The Human Connectome Project: A retrospective

The Human Connectome Project of adolescent anxiety and depression dataset

This article describes primary data and resources available from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, a novel arm of the Human Connectome Project (HCP). Data were collected from 215 adolescents (14-17 years old), 152 of whom had current diagnoses of anxiety and/or depressive disorders at study intake. Data include cross-sectional structural (T1- and T2-weighted), functional (resting state and three tasks), and diffusion-weighted magnetic resonance images. Both unprocessed and HCP minimally-preprocessed imaging data are available within the data release packages. Adolescent and parent clinical interview data, as well as cognitive and neuropsychological data are also included within these packages. Release packages additionally provide data collected from self-report measures assessing key features of adolescent psychopathology, including: anxious and depressive symptom dimensions, behavioral inhibition/activation, exposure to stressful life events, and risk behaviors. Finally, the release packages include 6- and 12-month longitudinal data acquired from clinical measures. Data are publicly accessible through the National Institute of Mental Health Data Archive (ID: #2505).

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

The HEALthy Brain and Child Development Study (HBCD): NIH collaboration to understand the impacts of prenatal and early life experiences on brain development

The human brain undergoes rapid development during the first years of life. Beginning in utero, a wide array of biological, social, and environmental factors can have lasting impacts on brain structure and function. To understand how prenatal and early life experiences alter neurodevelopmental trajectories and shape health outcomes, several NIH Institutes, Centers, and Offices collaborated to support and launch the HEALthy Brain and Child Development (HBCD) Study. The HBCD Study is a multi-site prospective longitudinal cohort study, that will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Influenced by the success of the ongoing Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) and in partnership with the NIH Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, the HBCD Study aims to establish a diverse cohort of over 7000 pregnant participants to understand how early life experiences, including prenatal exposure to addictive substances and adverse social environments as well as their interactions with an individual's genes, can affect neurodevelopmental trajectories and outcomes. Knowledge gained from the HBCD Study will help identify targets for early interventions and inform policies that promote resilience and mitigate the neurodevelopmental effects of adverse childhood experiences and environments.

An Introduction to the Human Connectome Project for Early Psychosis

BACKGROUND

The time following a recent onset of psychosis is a critical period during which intervention may be maximally effective. Studying individuals in this period also offers an opportunity to investigate putative brain biomarkers of illness prior to the long-term effects of chronicity and medication. The Human Connectome Project for Early Psychosis (HCP-EP) was funded by the National Institutes of Mental Health (NIMH) as an extension of the original Human Connectome Project's approach to understanding the human brain and its structural and functional connections.

DESIGN

The HCP-EP data were collected at 3 sites in Massachusetts (Beth Israel Deaconess Medical Center, McLean Hospital, and Massachusetts General Hospital), and one site in Indiana (Indiana University). Brigham and Women's Hospital served as the data coordination center and as an imaging site.

RESULTS

The HCP-EP dataset includes high-quality clinical, cognitive, functional, neuroimaging, and blood specimen data acquired from 303 individuals between the ages of 16-35 years old with affective psychosis (n = 75), non-affective psychosis (n = 148), and healthy controls (n = 80). Participants with early psychosis were within 5 years of illness onset (mean duration = 1.9 years, standard deviation = 1.4 years). All data and novel or modified analytic tools developed as part of the study are publicly available to the research community through the NIMH Data Archive (NDA) or GitHub (https://github.com/pnlbwh).

CONCLUSIONS

This paper provides an overview of the specific HCP-EP procedures, assessments, and protocols, as well as a brief characterization of the study participants to make it easier for researchers to use this rich dataset. Although we focus here on discussing and comparing affective and non-affective psychosis groups, the HCP-EP dataset also provides sufficient information for investigators to group participants differently.

