Heritability of the human connectome: A connectotyping study

Smaller total brain volume but not subcortical structure volume related to common genetic risk for ADHD

BACKGROUND

Mechanistic endophenotypes can inform process models of psychopathology and aid interpretation of genetic risk factors. Smaller total brain and subcortical volumes are associated with attention-deficit hyperactivity disorder (ADHD) and provide clues to its development. This study evaluates whether common genetic risk for ADHD is associated with total brain volume (TBV) and hypothesized subcortical structures in children.

METHODS

Children 7-15 years old were recruited for a case-control study (N = 312, N = 199 ADHD). Children were assessed with a multi-informant, best-estimate diagnostic procedure and motion-corrected MRI measured brain volumes. Polygenic scores were computed based on discovery data from the Psychiatric Genomics Consortium (N = 19 099 ADHD, N = 34 194 controls) and the ENIGMA + CHARGE consortium (N = 26 577).

RESULTS

ADHD was associated with smaller TBV, and altered volumes of caudate, cerebellum, putamen, and thalamus after adjustment for TBV; however, effects were larger and statistically reliable only in boys. TBV was associated with an ADHD polygenic score [β = -0.147 (-0.27 to -0.03)], and mediated a small proportion of the effect of polygenic risk on ADHD diagnosis (average ACME = 0.0087, p = 0.012). This finding was stronger in boys (average ACME = 0.019, p = 0.008). In addition, we confirm genetic variation associated with whole brain volume, via an intracranial volume polygenic score.

CONCLUSION

Common genetic risk for ADHD is not expressed primarily as developmental alterations in subcortical brain volumes, but appears to alter brain development in other ways, as evidenced by TBV differences. This is among the first demonstrations of this effect using molecular genetic data. Potential sex differences in these effects warrant further examination.

The nonhuman primate neuroimaging and neuroanatomy project

Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, 'ground truth' validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how "functional connectivity" from fMRI and "tractographic connectivity" from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.

Altered Neurocognitive Functional Connectivity and Activation Patterns Underlie Psychopathology in Preadolescence

BACKGROUND

Neurocognitive deficits are common among youth with mental disorders, and patterns of aberrant brain function generally cross diagnostic boundaries. This study investigated associations between functional neurocircuitry and broad transdiagnostic psychopathology dimensions in the critical preadolescent period when psychopathology is emerging.

METHODS

Participants were 9- to 10-year-olds from the Adolescent Brain Cognitive Development Study. Factor scores of general psychopathology, externalizing, internalizing, and thought disorder dimensions were calculated from a higher-order model of psychopathology using confirmatory factor analysis (N = 11,721) and entered as explanatory variables into linear mixed models to examine associations with resting-state functional connectivity (n = 9074) and neural activation during the emotional n-back task (n = 6146) when covarying for sex, race/ethnicity, parental education, and cognitive function.

RESULTS

All dimensions of psychopathology were commonly characterized by hypoconnectivity within the dorsal attention and retrosplenial-temporal networks, hyperconnectivity between the frontoparietal and ventral attention networks and between the dorsal attention network and amygdala, and hypoactivation of the caudal middle frontal gyrus. Externalizing pathology was uniquely associated with hyperconnectivity between the salience and ventral attention networks and hyperactivation of the cingulate and striatum. Internalizing pathology was uniquely characterized by hypoconnectivity between the default mode and cingulo-opercular networks. Connectivity between the cingulo-opercular network and putamen was uniquely higher for internalizing pathology and lower for thought disorder pathology.

CONCLUSIONS

These findings provide novel evidence that broad psychopathology dimensions are characterized by common and dissociable patterns, particularly for externalizing pathology, of functional connectivity and task-evoked activation throughout neurocognitive networks in preadolescence.

Multimodal Neurophysiological and Neuroimaging Evidence of Genetic Influence on Motor Control: A Case Report of Monozygotic Twins

Considering genetic influence on brain structure and function, including motor control, we report a case of right-handed monozygotic twins with atypical organization of fine motor movement control that might imply genetic influence. Structural and functional organization of the twins' motor function was assessed using transcranial magnetic stimulation (TMS), fMRI with a motor-task paradigm, and diffusion tensor imaging (DTI) tractography. TMS revealed that both twins presented the same unexpected activation and inhibition of both motor cortices during volitional unilateral fine hand movement. The right ipsilateral corticospinal tract was weaker than the left contralateral one. The motor-task fMRI identified activation in the left primary motor cortex and bilateral secondary motor areas during right-hand (dominant) movement and activation in the bilateral primary motor cortex and secondary motor areas during left-hand movement. Based on DTI tractography, both twins showed a significantly lower streamline count (number of fibers) in the right corticospinal tract compared with a control group, which was not the case for the left corticospinal tract. Neither twin reported any difficulty in conducting fine motor movements during their activities of daily living. The combination of TMS and advanced neuroimaging techniques identified an atypical motor control organization that might be influenced by genetic factors. This combination emphasizes that activation of the unilateral uncrossed pyramidal tract represents an alternative scheme to a "failure" of building a standard pattern but may not necessarily lead to disability.

