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The characterizing features of autism spectrum disorder (ASD) are continuously distributed in nature; however, prior twin studies have not systematically incorporated this knowledge into estimations of concordance and discordance. We conducted a quantitative analysis of twin–twin similarity for autistic trait severity in three existing data sets involving 366 pairs of uniformly-phenotyped monozygotic (MZ) twins with and without ASD. Probandwise concordance for ASD was 96%; however, MZ trait correlations differed markedly for pairs with ASD trait burden below versus above the threshold for clinical diagnosis, with R2s on the order of 0.6 versus 0.1, respectively. Categorical MZ twin discordance for ASD diagnosis is rare and more appropriately operationalized by standardized quantification of twin–twin differences. Here we provide new evidence that although ASD itself is highly heritable, variation-in-severity of symptomatology above the diagnostic threshold is substantially influenced, in contrast, by non-shared environmental factors which may identify novel targets of early ASD amelioration.

The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cinguloopercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala–cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

The goal of precision medicine (individually tailored treatments) is not being achieved for neurobehavioural conditions such as psychiatric disorders. Traditional randomized clinical trial methods are insufficient for advancing precision medicine because of the dynamic complexity of these conditions. We present a pragmatic solution: the precision clinical trial framework, encompassing methods for individually tailored treatments. This framework includes the following: (1) treatment-targeted enrichment, which involves measuring patients’ response after a brief bout of an intervention, and then randomizing patients to a full course of treatment, using the acute response to predict long-term outcomes; (2) adaptive treatments, which involve adjusting treatment parameters during the trial to individually optimize the treatment; and (3) precise measurement, which involves measuring predictor and outcome variables with high accuracy and reliability using techniques such as ecological momentary assessment. This review summarizes precision clinical trials and provides a research agenda, including new biomarkers such as precision neuroimaging, transcranial magnetic stimulation–electroencephalogram digital phenotyping and advances in statistical and machine-learning models. Validation of these approaches — and then widespread incorporation of the precision clinical trial framework — could help achieve the vision of precision medicine for neurobehavioural conditions.

High shyness during early adolescence is associated with impaired peer relationships and risk for psychiatric disorders. Little is known, however, about the relation between shyness and trajectories of brain development over early adolescence. The current study longitudinally examined trajectories of resting-state functional connectivity (rs-fc) within four brain networks in 147 adolescents. Subjects underwent functional magnetic resonance imaging at three different time points, at average ages 10.5 (range = 7.8-13.0), 11.7 (range = 9.3-14.1), and 12.9 years (range = 10.1-15.2). Multilevel linear modeling indicated that high shyness was associated with a less steep negative slope of default mode network (DMN) rs-fc over early adolescence relative to low shyness. Less steep decreases in DMN rs-fc may relate to increased self-focus in adolescents with high shyness.

The voluntary allocation of visuospatial attention depends upon top-down influences from the frontal eye field (FEF) and intraparietal sulcus (IPS)—the core regions of the dorsal attention network (DAN)—to visual occipital cortex (VOC), and has been further associated with within-DAN influences, particularly from the FEF to IPS. However, the degree to which these influences manifest at rest and are then modulated during anticipatory visuospatial attention tasks remains poorly understood. Here, we measured both undirected and directed functional connectivity (UFC, DFC) between the FEF, IPS, and VOC at rest and during an anticipatory visuospatial attention task, using a slow event-related design. Whereas the comparison between rest and task indicated FC modulations that persisted throughout the task duration, the large number of task trials we collected further enabled us to measure shorter timescale modulations of FC across the trial. Relative to rest, task engagement induced enhancement of both top-down influences from the DAN to VOC, as well as bidirectional influences between the FEF and IPS. These results suggest that task performance induces enhanced interaction within the DAN and a greater top-down influence on VOC. While resting FC generally showed right hemisphere dominance, task-related enhancement favored the left hemisphere, effectively balancing a resting hemispheric asymmetry, particularly within the DAN. On a shorter (within-trial) timescale, VOC-to-DAN and bidirectional FEF-IPS influences were transiently elevated during the anticipatory period of the trial, evincing phasic modulations related to changing attentional demands. In contrast to these task-specific effects, resting and task-related influence patterns were highly correlated, suggesting a predisposing role for resting organization, which requires minimal tonic and phasic modulations for control of visuospatial attention.

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis.

Neuroplasticity during sensitive periods, the molecular and cellular process of enduring neural change in response to external stimuli during windows of high environmental sensitivity, is crucial for adaptation to expected environments and has implications for psychiatry. Animal research has characterized the developmental sequence and neurobiological mechanisms that govern neuroplasticity, yet gaps in our ability to measure neuroplasticity in humans limit the clinical translation of these principles. Here, we present a roadmap for the development and validation of neuroimaging and electrophysiology measures that index neuroplasticity to begin to address these gaps. We argue that validation of measures to track neuroplasticity in humans will elucidate the etiology of mental illness and inform the type and timing of mental health interventions to optimize effectiveness. We outline criteria for evaluating putative neuroimaging measures of plasticity in humans including links to neurobiological mechanisms shown to govern plasticity in animal models, developmental change that reflects heightened early life plasticity, and prediction of neural and/or behavior change. These criteria are applied to three putative measures of neuroplasticity using electroencephalography (gamma oscillations, aperiodic exponent of power/frequency) or functional magnetic resonance imaging (amplitude of low frequency fluctuations). We discuss the use of these markers in psychiatry, envision future uses for clinical and developmental translation, and suggest steps to address the limitations of the current putative neuroimaging measures of plasticity. With additional work, we expect these markers will significantly impact mental health and be used to characterize mechanisms, devise new interventions, and optimize developmental trajectories to reduce psychopathology risk.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations 1 , 2 , despite evidence for concentric functional zones 3 and maps of complex actions 4 . Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action 5 and physiological control 6 , arousal 7 , errors 8 and pain 9 . This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions 4 and connectivity to internal organs 10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate–isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement. Functional MRI studies across ages show that the classic homunculus of the motor cortex in humans is in fact discontinuous, alternating with action control-linked regions termed the somato-cognitive action network.

Pediatric anxiety and depressive disorders are common, can be highly impairing, and can persist despite the best available treatments. Here, we review research into novel treatments for childhood anxiety and depressive disorders designed to target underlying cognitive, emotional, and neural circuit mechanisms. We highlight three novel treatments lying along a continuum relating to clinical impact of the disorder and the intensity of clinical management required. We review cognitive training, which involves the lowest risk and may be applicable for problems with mild to moderate impact; psychotherapy, which includes a higher level of clinical involvement and may be sufficient for problems with moderate impact; and brain stimulation, which has the highest potential risks and is therefore most appropriate for problems with high impact. For each treatment, we review the specific underlying cognitive, emotional, and brain circuit mechanisms that are being targeted, whether treatments modify those underlying mechanisms, and efficacy in reducing symptoms. We conclude by highlighting future directions, including the importance of work that leverages developmental windows of high brain plasticity to time interventions to the specific epochs in childhood that have the largest and most enduring life-long impact.

Humans require a shared conceptualization of others’ emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5–15-year-old children ( n  = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence. Camacho et al. show that emotion concepts are represented throughout the brain, giving insight to how the brain perceives real-world emotions. These patterns are present before children enter school and become more standardized across adolescence.