Neural underpinnings of individual differences in emotion regulation: A systematic review

This review synthesises individual differences in neural processes related to emotion regulation (ER). It comprises individual differences in self-reported and physiological regulation success, self-reported ER-related traits, and demographic variables, to assess their correlation with brain activation during ER tasks. Considering region-of-interest (ROI) and whole-brain analyses, the review incorporated data from 52 functional magnetic resonance imaging studies. Results can be summarized as follows: (1) Self-reported regulation success (assessed by emotional state ratings after regulation) and self-reported ER-related traits (assessed by questionnaires) correlated with brain activity in the lateral prefrontal cortex. (2) Amygdala activation correlated with ER-related traits only in ROI analyses, while it was associated with regulation success in whole-brain analyses. (3) For demographic and physiological measures, there was no systematic overlap in effects reported across studies. In showing that individual differences in regulation success and ER-related traits can be traced back to differences in the neural activity of brain regions associated with emotional reactivity (amygdala) and cognitive control (lateral prefrontal cortex), our findings can inform prospective personalised intervention models.

Discernible interindividual patterns of global efficiency decline during theoretical brain surgery

The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as "connectotypes". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.

Tractometry of the Human Connectome Project: resources and insights

Introduction

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.

Methods

We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.

Results

We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance

The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain's organization in a multidimensional connectivity gradient space. However, how the brain's morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a "fingerprint" of an individual's brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance.

Leveraging neural plasticity for the treatment of amblyopia

Amblyopia is a form of visual cortical impairment that arises from abnormal visual experience early in life. Most often, amblyopia is a unilateral visual impairment that can develop as a result of strabismus, anisometropia, or a combination of these conditions that result in discordant binocular experience. Characterized by reduced visual acuity and impaired binocular function, amblyopia places a substantial burden on the developing child. Although frontline treatment with glasses and patching can improve visual acuity, residual amblyopia remains for most children. Newer binocular-based therapies can elicit rapid recovery of visual acuity and may also improve stereoacuity in some children. Nevertheless, for both treatment modalities full recovery is elusive, recurrence of amblyopia is common, and improvements are negligible when treatment is administered at older ages. Insights derived from animal models about the factors that govern neural plasticity have been leveraged to develop innovative treatments for amblyopia. These novel therapies exhibit efficacy to promote recovery, and some are effective even at ages when conventional treatments fail to yield benefit. Approaches for enhancing visual system plasticity and promoting recovery from amblyopia include altering the balance between excitatory and inhibitory mechanisms, reversing the accumulation of proteins that inhibit plasticity, and harnessing the principles of metaplasticity. Although these therapies have exhibited promising results in animal models, their safety and ability to remediate amblyopia need to be evaluated in humans.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

New non-local mean methods for MRI denoising based on global self-similarity between values

Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high-resolution 3D images and valuable insights into human tissue conditions. Even at present, the refinement of denoising methods for MRI remains a crucial concern for improving the quality of the images. This study aims to improve the prefiltered rotationally invariant non-local principal component analysis (PRI-NL-PCA) algorithm. We relaxed the original restrictions using particle swarm optimization to determine optimal parameters for the PCA part of the original algorithm. In addition, we adjusted the prefiltered rotationally invariant non-local mean (PRI-NLM) part by traversing the signal intensities of voxels instead of their spatial positions to reduce duplicate calculations and expand the search volume to the whole image when estimating voxels' signal intensities. The new method demonstrated superior denoising performance compared to the original approach. Moreover, in most cases, the new algorithm ran faster. Furthermore, our proposed method can also be applied to process Gaussian noise in natural images and has the potential to enhance other NLM-based denoising algorithms.

White matter alterations in affective and non-affective early psychosis: A diffusion MRI study

BACKGROUND

The early years after the onset of psychotic disorders, known as "early psychosis" (EP) are critical to determining the path of psychosis trajectory. We used a Diffusion Magnetic Resonance Imaging (DMRI) connectometry approach to assess the microstructural changes of white matter (WM) associated with EP.

METHODS

We used the Human Connectome Project in Early Psychosis (HCP-EP) dataset to collect DMRI data from patients with EP. The imaging data were processed in the Montreal Neuroimaging Initiative space and transformed into quantitative anisotropy (QA). The QA value was translated into the WM connectivity of each tract and used in the subsequent analysis.

