Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys.

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT.

Theta-Alpha Connectivity in the Hippocampal-Entorhinal Circuit Predicts Working Memory Load.

Working memory (WM) maintenance relies on multiple brain regions and inter-regional communications. The hippocampus and entorhinal cortex (EC) are thought to support this operation. Besides, EC is the main gateway for information between the hippocampus and neocortex. However, the circuit-level mechanism of this interaction during WM maintenance remains unclear in humans. To address these questions, we recorded the intracranial electroencephalography from the hippocampus and EC while patients (N = 13, six females) performed WM tasks. We found that WM maintenance was accompanied by enhanced theta/alpha band (2-12 Hz) phase synchronization between the hippocampus to the EC. The Granger causality and phase slope index analyses consistently showed that WM maintenance was associated with theta/alpha band-coordinated unidirectional influence from the hippocampus to the EC. Besides, this unidirectional inter-regional communication increased with WM load and predicted WM load during memory maintenance. These findings demonstrate that WM maintenance in humans engages the hippocampal-entorhinal circuit, with the hippocampus influencing the EC in a load-dependent manner.

Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability.

Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.

Fluctuation in cortical excitation/inhibition modulates capability of attention across time scales ranging from hours to seconds.

Sustained attention, as the basis of general cognitive ability, naturally varies across different time scales, spanning from hours, e.g. from wakefulness to drowsiness state, to seconds, e.g. trial-by-trail fluctuation in a task session. Whether there is a unified mechanism underneath such trans-scale variability remains unclear. Here we show that fluctuation of cortical excitation/inhibition (E/I) is a strong modulator to sustained attention in humans across time scales. First, we observed the ability to attend varied across different brain states (wakefulness, postprandial somnolence, sleep deprived), as well as within any single state with larger swings. Second, regardless of the time scale involved, we found highly attentive state was always linked to more balanced cortical E/I characterized by electroencephalography (EEG) features, while deviations from the balanced state led to temporal decline in attention, suggesting the fluctuation of cortical E/I as a common mechanism underneath trans-scale attentional variability. Furthermore, we found the variations of both sustained attention and cortical E/I indices exhibited fractal structure in the temporal domain, exhibiting features of self-similarity. Taken together, these results demonstrate that sustained attention naturally varies across different time scales in a more complex way than previously appreciated, with the cortical E/I as a shared neurophysiological modulator.

Contributions of repeated learning to memory in humans: insights from single-neuron recordings in the hippocampus and amygdala.

Despite the well-established phenomenon of improved memory performance through repeated learning, studies investigating the associated neural mechanisms have yielded complex and sometimes contradictory findings, and direct evidence from human neuronal recordings has been lacking. This study employs single-neuron recordings with exceptional spatial-temporal resolution, combined with representational similarity analysis, to explore the neural dynamics within the hippocampus and amygdala during repeated learning. Our results demonstrate that in the hippocampus, repetition enhances both representational specificity and fidelity, with these features predicting learning times. Conversely, the amygdala exhibits heightened representational specificity and fidelity during initial learning but does not show improvement with repetition, suggesting functional specialization of the hippocampus and amygdala during different stages of the learning repetition. Specifically, the hippocampus appears to contribute to sustained engagement necessary for benefiting from repeated learning, while the amygdala may play a role in the representation of novel items. These findings contribute to a comprehensive understanding of the intricate interplay between these brain regions in memory processes. Significance statement  For over a century, understanding how repetition contributes to memory enhancement has captivated researchers, yet direct neuronal evidence has been lacking, with a primary focus on the hippocampus and a neglect of the neighboring amygdala. Employing advanced single-neuron recordings and analytical techniques, this study unveils a nuanced functional specialization within the amygdala-hippocampal circuit during various learning repetition. The results highlight the hippocampus's role in sustaining engagement for improved memory with repetition, contrasting with the amygdala's superior ability in representing novel items. This exploration not only deepens our comprehension of memory enhancement intricacies but also sheds light on potential interventions to optimize learning and memory processes.

Neuro-immune communication at the core of craving-associated brain structural network reconfiguration in methamphetamine users.

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347.52±99.25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0.35, P <0.01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR<0.05), with strong alterations in somatomotor regions (PFDR<0.05) and moderate alterations in high-level association networks (PFDR<0.05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

Brainnetome atlas of preadolescent children based on anatomical connectivity profiles.

During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement.

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicenter study.