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2 : M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2 : M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks

Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.

High-resolution connectomic fingerprints: Mapping neural identity and behavior

Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.

Cortical thickness as predictor of response to exercise in people with Parkinson's disease

We previously showed that dual-task cost (DTC) on gait speed in people with Parkinson's disease (PD) improved after 6 weeks of the Agility Boot Camp with Cognitive Challenge (ABC-C) exercise program. Since deficits in dual-task gait speed are associated with freezing of gait and gray matter atrophy, here we performed preplanned secondary analyses to answer two questions: (a) Do people with PD who are freezers present similar improvements compared to nonfreezers in DTC on gait speed with ABC-C? (b) Can cortical thickness at baseline predict responsiveness to the ABC-C? The DTC from 39 freezers and 43 nonfreezers who completed 6 weeks of ABC-C were analyzed. A subset of 51 participants (21 freezers and 30 nonfreezers) with high quality imaging data were used to characterize relationships between baseline cortical thickness and delta (Δ) DTC on gait speed following ABC-C. Freezers showed larger ΔDTC on gait speed than nonfreezers with ABC-C program (p < .05). Cortical thickness in visual and fronto-parietal areas predicted ΔDTC on gait speed in freezers, whereas sensorimotor-lateral thickness predicted ΔDTC on gait speed in nonfreezers (p < .05). When matched for motor severity, visual cortical thickness was a common predictor of response to exercise in all individuals, presenting the largest effect size. In conclusion, freezers improved gait automaticity even more than nonfreezers from cognitively challenging exercise. DTC on gait speed improvement was associated with larger baseline cortical thickness from different brain areas, depending on freezing status, but visual cortex thickness showed the most robust relationship with exercise-induced improvements in DTC.

Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment

Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.

Behavioral and brain signatures of substance use vulnerability in childhood

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [r_p]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (r_p: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.

Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting

Functional connectome "fingerprint" is a highly characterized brain pattern that distinguishes one individual from others. Although its existence has been demonstrated in adults, an unanswered but fundamental question is whether such individualized pattern emerges since infancy. This problem is barely investigated despites its importance in identifying the origin of the intrinsic connectome patterns that mirror distinct behavioral phenotypes. However, addressing this knowledge gap is challenging because the conventional methods are only applicable to developed brains with subtle longitudinal changes and typically fail on the dramatically developing infant brains. To tackle this challenge, we invent a novel model, namely, disentangled intensive triplet autoencoder (DI-TAE). First, we introduce the triplet autoencoder to embed the original connectivity into a latent space with higher discriminative capability among infant individuals. Then, a disentanglement strategy is proposed to separate the latent variables into identity-code, age-code, and noise-code, which not only restrains the interference from age-related developmental variance, but also captures the identity-related invariance. Next, a cross-reconstruction loss and an intensive triplet loss are designed to guarantee the effectiveness of the disentanglement and enhance the inter-subject dissimilarity for better discrimination. Finally, a variance-guided bootstrap aggregating is developed for DI-TAE to further improve the performance of identification. DI-TAE is validated on three longitudinal resting-state fMRI datasets with 394 infant scans aged 16 to 874 days. Our proposed model outperforms other state-of-the-art methods by increasing the identification rate by more than 50%, and for the first time suggests the plausible existence of brain functional connectome "fingerprint" since early infancy.

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart

Pioneering studies have shown that individual correlation measures from resting‐state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as “connectotyping” or functional connectome “fingerprinting.” We analyzed a unique dataset of 12–30 years old (N = 140) individuals who had two distinct resting state scans on the same day and again 12–18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify one's own scan was high (average AUC = 0.94), although it was significantly higher in the same day (average AUC = 0.97) than 1.5‐years later (average AUC = 0.91). Accuracy in youths (average AUC = 0.93) was not significantly different from adults (average AUC = 0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set (N = 208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of one's “fingerprint” contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of one's connectome.