RESULTS

121 patients with EP (94 non-affective/27 affective) and 56 healthy controls were recruited. EP was associated with increased QA in the body and tapetum of corpus callosum (CC) and decreased QA in the bilateral cerebellum, and middle cerebellar peduncle. Compared to non-affective psychosis, affective psychosis showed increased QA in the bilateral cerebellum and vermis and decreased QA in the forceps minor, body of CC, right cingulum, and bilateral inferior fronto-occipital fasciculus. Furthermore, QA changes in several WM tracts were correlated with positive and negative symptom scale scores.

LIMITATIONS

DMRI intrinsic limitations, limited sample size, and neurobiological effects of psychotropic treatment.

CONCLUSIONS

EP is associated with alterations in WM connectivity primarily in the CC and cerebellar regions. Also, affective and non-affective psychosis have distinct alterations in WM connectivity. These results can be used for the early diagnosis and differentiation of psychotic disorders.

Resting-State fMRI: Emerging Concepts for Future Clinical Application

Resting-state functional magnetic resonance imaging (rsfMRI) has been developed as a method of investigating spontaneous neural activity. Based on its low-frequency signal synchronization, rsfMRI has made it possible to identify multiple macroscopic structures termed resting-state networks (RSNs) on a single scan of less than 10 minutes. It is easy to implement even in clinical practice, in which assigning tasks to patients can be challenging. These advantages have accelerated the adoption and growth of rsfMRI. Recently, studies on the global rsfMRI signal have attracted increasing attention. Because it primarily arises from physiological events, less attention has hitherto been paid to the global signal than to the local network (i.e., RSN) component. However, the global signal is not a mere nuisance or a subsidiary component. On the contrary, it is quantitatively the dominant component that accounts for most of the variance in the rsfMRI signal throughout the brain and provides rich information on local hemodynamics that can serve as an individual-level diagnostic biomarker. Moreover, spatiotemporal analyses of the global signal have revealed that it is closely and fundamentally associated with the organization of RSNs, thus challenging the basic assumptions made in conventional rsfMRI analyses and views on RSNs. This review introduces new concepts emerging from rsfMRI spatiotemporal analyses focusing on the global signal and discusses how they may contribute to future clinical medicine. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

Selective vulnerability of layer 5a corticostriatal neurons in Huntington's disease

The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here, we employed serial fluorescence-activated nuclear sorting (sFANS), deep molecular profiling, and single-nucleus RNA sequencing (snRNA-seq) of motor-cortex samples from thirteen predominantly early stage, clinically diagnosed HD donors and selected samples from cingulate, visual, insular, and prefrontal cortices to demonstrate loss of layer 5a pyramidal neurons in HD. Extensive mHTT CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layers 6a and 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in HD cerebral cortex.

Evaluating the Neural Underpinnings of Motivation for Walking Exercise

OBJECTIVE

Motivation is critically important for rehabilitation, exercise, and motor performance, but its neural basis is poorly understood. Recent correlational research suggests that the dorsomedial prefrontal cortex (dmPFC) may be involved in motivation for walking activity and/or descending motor output. This study experimentally evaluated brain activity changes in periods of additional motivation during walking exercise and tested how these brain activity changes relate to self-reported exercise motivation and walking speed.

METHODS

Adults without disability (N = 26; 65% women; 25 [standard deviation = 5] years old) performed a vigorous exercise experiment involving 20 trials of maximal speed overground walking. Half of the trials were randomized to include "extra-motivation" stimuli (lap timer, tracked best lap time, and verbal encouragement). Wearable near-infrared spectroscopy measured oxygenated hemoglobin responses from frontal lobe regions, including the dmPFC, primary sensorimotor, dorsolateral prefrontal, anterior prefrontal, supplementary motor, and dorsal premotor cortices.

