A somato-cognitive action network alternates with effector regions in motor cortex.

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. 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 action5 and physiological control6, arousal7, errors8 and pain9. 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 actions4 and connectivity to internal organs10 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.

Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Age-related differences in resting-state functional connectivity from childhood to adolescence.

The human brain is active at rest, and spontaneous fluctuations in functional MRI BOLD signals reveal an intrinsic functional architecture. During childhood and adolescence, functional networks undergo varying patterns of maturation, and measures of functional connectivity within and between networks differ as a function of age. However, many aspects of these developmental patterns (e.g. trajectory shape and directionality) remain unresolved. In the present study, we characterised age-related differences in within- and between-network resting-state functional connectivity (rsFC) and integration (i.e. participation coefficient, PC) in a large cross-sectional sample of children and adolescents (n = 628) aged 8-21 years from the Lifespan Human Connectome Project in Development. We found evidence for both linear and non-linear differences in cortical, subcortical, and cerebellar rsFC, as well as integration, that varied by age. Additionally, we found that sex moderated the relationship between age and putamen integration where males displayed significant age-related increases in putamen PC compared with females. Taken together, these results provide evidence for complex, non-linear differences in some brain systems during development.

Thalamo-cortical and cerebello-cortical functional connectivity in development.

The thalamus is a critical relay center for neural pathways involving sensory, motor, and cognitive functions, including cortico-striato-thalamo-cortical and cortico-ponto-cerebello-thalamo-cortical loops. Despite the importance of these circuits, their development has been understudied. One way to investigate these pathways in human development in vivo is with functional connectivity MRI, yet few studies have examined thalamo-cortical and cerebello-cortical functional connectivity in development. Here, we used resting-state functional connectivity to measure functional connectivity in the thalamus and cerebellum with previously defined cortical functional networks in 2 separate data sets of children (7-12 years old) and adults (19-40 years old). In both data sets, we found stronger functional connectivity between the ventral thalamus and the somatomotor face cortical functional network in children compared with adults, extending previous cortico-striatal functional connectivity findings. In addition, there was more cortical network integration (i.e. strongest functional connectivity with multiple networks) in the thalamus in children than in adults. We found no developmental differences in cerebello-cortical functional connectivity. Together, these results suggest different maturation patterns in cortico-striato-thalamo-cortical and cortico-ponto-cerebellar-thalamo-cortical pathways.

Parallel hippocampal-parietal circuits for self- and goal-oriented processing.

The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. Using individual-specific precision functional mapping of resting-state functional MRI data, we found the anterior hippocampus (head and body) to be preferentially functionally connected to the default mode network (DMN), as expected. The hippocampal tail, however, was strongly preferentially functionally connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This anterior-posterior dichotomy of resting-state functional connectivity was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the hippocampal head and body (DMN), relatively sparing the tail (PMN). The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel but distinct circuits between the hippocampus and medial parietal cortex for self- versus goal-oriented processing.

Cingulo-opercular control network and disused motor circuits joined in standby mode.

Whole-brain resting-state functional MRI (rs-fMRI) during 2 wk of upper-limb casting revealed that disused motor regions became more strongly connected to the cingulo-opercular network (CON), an executive control network that includes regions of the dorsal anterior cingulate cortex (dACC) and insula. Disuse-driven increases in functional connectivity (FC) were specific to the CON and somatomotor networks and did not involve any other networks, such as the salience, frontoparietal, or default mode networks. Censoring and modeling analyses showed that FC increases during casting were mediated by large, spontaneous activity pulses that appeared in the disused motor regions and CON control regions. During limb constraint, disused motor circuits appear to enter a standby mode characterized by spontaneous activity pulses and strengthened connectivity to CON executive control regions.

An integrative model of the maturation of cognitive control.

Brains systems undergo unique and specific dynamic changes at the cellular, circuit, and systems level that underlie the transition to adult-level cognitive control. We integrate literature from these different levels of analyses to propose a novel model of the brain basis of the development of cognitive control. The ability to consistently exert cognitive control improves into adulthood as the flexible integration of component processes, including inhibitory control, performance monitoring, and working memory, increases. Unique maturational changes in brain structure, supported by interactions between dopaminergic and GABAergic systems, contribute to enhanced network synchronization and an improved signal-to-noise ratio. In turn, these factors facilitate the specialization and strengthening of connectivity in networks supporting the transition to adult levels of cognitive control. This model provides a novel understanding of the adolescent period as an adaptive period of heightened experience-seeking necessary for the specialization of brain systems supporting cognitive control.

Individualized Functional Subnetworks Connect Human Striatum and Frontal Cortex.

