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Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization. •Spatial autocorrelation can dramatically inflate p-values in brain map analyses.•Null model generates surrogate brain maps matched to target spatial autocorrelation.•Spatial autocorrelation drives spurious findings in gene set enrichment analyses.•Surrogate maps can correct statistical analyses including gene set enrichment.•Python-based package implements the generative model with neuroimaging functionality.

Understanding complex systems such as the human brain requires characterization of the system's architecture across multiple levels of organization – from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brain's large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently-identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly-organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas – released as an open resource for the neuroscience community – places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease. •Large-scale functional network map of the entire human brain.•Cortical networks based on multiband fMRI, recently-identified regions.•Subcortical extension of networks covering all subcortical structures.•Multiple quality assessments demonstrate robustness of functional networks.•Network atlas released as public resource, providing framework for future studies.

Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merging of insights across different areas of biological sciences.

The psychedelic drug LSD alters thinking and perception. Users can experience hallucinations, in which they, for example, see things that are not there. Colors, sounds and objects can appear distorted, and time can seem to speed up or slow down. These changes bear some resemblance to the changes in thinking and perception that occur in certain psychiatric disorders, such as schizophrenia. Studying how LSD affects the brain could thus offer insights into the mechanisms underlying these conditions. There is also evidence that LSD itself could help to reduce the symptoms of depression and anxiety disorders. Preller et al. have now used brain imaging to explore the effects of LSD on the brains of healthy volunteers. This revealed that LSD reduced communication among brain areas involved in planning and decision-making, but it increased communication between areas involved in sensation and movement. Volunteers whose brains showed the most communication between sensory and movement areas also reported the strongest effects of LSD on their thinking and perception. Preller et al. also found that another drug called Ketanserin prevented LSD from altering how different brain regions communicate. It also prevented LSD from inducing changes in thinking and perception. Ketanserin blocks a protein called the serotonin 2A receptor, which is activated by a brain chemical called serotonin that, amongst other roles, helps to regulate mood. By mapping the location of the gene that produces the serotonin 2A receptor, Preller et al. showed that the receptor is present in brain regions that show altered communication after LSD intake, therefore pinpointing the importance of this receptor in the effects of LSD. Psychiatric disorders that produce psychotic symptoms affect vast numbers of people worldwide. Further research into how LSD affects the brain could help us to better understand how such symptoms arise, and may also lead to the development of more effective treatments for a range of mental health conditions.

Hierarchy provides a unifying principle for the macroscale organization of anatomical and functional properties across primate cortex, yet microscale bases of specialization across human cortex are poorly understood. Anatomical hierarchy is conventionally informed by invasive tract-tracing measurements, creating a need for a principled proxy measure in humans. Moreover, cortex exhibits marked interareal variation in gene expression, yet organizing principles of cortical transcription remain unclear. We hypothesized that specialization of cortical microcircuitry involves hierarchical gradients of gene expression. We found that a noninvasive neuroimaging measure—MRI-derived T1-weighted/T2-weighted (T1w/T2w) mapping—reliably indexes anatomical hierarchy, and it captures the dominant pattern of transcriptional variation across human cortex. We found hierarchical gradients in expression profiles of genes related to microcircuit function, consistent with monkey microanatomy, and implicated in neuropsychiatric disorders. Our findings identify a hierarchical axis linking cortical transcription and anatomy, along which gradients of microscale properties may contribute to the macroscale specialization of cortical function.

Psychosis spectrum disorder (PSD) affects 1% of the world population and results in a lifetime of chronic disability, causing devastating personal and economic consequences. Developing new treatments for PSD remains a challenge, particularly those that target its core cognitive deficits. A key barrier to progress is the tenuous link between the basic neurobiological understanding of PSD and its clinical phenomenology. In this perspective, we focus on a key opportunity that combines innovations in non-invasive human neuroimaging with basic insights into thalamic regulation of functional cortical connectivity. The thalamus is an evolutionary conserved region that forms forebrain-wide functional loops critical for the transmission of external inputs as well as the construction and update of internal models. We discuss our perspective across four lines of evidence: First, we articulate how PSD symptomatology may arise from a faulty network organization at the macroscopic circuit level with the thalamus playing a central coordinating role. Second, we discuss how recent animal work has mechanistically clarified the properties of thalamic circuits relevant to regulating cortical dynamics and cognitive function more generally. Third, we present human neuroimaging evidence in support of thalamic alterations in PSD, and propose that a similar “thalamocortical dysconnectivity” seen in pharmacological imaging (under ketamine, LSD and THC) in healthy individuals may link this circuit phenotype to the common set of symptoms in idiopathic and drug-induced psychosis. Lastly, we synthesize animal and human work, and lay out a translational path for biomarker and therapeutic development.