RESULTS

Compared with standard trials, participants walked faster during extra-motivation trials (2.43 vs 2.67 m/s; P < .0001) and had higher oxygenated hemoglobin responses in all tested brain regions, including dmPFC (+842 vs +1694 μM; P < .0001). Greater dmPFC activity was correlated with more self-determined motivation for exercise between individuals (r = 0.55; P = .004) and faster walking speed between trials (r = 0.18; P = .0002). dmPFC was the only tested brain region that showed both of these associations.

CONCLUSION

Simple motivational stimuli during walking exercise seem to upregulate widespread brain regions. Results suggest that dmPFC may be a key brain region linking affective signaling to motor output.

IMPACT

These findings provide a potential biologic basis for the benefits of motivational stimuli, elicited with clinically feasible methods during walking exercise. Future clinical studies could build on this information to develop prognostic biomarkers and test novel brain stimulation targets for enhancing exercise motivation (eg, dmPFC).

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Cerebral MRI in a prospective cohort study on depression and atherosclerosis: the BiDirect sample, processing pipelines, and analysis tools

BACKGROUND

The use of cerebral magnetic resonance imaging (MRI) in observational studies has increased exponentially in recent years, making it critical to provide details about the study sample, image processing, and extracted imaging markers to validate and replicate study results. This article reviews the cerebral MRI dataset from the now-completed BiDirect cohort study, as an update and extension of the feasibility report published after the first two examination time points.

METHODS

We report the sample and flow of participants spanning four study sessions and twelve years. In addition, we provide details on the acquisition protocol; the processing pipelines, including standardization and quality control methods; and the analytical tools used and markers available.

RESULTS

All data were collected from 2010 to 2021 at a single site in Münster, Germany, starting with a population of 2,257 participants at baseline in 3 different cohorts: a population-based cohort (n = 911 at baseline, 672 with MRI data), patients diagnosed with depression (n = 999, 736 with MRI data), and patients with manifest cardiovascular disease (n = 347, 52 with MRI data). During the study period, a total of 4,315 MRI sessions were performed, and over 535 participants underwent MRI at all 4 time points.

CONCLUSIONS

Images were converted to Brain Imaging Data Structure (a standard for organizing and describing neuroimaging data) and analyzed using common tools, such as CAT12, FSL, Freesurfer, and BIANCA to extract imaging biomarkers. The BiDirect study comprises a thoroughly phenotyped study population with structural and functional MRI data.

RELEVANCE STATEMENT

The BiDirect Study includes a population-based sample and two patient-based samples whose MRI data can help answer numerous neuropsychiatric and cardiovascular research questions.

KEY POINTS

• The BiDirect study included characterized patient- and population-based cohorts with MRI data. • Data were standardized to Brain Imaging Data Structure and processed with commonly available software. • MRI data and markers are available upon request.

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

Predictable navigation through spontaneous brain states with cognitive-map-like representations

Just as navigating a physical environment, navigating through the landscapes of spontaneous brain states may also require an internal cognitive map. Contemporary computation theories propose modeling a cognitive map from a reinforcement learning perspective and argue that the map would be predictive in nature, representing each state as its upcoming states. Here, we used resting-state fMRI to test the hypothesis that the spaces of spontaneously reoccurring brain states are cognitive map-like, and may exhibit future-oriented predictivity. We identified two discrete brain states of the navigation-related brain networks during rest. By combining pattern similarity and dimensional reduction analysis, we embedded the occurrences of each brain state in a two-dimensional space. Successor representation modeling analysis recognized that these brain state occurrences exhibit place cell-like representations, akin to those observed in a physical space. Moreover, we observed predictive transitions of reoccurring brain states, which strongly covaried with individual cognitive and emotional assessments. Our findings offer a novel perspective on the cognitive significance of spontaneous brain activity and support the theory of cognitive map as a unifying framework for mental navigation.