The striatum and cerebral cortex are interconnected via multiple recurrent loops that play a major role in many neuropsychiatric conditions. Primate corticostriatal connections can be precisely mapped using invasive tract-tracing. However, noninvasive human research has not mapped these connections with anatomical precision, limited in part by the practice of averaging neuroimaging data across individuals. Here we utilized highly sampled resting-state functional connectivity MRI for individual-specific precision functional mapping (PFM) of corticostriatal connections. We identified ten individual-specific subnetworks linking cortex-predominately frontal cortex-to striatum, most of which converged with nonhuman primate tract-tracing work. These included separable connections between nucleus accumbens core/shell and orbitofrontal/medial frontal gyrus; between anterior striatum and dorsomedial prefrontal cortex; between dorsal caudate and lateral prefrontal cortex; and between middle/posterior putamen and supplementary motor/primary motor cortex. Two subnetworks that did not converge with nonhuman primates were connected to cortical regions associated with human language function. Thus, precision subnetworks identify detailed, individual-specific, neurobiologically plausible corticostriatal connectivity that includes human-specific language networks.

Default-mode network streams for coupling to language and control systems.

The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.

Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry.

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 cingulo-opercular 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.

Accuracy and reliability of diffusion imaging models.

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.

Adolescent development of cortical oscillations: Power, phase, and support of cognitive maturation.

During adolescence, the integration of specialized functional brain networks related to cognitive control continues to increase. Slow frequency oscillations (4-10 Hz) have been shown to support cognitive control processes, especially within prefrontal regions. However, it is unclear how neural oscillations contribute to functional brain network development and improvements in cognitive control during adolescence. To bridge this gap, we employed magnetoencephalography (MEG) to explore changes in oscillatory power and phase coupling across cortical networks in a sample of 68 adolescents and young adults. We found a redistribution of power from lower to higher frequencies throughout adolescence, such that delta band (1-3 Hz) power decreased, whereas beta band power (14-16 and 22-26 Hz) increased. Delta band power decreased with age most strongly in association networks within the frontal lobe and operculum. Conversely, beta band power increased throughout development, most strongly in processing networks and the posterior cingulate cortex, a hub of the default mode (DM) network. In terms of phase, theta band (5-9 Hz) phase-locking robustly decreased with development, following an anterior-to-posterior gradient, with the greatest decoupling occurring between association networks. Additionally, decreased slow frequency phase-locking between frontolimbic regions was related to decreased impulsivity with age. Thus, greater decoupling of slow frequency oscillations may afford functional networks greater flexibility during the resting state to instantiate control when required.

Development of Network Topology and Functional Connectivity of the Prefrontal Cortex.

The prefrontal cortex (PFC) comprises distinct regions and networks that vary in their trajectories across development. Further understanding these diverging trajectories may elucidate the neural mechanisms by which distinct PFC regions contribute to cognitive maturity. In particular, it remains unclear whether PFC regions of distinct network affiliations differ in topology and their relationship to cognition. We examined 615 individuals (8-21 years) to characterize age-related effects in participation coefficient of 28 PFC regions of distinct networks, evaluating connectivity profiles of each region to understand patterns influencing topological maturity. Findings revealed that PFC regions of attention, frontoparietal, and default mode networks (DMN) displayed varying rates of decline in participation coefficient with age, characterized by stronger connectivity with each PFC's respective network; suggesting that PFC regions largely aid network segregation. Conversely, PFC regions of the cinguloopercular/salience network increased in participation coefficient with age, marked by stronger between-network connections, suggesting that some PFC regions feature a distinctive ability to facilitate network integration. PFC topology of the DMN, in particular, predicted improvements in global cognition, including motor speed and higher order abilities. Together, these findings elucidate systematic differences in topology across PFC regions of different network affiliation, representing important neural signatures of typical brain development.

Organization of Propagated Intrinsic Brain Activity in Individual Humans.

Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks-altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum.

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Girls' brain structural connectivity in late adolescence relates to history of depression symptoms.

BACKGROUND: Girls' depressive symptoms typically increase in adolescence, with individual differences in course and severity being key risk factors for impaired emotional functioning in young adulthood. Given the continued brain white matter (WM) maturation that occurs in adolescence, the present study tested whether structural connectivity patterns in late adolescence are associated with variation in the course of depression symptom severity throughout adolescence. METHOD: Participants were girls (N = 115) enrolled in a multiyear prospective cohort study of risk for depression. Initial depression severity (intercept) at age 10 and change in severity (linear slope) across ages 10-19 were examined in relation to WM tractography collected at age 19. Network-based statistic analyses were used to identify clusters showing variation in structural connectivity in association with depressive symptom intercept, slope, and their interaction. RESULTS: Higher initial depressive severity and steeper positive slope (separately) were associated with greater structural connectivity between temporal, subcortical socioaffective, and occipital regions. Intercept showed more connectivity associations than slope. The interaction effect indicated that higher initial symptom severity and a steeper negative slope (i.e., alleviating symptoms) were related to greater connectivity between cognitive control regions. Moderately severe symptoms that worsened over time were followed by greater connectivity between self-referential and cognitive regions (e.g., posterior cingulate and frontal gyrus). CONCLUSIONS: Higher depressive symptom severity in early adolescence and increasing symptom severity over time may forecast structural connectivity differences in late adolescence, particularly in pathways involving cognitive and emotion-processing regions. Understanding how clinical course relates to neurobiological correlates may inform new treatment approaches to adolescent depression.

The Contribution of Network Organization and Integration to the Development of Cognitive Control.

Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

MRI economics: Balancing sample size and scan duration in brain wide association studies.

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.