectivity strength of heterologous brain networks: compared with the normal group, the functional connectivity of SEN-VAN, SEN-DMN, SEN-FPN, VAN-FPN and DMN-FPN in the severe group decreased significantly, and the differences were statistically significant (<italic>P</italic>&lt;0.05). Compared with the mild to moderate group, the functional connectivity of SEN-DAN and SEN-FPN in the severe group decreased significantly, and the differences were statistically significant (<italic>P</italic>&lt;0.05). There was no significant difference of strength of functional connectivity based on HbR in the three groups (<italic>P</italic>&gt;0.05). (4) Correlation analysis between SEN-FPN heterologous functional connectivity and MMSE, FCA scores: MMSE score (<italic>r</italic>=0.32, <italic>P</italic>&lt;0.05) and FCA score (<italic>r</italic>=0.38, <italic>P</italic>&lt;0.05) were positively correlated with SEN-FPN functional connectivity. <bold>Conclusion</bold> The SEN-FPN functional connectivity may play an important role in the cognitive function of stroke patients. Altered functional connectivity patterns of SEN-FPN may be closely related to the occurrence and progression of PSCI, and changes in functional connectivity strength of SEN-FPN may be a potential imaging indicator of PSCI.

Functional connectomics is a popular approach to investigate the neural underpinnings of developmental disorders of which attention deficit hyperactivity disorder (ADHD) is one of the most prevalent. Nonetheless, neuronal mechanisms driving the aberrant functional connectivity resulting in ADHD symptoms remain largely unclear. Whereas resting state activity reflecting intrinsic tonic background activity is only vaguely connected to behavioral effects, naturalistic neuroscience has provided means to measure phasic brain dynamics associated with overt manifestation of the symptoms. Here we collected functional magnetic resonance imaging (fMRI) data in three experimental conditions, an active virtual reality (VR) task where the participants execute goal-directed behaviors, a passive naturalistic Video Viewing task, and a standard Resting State condition. Thirty-nine children with ADHD and thirty-seven typically developing (TD) children participated in this preregistered study. Functional connectivity was examined with network-based statistics (NBS) and graph theoretical metrics. During the naturalistic VR task, the ADHD group showed weaker task performance and stronger functional connectivity than the TD group. Group differences in functional connectivity were observed in widespread brain networks: particularly subcortical areas showed hyperconnectivity in ADHD. More restricted group differences in functional connectivity were observed during the Video Viewing, and there were no group differences in functional connectivity in the Resting State condition. These observations were consistent across NBS and graph theoretical analyses, although NBS revealed more pronounced group differences. Furthermore, during the VR task and Video Viewing, functional connectivity in TD controls was associated with task performance during the measurement, while Resting State activity in TD controls was correlated with ADHD symptoms rated over six months. We conclude that overt expression of the symptoms is correlated with aberrant brain connectivity in ADHD. Furthermore, naturalistic paradigms where clinical markers can be coupled with simultaneously occurring brain activity may further increase the interpretability of psychiatric neuroimaging findings.

Naltrexone, an opioid receptor antagonist that is Food and Drug Administration approved for treating alcohol use disorder (AUD), reduces alcohol craving and intake. Despite known pharmacological properties, little is known regarding the effects of naltrexone on neural circuit function. Thus, a data-driven examination of the neural effects of naltrexone in human subjects may offer novel insight into its treatment mechanisms. Twenty-one alcohol using males (22 to 39) participated in a double-blind, placebo-controlled crossover study of the effects of naltrexone on brain voxel-wise functional connectivity (FC) using intersubject FC correlation mapping. We first cross-correlated the time series from each gray matter voxel to produce a 6,356×6,356 FC matrix for each subject and session. We then subtracted the placebo FC matrix from the naltrexone FC matrix. To identify brain regions demonstrating significant reconfiguration of whole-brain FC patterns following naltrexone treatment, we statistically quantified the consistency of patterns of voxel FC changes across subjects. Permutation testing identified significant clusters of voxels undergoing significant reconfiguration. Using the identified clusters in a seed-based FC analysis, we then compared the FC patterns of affected brain areas on placebo versus naltrexone in a paired t-test. Ridge regression analyses identified self-report measures, including substance use, that significantly predicted individual differences in FC among naltrexone-modulated regions. Two clusters in the rostral anterior cingulate cortex (rACC)/ventromedial prefrontal cortex (vmPFC) demonstrated significant modulation of FC by naltrexone. Using these 2 proximal clusters as a single seed, specific FC changes were identified in regions associated with a left frontoparietal network (increasing), as well as visual and motor regions (decreasing). Stronger FC between the rACC/vmPFC and this set of regions on placebo was associated with more external locus of control, whereas weaker connectivity was associated with greater substance use problems. Naltrexone strengthened these connections most among individuals who reported greater drinking to cope. Enhancing connectivity between the rACC/vmPFC, implicated in alcohol craving, and components of a left frontoparietal network involved in executive control may represent an effective strategy for the treatment of AUD.

Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability.

Previous voxel-based morphometry (VBM) and cortical thickness (CT) studies on Parkinson's disease (PD) have mainly reported the gray matter size reduction, whereas the shape of cortical surface can also change in PD patients. For the first time, we analyzed sulcal depth (SD) patterns in PD patients by using whole brain region of interest (ROI)-based approach. In a cross-sectional study, high-resolution brain structural MRI images were collected from 60 PD patients without dementia and 56 age-and sex-matched healthy controls (HC). SD and CT were estimated using the Computational Anatomy Toolbox (CAT12) and statistically compared between groups on whole brain ROI-based level using statistical parametric mapping 12 (SPM12). Additionally, correlations between regional brain changes and clinical variables were also examined. Compared to HC, PD patients showed lower SD in widespread regions, including temporal (the bilateral transverse temporal, the left inferior temporal, the right middle temporal and the right superior temporal), insular (the left insula), frontal (the left pars triangularis, the left pars opercularis and the left precentral), parietal (the bilateral superior parietal) and occipital (the right cuneus) regions. For CT, only the left pars opercularis showed lower CT in PD patients compared to HC. No regions showed higher SD or CT in PD patients compared to HC. In PD patients, a significant positive correlation was found between SD of the left pars opercularis and MMSE scores, such that lower MMSE scores were related to lower SD of the left pars opercularis. Our results of widespread lower SD, but relatively localized lower CT, indicate that SD seems to be more sensitive to brain changes than CT and may be mainly affected by white matter damage. Hence, SD may be a more promising indicator to investigate the surface shape changes in PD patients. The significant positive correlation between SD of the left pars opercularis and MMSE scores suggests that SD may be prognostic of future cognitive decline.