The superior colliculus motor region does not respond to finger tapping movements in humans

Electrophysiological studies in macaques and functional neuroimaging in humans revealed a motor region in the superior colliculus (SC) for upper limb reaching movements. Connectivity studies in macaques reported direct connections between this SC motor region and cortical premotor arm, hand, and finger regions. These findings motivated us to investigate if the human SC is also involved in sequential finger tapping movements. We analyzed fMRI task data of 130 subjects executing finger tapping from the Human Connectome Project. While we found strong signals in the SC for visual cues, we found no signals related to simple finger tapping. In subsequent experimental measurements, we searched for responses in the SC corresponding to complex above simple finger tapping sequences. We observed expected signal increases in cortical motor and premotor regions for complex compared to simple finger tapping, but no signal increases in the motor region of the SC. Despite evidence for direct anatomical connections of the SC motor region and cortical premotor hand and finger areas in macaques, our results suggest that the SC is not involved in simple or complex finger tapping in humans.

Corticocortical Evoked Potentials in Eloquent Brain Tumor Surgery. A Systematic Review

Eloquent brain tumor surgery involves the delicate task of resecting tumors located in regions of the brain responsible for critical functions, such as language, motor control, and sensory perception. Preserving these functions is of paramount importance to maintain the patient's quality of life. Corticocortical evoked potentials (CCEPs) have emerged as a valuable intraoperative monitoring technique that aids in identifying and preserving eloquent cortical areas during surgery. This systematic review aimed to assess the utility of CCEPs in eloquent brain tumor surgery and determine their effectiveness in improving patient outcomes. A comprehensive literature search was conducted using electronic databases, including PubMed/Medline and Scopus. The search strategy identified 11 relevant articles for detailed analysis. The findings of the included studies consistently demonstrated the potential of CCEPs in guiding surgical decision making, minimizing the risk of postoperative neurological deficits, and mapping functional connectivity during surgery. However, further research and standardization are needed to fully establish the clinical benefits and refine the implementation of CCEPs in routine neurosurgical practice.

Discovering individual fingerprints in resting-state functional connectivity using deep neural networks

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

Tumor location and neurocognitive function-Unravelling the association and identifying relevant anatomical substrates in intra-axial brain tumors

Background

Neurocognitive function is a key outcome indicator of therapy in brain tumors. Understanding the underlying anatomical substrates involved in domain function and the pathophysiological basis of dysfunction can help ameliorate the effects of therapy and tailor directed rehabilitative strategies.

Methods

Hundred adult diffuse gliomas were co-registered onto a common demographic-specific brain template to create tumor localization maps. Voxel-based lesion symptom (VLSM) technique was used to assign an association between individual voxels and neuropsychological dysfunction in various domains (attention and executive function (A & EF), language, memory, visuospatial/constructive abilities, and visuomotor speed). The probability maps thus generated were further co-registered to cortical and subcortical atlases. A permutation-based statistical testing method was used to evaluate the statistically and clinically significant anatomical parcels associated with domain dysfunction and to create heat maps.

Results

Neurocognition was affected in a high proportion of subjects (93%), with A & EF and memory being the most affected domains. Left-sided networks were implicated in patients with A & EF, memory, and language deficits with the perisylvian white matter tracts being the most common across domains. Visuospatial dysfunction was associated with lesions involving the right perisylvian cortical regions, whereas deficits in visuomotor speed were associated with lesions involving primary visual and motor output pathways.

Conclusions

Significant baseline neurocognitive deficits are prevalent in gliomas. These are multidomain and the perisylvian network especially on the left side seems to be very important, being implicated in dysfunction of many domains.

Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics

Human brain is a complex system composed of many components that interact with each other. A well-designed computational model, usually in the format of partial differential equations (PDEs), is vital to understand the working mechanisms that can explain dynamic and self-organized behaviors. However, the model formulation and parameters are often tuned empirically based on the predefined domain-specific knowledge, which lags behind the emerging paradigm of discovering novel mechanisms from the unprecedented amount of spatiotemporal data. To address this limitation, we sought to link the power of deep neural networks and physics principles of complex systems, which allows us to design explainable deep models for uncovering the mechanistic role of how human brain (the most sophisticated complex system) maintains controllable functions while interacting with external stimulations. In the spirit of optimal control, we present a unified framework to design an explainable deep model that describes the dynamic behaviors of underlying neurobiological processes, allowing us to understand the latent control mechanism at a system level. We have uncovered the pathophysiological mechanism of Alzheimer's disease to the extent of controllability of disease progression, where the dissected system-level understanding enables higher prediction accuracy for disease progression and better explainability for disease etiology than conventional (black box) deep models.