Estimating the heritability of psychological measures in the Human Connectome Project dataset

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.

Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: A systematic review

This systematic review examines the genetic and epigenetic factors associated with resting-state functional connectivity (RSFC) in healthy human adult brains across the lifespan, with a focus on genes associated with Alzheimer's disease (AD). There were 58 studies included. The key findings are: (i) genetic factors have a low to moderate contribution; (ii) the apolipoprotein E ε2/3/4 polymorphism was the most studied genetic variant, with the APOE-ε4 allele most consistently associated with deficits of the default mode network, but there were insufficient studies to determine the relationships with other AD candidate risk genes; (iii) a single genome-wide association study identified several variants related to RSFC; (iv) two epigenetic independent studies showed a positive relationship between blood DNA methylation of the SLC6A4 promoter and RSFC measures. Thus, there is emerging evidence that genetic and epigenetic variation influence the brain's functional organisation and connectivity over the adult lifespan. However, more studies are required to elucidate the roles genetic and epigenetic factors play in RSFC measures across the adult lifespan.

Polygenic Risk Score-Derived Subcortical Connectivity Mediates Attention-Deficit/Hyperactivity Disorder Diagnosis

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) has substantial heritability, and a recent large-scale investigation has identified common genome-wide significant loci associated with increased risk for ADHD. Along the same lines, many studies using noninvasive neuroimaging have identified differences in brain functional connectivity in children with ADHD. We attempted to bridge these studies to identify differences in functional connectivity associated with common genetic risk for ADHD using polygenic risk score (PRS).

METHODS

We computed ADHD PRSs for all participants in our sample (N = 315, children 7-13 years of age, 196 with ADHD and 119 unaffected comparison children) using ADHD data from the Psychiatric Genomics Consortium as a discovery set. Magnetic resonance imaging was used to evaluate resting-state functional connectivity of targeted subcortical structures.

RESULTS

The functional connectivity between 2 region pairs demonstrated a significant correlation to PRS: right caudate-parietal cortex and nucleus accumbens-occipital cortex. Connectivity between these areas, in addition to being correlated with PRS, was correlated with ADHD status. The connection between the caudate and the parietal region acted as a statistical suppressor, such that when it was included in a path model, the association between PRS and ADHD status was enhanced.

CONCLUSIONS

Our results suggest that functional connectivity to certain subcortical brain regions is directly altered by genetic variants, and certain cortico-subcortical connections may modulate ADHD-related genetic effects.

Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth

BACKGROUND

Resting-state functional neuroimaging captures large-scale network organization; whether this organization is intact or disrupted during adolescent development across the psychosis spectrum is unresolved. We investigated the integrity of network organization in psychosis spectrum youth and those with first episode psychosis (FEP) from late childhood through adulthood.

METHODS

We analyzed data from Philadelphia Neurodevelopmental Cohort (PNC; typically developing = 450, psychosis spectrum = 273, 8-22 years), a longitudinal cohort of typically developing youth (LUNA; N = 208, 1-3 visits, 10-25 years), and a sample of FEP (N = 39) and matched controls (N = 34). We extracted individual time series and calculated correlations from brain regions and averaged them for 4 age groups: late childhood, early adolescence, late adolescence, adulthood. Using multiple analytic approaches, we assessed network stability across 4 age groups, compared stability between controls and psychosis spectrum youth, and compared group-level network organization of FEP to controls. We explored whether variability in cognition or clinical symptomatology was related to network organization.

RESULTS

Network organization was stable across the 4 age groups in the PNC and LUNA typically developing youth and PNC psychosis spectrum youth. Psychosis spectrum and typically developing youth had similar functional network organization during all age ranges. Network organization was intact in PNC youth who met full criteria for psychosis and in FEP. Variability in cognitive functioning or clinical symptomatology was not related to network organization in psychosis spectrum youth or FEP.

DISCUSSION

These findings provide rigorous evidence supporting intact functional network organization in psychosis risk and psychosis from late childhood through adulthood.

Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

Distinguishing individuals from brain connectivity, and studying the genetic influences on that identification across different ages, improves our basic understanding of functional brain network organization. We applied support vector machine classifiers to two datasets of twins (adult, pediatric) and two datasets of repeat-scan individuals (adult, pediatric). Classifiers were trained on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) data and used to predict individuals and co-twin pairs from independent data. The classifiers successfully identified individuals from a previous scan with 100% accuracy, even when scans were separated by months. In twin samples, classifier accuracy decreased as genetic similarity decreased. Our results demonstrate that classification is stable within individuals, similar within families, and contains similar representations of functional connections over a few decades of life. Moreover, the degree to which these patterns of connections predict siblings' data varied by genetic relatedness, suggesting that genetic influences on rs-fcMRI connectivity are established early in life.

Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins

Studying the early dynamic development of cortical folding with remarkable individual variability is critical for understanding normal early brain development and related neurodevelopmental disorders. This study focuses on the fingerprinting capability and the individual variability of cortical folding during early brain development. Specifically, we aim to explore (a) whether the developing neonatal cortical folding is unique enough to be considered as a "fingerprint" that can reliably identify an individual within a cohort of infants; (b) which cortical regions manifest more individual variability and thus contribute more for infant identification; (c) whether the infant twins can be distinguished by cortical folding. Hence, for the first time, we conduct infant individual identification and individual variability analysis involving twins based on the developing cortical folding features (mean curvature, average convexity, and sulcal depth) in 472 neonates with 1,141 longitudinal MRI scans. Experimental results show that the infant individual identification achieves 100% accuracy when using the neonatal cortical folding features to predict the identities of 1- and 2-year-olds. Besides, we observe high identification capability in the high-order association cortices (i.e., prefrontal, lateral temporal, and inferior parietal regions) and two unimodal cortices (i.e., precentral gyrus and lateral occipital cortex), which largely overlap with the regions encoding remarkable individual variability in cortical folding during the first 2 years. For twins study, we show that even for monozygotic twins with identical genes and similar developmental environments, their cortical folding features are unique enough for accurate individual identification; and in some high-order association cortices, the differences between monozygotic twin pairs are significantly lower than those between dizygotic twins. This study thus provides important insights into individual identification and individual variability based on cortical folding during infancy.

Identifying reproducible individual differences in childhood functional brain networks: An ABCD study

The 21-site Adolescent Brain Cognitive Development (ABCD) study provides an unparalleled opportunity to characterize functional brain development via resting-state functional connectivity (RSFC) and to quantify relationships between RSFC and behavior. This multi-site data set includes potentially confounding sources of variance, such as differences between data collection sites and/or scanner manufacturers, in addition to those inherent to RSFC (e.g., head motion). The ABCD project provides a framework for characterizing and reproducing RSFC and RSFC-behavior associations, while quantifying the extent to which sources of variability bias RSFC estimates. We quantified RSFC and functional network architecture in 2,188 9-10-year old children from the ABCD study, segregated into demographically-matched discovery (N = 1,166) and replication datasets (N = 1,022). We found RSFC and network architecture to be highly reproducible across children. We did not observe strong effects of site; however, scanner manufacturer effects were large, reproducible, and followed a "short-to-long" association with distance between regions. Accounting for potential confounding variables, we replicated that RSFC between several higher-order networks was related to general cognition. In sum, we provide a framework for how to characterize RSFC-behavior relationships in a rigorous and reproducible manner using the ABCD dataset and other large multi-site projects.

Machine learning in resting-state fMRI analysis

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging

The brain functional connectome forms a relatively stable and idiosyncratic backbone that can be used for identification or “fingerprinting” of individuals with a high level of accuracy. While previous cross‐sectional evidence has demonstrated increased stability and distinctiveness of the brain connectome during the course of childhood and adolescence, less is known regarding the longitudinal stability in middle and older age. Here, we collected structural and resting‐state functional MRI data at two time points separated by 2–3 years in 75 middle‐aged and older adults (age 49–80, SD = 6.91 years) which allowed us to assess the long‐term stability of the functional connectome. We show that the connectome backbone generally remains stable over a 2–3 years period in middle and older age. Independent of age, cortical volume was associated with the connectome stability of several canonical resting‐state networks, suggesting that the connectome backbone relates to structural properties of the cortex. Moreover, the individual longitudinal stability of subcortical and default mode networks was associated with individual differences in cross‐sectional and longitudinal measures of episodic memory performance, providing new evidence for the importance of these networks in maintaining mnemonic processing in middle and old age. Together, the findings encourage the use of within‐subject connectome stability analyses for understanding individual differences in brain function and cognition in aging.