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread “global” structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary—to do or not to do global signal regression—given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a “best of both worlds” solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. •Temporal ICA separates global noise and global signal allowing each to be measured.•Temporal ICA enables new neurobiological insights into functional networks.•Temporal ICA enables generation of unbiased functional connectivity.•Temporal ICA solves the GSR/no GSR cleanup dilemma.

Psychoactive drugs can transiently perturb brain physiology while preserving brain structure. The role of physiological state in shaping neural function can therefore be investigated through neuroimaging of pharmacologically induced effects. Previously, using pharmacological neuroimaging, we found that neural and experiential effects of lysergic acid diethylamide (LSD) are attributable to agonism of the serotonin-2A receptor (Preller et al., 2018). Here, we integrate brain-wide transcriptomics with biophysically based circuit modeling to simulate acute neuromodulatory effects of LSD on human cortical large-scale spatiotemporal dynamics. Our model captures the inter-areal topography of LSD-induced changes in cortical blood oxygen level-dependent (BOLD) functional connectivity. These findings suggest that serotonin-2A-mediated modulation of pyramidal-neuronal gain is a circuit mechanism through which LSD alters cortical functional topography. Individual-subject model fitting captures patterns of individual neural differences in pharmacological response related to altered states of consciousness. This work establishes a framework for linking molecular-level manipulations to systems-level functional alterations, with implications for precision medicine.

Capitalizing on recent advances in resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) and the distinctive paradigm of rapid mood normalization following ketamine treatment, the current study investigated intrinsic brain networks in major depressive disorder (MDD) during a depressive episode and following treatment with ketamine. Medication-free patients with MDD and healthy control subjects (HC) completed baseline rs-fcMRI. MDD patients received a single infusion of ketamine and underwent repeated rs-fcMRI at 24 h posttreatment. Global brain connectivity with global signal regression (GBCr) values were computed as the average of correlations of each voxel with all other gray matter voxels in the brain. MDD group showed reduced GBCr in the prefrontal cortex (PFC) but increased GBCr in the posterior cingulate, precuneus, lingual gyrus, and cerebellum. Ketamine significantly increased GBCr in the PFC and reduced GBCr in the cerebellum. At baseline, 2174 voxels of altered GBCr were identified, but only 310 voxels significantly differed relative to controls following treatment (corrected α<0.05). Responders to ketamine showed increased GBCr in the lateral PFC, caudate, and insula. Follow-up seed-based analyses illustrated a pattern of dysconnectivity between the PFC/subcortex and the rest of the brain in MDD, which appeared to normalize postketamine. The extent of the functional dysconnectivity identified in MDD and the swift and robust normalization following treatment suggest that GBCr may serve as a treatment response biomarker for the development of rapid acting antidepressants. The data also identified unique prefrontal and striatal circuitry as a putative marker of successful treatment and a target for antidepressants' development.

Previous work has shown that temporo-parietal junction (TPJ), part of a ventral attention network for stimulus-driven reorienting, deactivates during effortful cognitive engagement, along with the default mode network (DMN). TPJ deactivation has been reported both during working memory (WM) and rapid visual search, ostensibly to prevent reorienting to irrelevant objects. We tested whether the magnitude of this deactivation during WM encoding is predictive of subsequent WM performance. Using slow event-related fMRI and a delayed WM task in which distracter stimuli were presented during the maintenance phase, we found that greater TPJ and DMN deactivation during the encoding phase predicted better WM performance. TPJ and DMN, however, also showed several functional dissociations: (1) TPJ exhibited a different task-evoked pattern than DMN, responding to distracters sharing task-relevant features, but not to other types of distracters; and (2) TPJ showed strong functional connectivity with the DMN at encoding but not during distracter presentation. These results provide further evidence for the functional importance of TPJ suppression and indicate that TPJ and DMN deactivation is especially critical during WM trace formation. In addition, the functional connectivity results suggest that TPJ, while not part of the DMN during the resting state, may flexibly “couple” with this network depending on task demands.