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods. Overall, causal discovery is a promising approach for analyzing fMRI data, and multiple successful applications have indicated that it is superior to traditional fMRI functional connectivity methods, but current causal discovery methods for fMRI leave room for improvement.

Benefits of sharing neurophysiology data from the BRAIN Initiative Research Opportunities in Humans Consortium

Sharing human brain data can yield scientific benefits, but because of various disincentives, only a fraction of these data is currently shared. We profile three successful data-sharing experiences from the NIH BRAIN Initiative Research Opportunities in Humans (ROH) Consortium and demonstrate benefits to data producers and to users.

Connectome-based approaches in pediatric epilepsy surgery: "State-of-the art" and future perspectives

Modern epilepsy science has overcome the traditional interpretation of a strict region-specific origin of epilepsy, highlighting the involvement of wider patterns of altered neuronal circuits. In selected cases, surgery may constitute a valuable option to achieve both seizure freedom and neurocognitive improvement. Although epilepsy is now considered as a brain network disease, the most relevant literature concerning the "connectome-based" epilepsy surgery mainly refers to adults, with a limited number of studies dedicated to the pediatric population. In this review, the Authors summarized the main current available knowledge on the relevance of WM surgical anatomy in epilepsy surgery, the post-surgical modifications of brain structural connectivity and the related clinical impact of such modifications within the pediatric context. In the last part, possible implications and future perspectives of this approach have been discussed, especially concerning the optimization of surgical strategies and the predictive value of the epilepsy network analysis for planning tailored approaches, with the final aim of improving case selection, presurgical planning, intraoperative management, and postoperative results.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Identifying and reverting the adverse effects of white matter hyperintensities on cortical surface analyses

The Human Connectome Project (HCP)-style surface-based brain MRI analysis is a powerful technique that allows precise mapping of the cerebral cortex. However, the strength of its surface-based analysis has not yet been tested in the older population that often presents with white matter hyperintensities (WMHs) on T2-weighted (T2w) MRI (hypointensities on T1w MRI). We investigated T1-weighted (T1w) and T2w structural MRI in 43 healthy middle-aged to old participants. Juxtacortical WMHs were often misclassified by the default HCP pipeline as parts of the gray matter in T1w MRI, leading to incorrect estimation of the cortical surfaces and cortical metrics. To revert the adverse effects of juxtacortical WMHs, we incorporated the Brain Intensity AbNormality Classification Algorithm into the HCP pipeline (proposed pipeline). Blinded radiologists performed stereological quality control (QC) and found a decrease in the estimation errors in the proposed pipeline. The superior performance of the proposed pipeline was confirmed using an originally-developed automated surface QC based on a large database. Here we showed the detrimental effects of juxtacortical WMHs for estimating cortical surfaces and related metrics and proposed a possible solution for this problem. The present knowledge and methodology should help researchers identify adequate cortical surface biomarkers for aging and age-related neuropsychiatric disorders.

Selective Vulnerability of Layer 5a Corticostriatal Neurons in Huntington's Disease

The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here we employed serial fluorescence activated nuclear sorting (sFANS), deep molecular profiling, and single nucleus RNA sequencing (snRNAseq) to demonstrate that layer 5a pyramidal neurons are vulnerable in primary motor cortex and other cortical areas of HD donors. Extensive mHTT -CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layer 6a, layer 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify the vulnerable layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT -CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in the HD cerebral cortex.