OBJECTIVES: Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics model for the prediction of LNM in OPSCCs using contrast-enhanced computed tomography (CECT). METHODS: A retrospective analysis included 279 OPSCC patients from three institutions. CECT images were used for handcrafted (HCR) and DL feature extraction. Dimensionality reduction for HCR features used recursive feature elimination and least absolute shrinkage and selection operator algorithms, whereas DL feature dimensionality reduction used variance-threshold and recursive feature elimination algorithms. Radiomics signatures were constructed using support vector machine, decision tree, random forest, k-nearest neighbor, gaussian naive bayes classifiers and light gradient boosting machine. A combined model was then constructed using the screened DL, HCR, and clinical features. The area under the receiver operating characteristic curve (AUC) served to quantify the model's performance, and calibration curves were utilized to assess its calibration. RESULTS: The combined model exhibited robust performance, achieving AUC values of 0.909 (95% CI: 0.861-0.957) in the training cohort, 0.884 (95% CI: 0.800-0.968) in the internal validation cohort, and 0.865 (95% CI: 0.791-0.939) in the external validation cohort. It outperformed both the clinical model and best-performing radiomics model. Moreover, calibration was deemed satisfactory. CONCLUSIONS: The combined model based on CECT demonstrates the potential to predict LNM in OPSCCs preoperatively, offering a valuable tool for more precise and tailored treatment strategies. ADVANCES IN KNOWLEDGE: This study presents a novel combined model integrating clinical factors with deep learning radiomics, significantly enhancing preoperative LNM prediction in OPSCC.

Anterior-Posterior Hippocampal Dynamics Support Working Memory Processing.

The hippocampus is a locus of working memory (WM) with anterior and posterior subregions that differ in their transcriptional and external connectivity patterns. However, the involvement and functional connections between these subregions in WM processing are poorly understood. To address these issues, we recorded intracranial EEG from the anterior and the posterior hippocampi in humans (seven females and seven males) who maintained a set of letters in their WM. We found that WM maintenance was accompanied by elevated low-frequency activity in both the anterior and posterior hippocampus and by increased theta/alpha band (3-12 Hz) phase synchronization between anterior and posterior subregions. Cross-frequency and Granger prediction analyses consistently showed that the correct WM trials were associated with theta/alpha band-coordinated unidirectional influence from the posterior to the anterior hippocampus. In contrast, WM errors were associated with bidirectional interactions between the anterior and posterior hippocampus. These findings imply that theta/alpha band synchrony within the hippocampus may support successful WM via a posterior to anterior influence. A combination of intracranial recording and a fine-grained atlas may be of value in understanding the neural mechanisms of WM processing.SIGNIFICANCE STATEMENT Working memory (WM) is crucial to everyday functioning. The hippocampus has been proposed to be a subcortical node involved in WM processes. Previous studies have suggested that the anterior and posterior hippocampi differ in their external connectivity patterns and gene expression. However, it remains unknown whether and how human hippocampal subregions are recruited and coordinated during WM tasks. Here, by recording intracranial electroencephalography simultaneously from both hippocampal subregions, we found enhanced power in both areas and increased phase synchronization between them. Furthermore, correct WM trials were associated with a unidirectional influence from the posterior to the anterior hippocampus, whereas error trials were correlated with bidirectional interactions. These findings indicate a long-axis specialization in the human hippocampus during WM processing.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Pinpointing the precise stimulation targets for brain rehabilitation in early-stage Parkinson's disease.

BACKGROUND: Transcranial magnetic stimulation (TMS) is increasingly used as a promising non-pharmacological treatment for Parkinson's disease (PD). Scalp-to-cortex distance (SCD), as a key technical parameter of TMS, plays a critical role in determining the locations of treatment targets and corresponding dosage. Due to the discrepancies in TMS protocols, the optimal targets and head models have yet to be established in PD patients. OBJECTIVE: To investigate the SCDs of the most popular used targets in left dorsolateral prefrontal cortex (DLPFC) and quantify its impact on the TMS-induced electric fields (E-fields) in early-stage PD patients. METHODS: Structural magnetic resonance imaging scans from PD patients (n = 47) and normal controls (n = 36) were drawn from the NEUROCON and Tao Wu datasets. SCD of left DLPFC was measured by Euclidean Distance in TMS Navigation system. The intensity and focality of SCD-dependent E-fields were examined and quantified using Finite Element Method. RESULTS: Early-stage PD patients showed an increased SCDs, higher variances in the SCDs and SCD-dependent E-fields across the seven targets of left DLPFC than normal controls. The stimulation targets located on gyral crown had more focal and homogeneous E-fields. The SCD of left DLPFC had a better performance in differentiating early-stage PD patients than global cognition and other brain measures. CONCLUSION: SCD and SCD-dependent E-fields could determine the optimal TMS treatment targets and may also be used as a novel marker to differentiate early-stage PD patients. Our findings have important implications for developing optimal TMS protocols and personalized dosimetry in real-world clinical practice.

Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum.

The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.

Mapping blood traits to structural organization of the brain in rhesus monkeys.

Hematological and biochemical blood traits have been linked to brain structural characteristics in humans. However, the relationship between these two domains has not been systematically explored in nonhuman primates, which are crucial animal models for understanding the mechanisms of brain function and developing therapeutics for various disorders. Here we investigated the associations between hematological/biochemical parameters and the brain's gray matter volume and white matter integrity derived from T1-weighted and diffusion magnetic resonance imaging in 36 healthy macaques. We found that intersubject variations in basophil count and hemoglobin levels correlated with gray matter volumes in the anterior cingulum, prefrontal cortex, and putamen. Through interactions between these key elements, the blood parameters' covariation network was linked with that of the brain structures, forming overarching networks connecting blood traits with structural brain features. These networks exhibited hierarchical small-world architecture, indicating highly effective interactions between their constituent elements. In addition, different subnetworks of the brain areas or fiber tracts tended to correlate with unique groups of blood indices, revealing previously unknown brain structural organization. These results provide a quantitative characterization of the interactions between blood parameters and brain structures in macaques and may increase the understanding of the body-brain relationship and the pathogenesis of relevant disorders.

The human mediodorsal thalamus: Organization, connectivity, and function.

The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition.

Structural and functional biomarkers of the insula subregions predict sex differences in aggression subscales.

Aggression is a common and complex social behavior that is associated with violence and mental diseases. Although sex differences were observed in aggression, the neural mechanism for the effect of sex on aggression behaviors remains unclear, especially in specific subscales of aggression. In this study, we investigated the effects of sex on aggression subscales, gray matter volume (GMV), and functional connectivity (FC) of each insula subregion as well as the correlation of aggression subscales with GMV and FC. This study found that sex significantly influenced (a) physical aggression, anger, and hostility; (b) the GMV of all insula subregions; and (c) the FC of the dorsal agranular insula (dIa), dorsal dysgranular insula (dId), and ventral dysgranular and granular insula (vId_vIg). Additionally, mediation analysis revealed that the GMV of bilateral dIa mediates the association between sex and physical aggression, and left dId-left medial orbital superior frontal gyrus FC mediates the relationship between sex and anger. These findings revealed the neural mechanism underlying the sex differences in aggression subscales and the important role of the insula in aggression differences between males and females. This finding could potentially explain sexual dimorphism in neuropsychiatric disorders and improve dysregulated aggressive behavior.

Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage.

Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early-onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early-onset (EOS) and adult-onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co-varying patterns by jointly analyzing three MRI features: fractional amplitude of low-frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug-naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub-cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug-naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug-naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.

Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks.

BACKGROUND: Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia. AIMS: To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers. METHOD: We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites. RESULTS: We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19-85.74%; sensitivity, 75.31-89.29% and area under the receiver operating characteristic curve, 0.797-0.909. CONCLUSIONS: These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.

Orbitofrontal cortex volume links polygenic risk for smoking with tobacco use in healthy adolescents.

BACKGROUND: Tobacco smoking remains one of the leading causes of preventable illness and death and is heritable with complex underpinnings. Converging evidence suggests a contribution of the polygenic risk for smoking to the use of tobacco and other substances. Yet, the underlying brain mechanisms between the genetic risk and tobacco smoking remain poorly understood. METHODS: Genomic, neuroimaging, and self-report data were acquired from a large cohort of adolescents from the IMAGEN study (a European multicenter study). Polygenic risk scores (PGRS) for smoking were calculated based on a genome-wide association study meta-analysis conducted by the Tobacco and Genetics Consortium. We examined the interrelationships among the genetic risk for smoking initiation, brain structure, and the number of occasions of tobacco use. RESULTS: A higher smoking PGRS was significantly associated with both an increased number of occasions of tobacco use and smaller cortical volume of the right orbitofrontal cortex (OFC). Furthermore, reduced cortical volume within this cluster correlated with greater tobacco use. A subsequent path analysis suggested that the cortical volume within this cluster partially mediated the association between the genetic risk for smoking and the number of occasions of tobacco use. CONCLUSIONS: Our data provide the first evidence for the involvement of the OFC in the relationship between smoking PGRS and tobacco use. Future studies of the molecular mechanisms underlying tobacco smoking should consider the mediation effect of the related neural structure.

Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan.

Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18-88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.