Evidence for cerebello-thalamo-cortical hyperconnectivity as a heritable trait for schizophrenia

Our recent study has demonstrated that increased connectivity in the cerebello-thalamo-cortical (CTC) circuitry is a state-independent neural trait that can potentially predict the onset of psychosis. One possible cause of such "trait" abnormality would be genetic predisposition. Here, we tested this hypothesis using multi-paradigm functional magnetic resonance imaging (fMRI) data from two independent twin cohorts. In a sample of 85 monozygotic (MZ) and 52 dizygotic (DZ) healthy twin pairs acquired from the Human Connectome Project, we showed that the connectivity pattern of the identified CTC circuitry was more similar in the MZ twins (r = 0.54) compared with that in the DZ twins (r = 0.22). The structural equation modeling analysis revealed a heritability estimate of 0.52 for the CTC connectivity, suggesting a moderately strong genetic effect. Moreover, using an independent schizophrenia cotwin sample (10 discordant MZ cotwins, 30 discordant DZ cotwins, and 32 control cotwins), we observed a significant linear relationship between genetic distance to schizophrenia and the connectivity strength in the CTC circuitry (i.e., schizophrenia MZ cotwins > schizophrenia DZ cotwins > control twins, P = 0.045). The present data provide converging evidence that increased connectivity in the CTC circuitry is likely to be a heritable trait that is associated with the genetic risk of schizophrenia.

The Big Reveal: Precision Mapping Shines a Gigantic Floodlight on the Cerebellum

The cerebellum, a brain structure historically considered to be important for motor coordination, is often overlooked in terms of its role in higher-order control processing. Using resting-state functional connectivity and precision mapping, Marek et al. (2018) illuminate the cerebellum as an important source of individual variation in brain function and cognition.

Genetics and Learning: How the Genes Influence Educational Attainment

The brain is the organ of thought. The word thought is defined as the act of thinking about or considering something: an idea or opinion, or a set of ideas about a particular subject. It implicitly includes the processes of learning. Mental functions, including most if not all aspects of human behavior, such as those related to learning, arise from the activity of the brain. Neural connections that generate and support mental functions are formed throughout life, which enables lifelong learning of new concepts and skills. Both brain formation and function, as well as neural plasticity, are influenced by the activity of a variety of genes and also by epigenetic modifications, which contribute to the regulation of gene expression by adapting it to environmental conditions. In this review, aimed especially at education professionals, I discuss the genetic and epigenetic contributions to mental aspects related to learning processes in terms of heritability. I will argue that, despite most if not all aspects related to learning having a clear genetic background, innate abilities can be enhanced or diminished through educational processes. Thus, the importance of education, in the context of the inheritability of learning processes, will be discussed. The conclusion I draw is that, despite the relatively high genetic heritability shown in most brain processes associated with learning, educational practices are a key contributor to student development, allowing genetically based skills to be enhanced or alternatively diminished. Therefore one of the main goals of education in a changing an uncertain world should be to form adaptable and versatile people who can, and want to, make the most of their capabilities. Thus, knowledge derived from genetics and epigenetics, as well as from neuroscience, should be used to enhance education professionals’ understanding of the biological origins of differences in mental capabilities, thereby empowering them with the possibility to adopt more respectful and flexible educational practices to attain the goal mentioned above.

Identifying individuals using fNIRS-based cortical connectomes

The fMRI-based functional connectome was shown to be sufficiently unique to allow individual identification (fingerprinting). We aimed to test whether a fNIRS-based connectome could also be used to identify individuals. Forty-four participants performed experimental protocols that consisted of two periods of resting-state interleaved by a cognitive task period. Connectome identification was performed for all possible pairwise combinations of the three periods. The influence of hemodynamic global variation was tested using global signal regression and principal component analysis. High identification accuracies well-above chance level (2.3%) were observed overall, being particularly high (93%) to the oxyhemoglobin signal between resting conditions. Our results suggest that fNIRS is a suitable technique to assess connectome fingerprints.

The individual functional connectome is unique and stable over months to years

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.

High-accuracy individual identification using a "thin slice" of the functional connectome

Connectome fingerprinting-a method that uses many thousands of functional connections in aggregate to identify individuals-holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance-how many and which functional connections are necessary and/or sufficient for high accuracy-will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small "thin slice" of the connectome-as few as 40 out of 64,620 functional connections-was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain.

Heritability of the human connectome: A connectotyping study

Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity-a "connectotype." How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.

Editorial: New Trends in Connectomics

Connectomics is an integral part of network neuroscience. The field has undergone rapid expansion over recent years and increasingly involves a blend of experimental and computational approaches to brain connectivity. This Focus Feature on “New Trends in Connectomics” aims to track the progress of the field and its many applications across different neurobiological systems and species.

Prediction complements explanation in understanding the developing brain

A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.