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

Family income is not significantly associated with T1w/T2w ratio in the Human Connectome Project in Development

Growing evidence indicates that brain development varies as a function of family socioeconomic status (SES). Numerous studies have demonstrated that children from low-SES backgrounds have thinner cortex than children from higher-SES backgrounds. A recent study in a large developmental sample found widespread associations between lower SES and greater cortical T1w/T2w ratio-thought to be an indirect proxy for cortical myelin. We evaluated the association of family income with cortical T1w/T2w ratio as a function of age in the Human Connectome Project in Development sample of 989 youth aged 8-21 years. We observed no associations between family income and T1w/T2w ratio that were significant after corrections for multiple comparisons at the region, network, or whole-brain level. Region of practical equivalence (ROPE) analyses were also consistent with the absence of an association between family income and T1w/T2w ratio. We discuss potential methodological sources of inconsistency between this and the previous study examining the same question. While the question of whether family income may influence cortical myelin development remains, these null results may indicate that the association between SES and cortical myelin development may not be as strong as with other aspects of brain structure.

Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN

Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer's disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Applying Joint Graph Embedding to Study Alzheimer's Neurodegeneration Patterns in Volumetric Data

Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimer's Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration on a whole brain scale rather than locally may help address this. In this work, we turn to network based analyses and extend a graph embedding algorithm to study morphometric connectivity from volume-change correlations measured with structural MRI on the timescale of years. We model our data with the multiple random eigengraphs framework, as well as modify and implement a multigraph embedding algorithm proposed earlier to estimate a low dimensional embedding of the networks. Our version of the algorithm guarantees meaningful finite-sample results and estimates maximum likelihood edge probabilities from population-specific network modes and subject-specific loadings. Furthermore, we propose and implement a novel statistical testing procedure to analyze group differences after accounting for confounders and locate significant structures during AD neurodegeneration. Family-wise error rate is controlled at 5% using permutation testing on the maximum statistic. We show that results from our analysis reveal networks dominated by known structures associated to AD neurodegeneration, indicating the framework has promise for studying AD. Furthermore, we find network-structure tuples that are not found with traditional methods in the field.

A Novel Method for Multi-subject fMRI Data Analysis: Independent Component Analysis with Clustering Embedded (ICA-CE)

The analysis of multi-subject functional magnetic resonance imaging (fMRI) data and the extraction of accurate brain functional networks (FNs) are of great importance. However, traditional independent component analysis (ICA) methods perform analysis on multi-subject fMRI data under the condition of known or assumed classes of subjects, which may decrease its ability to extract accurate individual brain FNs. Although a previous method named clusterwise ICA (C-ICA) clusters subjects and obtains shared FNs in group-level for each class, its clustering performance on complex data is not ideal. To address the issues, we propose a novel method called independent component analysis with clustering embedded (ICA-CE) that can achieve both the estimation of individual FNs and the clustering of subjects in an unsupervised or semi-supervised manner. Using the simulated data with different properties, ICA-CE achieved better clustering performance than group ICA followed by K-means and C-ICA, and the mean accuracy of extracted individual FNs obtained by ICA-CE was greater than 90%. Using the task-related fMRI data from Human Connectome Project (HCP), our method also achieved higher clustering accuracy, while extracting task-related class-specific FNs. In summary, ICA-CE is effective in estimating accurate brain FNs while achieving the clustering of multiple subjects.Clinical Relevance- Our method is promising in estimating accurate brain functional networks for patients with brain disorders and outputting related class label for each subject.

Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization

Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.

Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis

The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.

QuNex-An integrative platform for reproducible neuroimaging analytics

Introduction

Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability.

Methods

To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features.

Results

The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform.

Discussion

Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

A consensus protocol for functional connectivity analysis in the rat brain

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

Psychedelic-Assisted Therapy and Psychedelic Science: A Review and Perspective on Opportunities in Neurosurgery and Neuro-Oncology

After a decades-long pause, psychedelics are again being intensely investigated for treating a wide range of neuropsychiatric ailments including depression, anxiety, addiction, post-traumatic stress disorder, anorexia, and chronic pain syndromes. The classic serotonergic psychedelics psilocybin and lysergic acid diethylamide and nonclassic psychedelics 3,4-methylenedioxymethamphetamine and ketamine are increasingly appreciated as neuroplastogens given their potential to fundamentally alter mood and behavior well beyond the time window of measurable exposure. Imaging studies with psychedelics are also helping advance our understanding of neural networks and connectomics. This resurgence in psychedelic science and psychedelic-assisted therapy has potential significance for the fields of neurosurgery and neuro-oncology and their diverse and challenging patients, many of whom continue to have mental health issues and poor quality of life despite receiving state-of-the-art care. In this study, we review recent and ongoing clinical trials, the set and setting model of psychedelic-assisted therapy, potential risks and adverse events, proposed mechanisms of action, and provide a perspective on how the safe and evidence-based use of psychedelics could potentially benefit many patients, including those with brain tumors, pain syndromes, ruminative disorders, stroke, SAH, TBI, and movement disorders. By leveraging psychedelics' neuroplastic potential to rehabilitate the mind and brain, novel treatments may be possible for many of these patient populations, in some instances working synergistically with current treatments and in some using subpsychedelic doses that do not require mind-altering effects for efficacy. This review aims to encourage broader multidisciplinary collaboration across the neurosciences to explore and help realize the transdiagnostic healing potential of psychedelics.

Identification of central amygdala and trigeminal motor nucleus connectivity in humans: An ultra-high field diffusion MRI study

The neuroanatomical circuitry of jaw muscles has been mostly explored in non-human animals. A recent rodent study revealed a novel circuit from the central amygdala (CeA) to the trigeminal motor nucleus (5M), which controls biting attacks. This circuit has yet to be delineated in humans. Ultra-high diffusion-weighted imaging data from the Human Connectome Project (HCP) allow in vivo delineation of circuits identified in other species-for example, the CeA-5M pathway-in humans. We hypothesized that the CeA-5M circuit could be resolved in humans at both 7 and 3 T. We performed probabilistic tractography between the CeA and 5M in 30 healthy young adults from the HCP database. As a negative control, we performed tractography between the basolateral amygdala (BLAT) and 5M, as CeA is the only amygdalar nucleus with extensive projections to the brainstem. Connectivity strength was operationalized as the number of streamlines between each region of interest. Connectivity strength between CeA-5M and BLAT-5M within each hemisphere was compared, and CeA-5M circuit had significantly stronger connectivity than the BLAT-5M circuit, bilaterally at both 7 T (all p < .001) and 3 T (all p < .001). This study is the first to delineate the CeA-5M circuit in humans.

Early Social Adversity, Altered Brain Functional Connectivity, and Mental Health

Early adverse environmental exposures during brain development are widespread risk factors for the onset of severe mental disorders and strong and consistent predictors of stress-related mental and physical illness and reduced life expectancy. Current evidence suggests that early negative experiences alter plasticity processes during developmentally sensitive time windows and affect the regular functional interaction of cortical and subcortical neural networks. This, in turn, may promote a maladapted development with negative consequences on the mental and physical health of exposed individuals. In this review, we discuss the role of functional magnetic resonance imaging-based functional connectivity phenotypes as potential biomarker candidates for the consequences of early environmental exposures-including but not limited to-childhood maltreatment. We take an expanded concept of developmentally relevant adverse experiences from infancy over childhood to adolescence as our starting point and focus our review of functional connectivity studies on a selected subset of functional magnetic resonance imaging-based phenotypes, including connectivity in the limbic and within the frontoparietal as well as default mode networks, for which we believe there is sufficient converging evidence for a more detailed discussion in a developmental context. Furthermore, we address specific methodological challenges and current knowledge gaps that complicate the interpretation of early stress effects on functional connectivity and deserve particular attention in future studies. Finally, we highlight the forthcoming prospects and challenges of this research area with regard to establishing functional connectivity measures as validated biomarkers for brain developmental processes and individual risk stratification and as target phenotypes for mechanism-based interventions.

First Organoid Intelligence (OI) workshop to form an OI community

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.