Microstructural and functional gradients are increasingly dissociated in transmodal cortices

Neurogenetic phenotypes of learning-dependent plasticity for improved perceptual decisions

Genetics and experience are known to mold our cognitive development. Yet, the interactions between genetics and brain mechanisms that support learning and flexible behavior in the adult human brain remain largely unknown. Here, we test the link between brain-wide gene expression and macroscopic neuroimaging phenotypes of brain plasticity that support our ability to improve perceptual decisions with training. We demonstrate that gene expression links to learning-dependent changes in spatial variations of cortical microstructure and functional connectivity in visual and fronto-parietal networks that are known to be involved in perceptual decisions. Further, we show that brain stimulation in visual cortex during training boosts learning and alters functional connections, rather than microstructure organization, within and between these networks. Our results reveal neurogenetic phenotypes of plasticity in perceptual decision networks, providing insights into the interplay of genetic expression and macroscopic mechanisms of structural and functional plasticity for learning and flexible behavior.

Anterior-posterior systematic deficits of cortical thickness in early-onset schizophrenia

Schizophrenia is a neurodevelopmental condition with alterations in both sensory and association cortical areas. These alterations have been reported to follow structural connectivity patterning, and to occur in a system-level fashion. Here we investigated whether pathological alterations of schizophrenia originate from an early disruption of cortical organization. We found a structural covariance gradient axis of cortical thickness discriminated anterior from posterior region and was compressed in early-onset schizophrenia (EOS) patients. Patients showed increased structural covariance between two ends of the anterior-posterior axis, with increased geodesic distance of covarying regions between two ends. Positive symptoms increased with the strengthening of structural covariance between two ends. Our findings revealed a contracted organizational axis in EOS patients, which was attributed to excessive distally coordinated changes between anterior and posterior cortical regions. Our study from a systematic perspective suggests disturbed maturational processes of cortical thickness in EOS, supporting the neurodevelopmental hypothesis of schizophrenia.

Atypical brain function hierarchy in autism spectrum disorder: insights from a novel analytical approach based on neuronal oscillation pattern

Hierarchy is the basic character of the human brain. Neuronal oscillation is one of the fundamental features of brain function, revealing abnormal hierarchical structures in psychiatric disorders from a system-level perspective. However, to date, no research has yet quantified the normal and abnormal brain functional hierarchy based on oscillation patterns. Therefore, this study aimed to quantify brain hierarchy based on neuronal oscillation patterns using the wide-scale information across multiple frequency bands of functional magnetic resonance imaging (fMRI) data and further investigate atypical oscillation patterns in autism spectrum disorder (ASD) at the system level. We analyzed resting-state fMRI data from the Autism Brain Imaging Data Exchange II, including 132 participants with ASD and 132 healthy controls. The energy distribution patterns (EDPs) across frequency bands were calculated for different brain networks using multivariate empirical mode decomposition and Hilbert Transform to represent oscillation patterns. The gradient analysis was applied to quantify the EDP segregation among networks, and the network median distance of gradients was compared between the two groups. The k-means clustering was applied to intuitively verify the atypical EDP in ASD. Across all participants, we observed that the EDPs of different brain regions were spatially coupled to the brain hierarchy. Compared to healthy controls, the ASD exhibited reduced segregation between unimodal and transmodal regions on both energy gradient and clustering analyses, correlating with social deficits. Our results quantitatively confirm that oscillation patterns can reflect the functional segregation among networks and provide novel evidence of the system-level imbalances in neuronal oscillations in ASD.

Molecular mechanisms explaining sex-specific functional connectivity changes in chronic insomnia disorder

BACKGROUND

This study investigates the hypothesis that chronic insomnia disorder (CID) is characterized by sex-specific changes in resting-state functional connectivity (rsFC), with certain molecular mechanisms potentially influencing CID's pathophysiology by altering rsFC in relevant networks.

METHODS

Utilizing a resting-state functional magnetic resonance imaging (fMRI) dataset of 395 participants, including 199 CID patients and 196 healthy controls, we examined sex-specific rsFC effects, particularly in the default mode network (DMN) and five insomnia-genetically vulnerable regions of interest (ROIs). By integrating gene expression data from the Allen Human Brain Atlas, we identified genes linked to these sex-specific rsFC alterations and conducted enrichment analysis to uncover underlying molecular mechanisms. Additionally, we simulated the impact of sex differences in rsFC with different sex compositions in our dataset and employed machine learning classifiers to distinguish CID from healthy controls based on sex-specific rsFC data.

RESULTS

We identified both shared and sex-specific rsFC changes in the DMN and the five genetically vulnerable ROIs, with gene expression variations associated with these sex-specific connectivity differences. Enrichment analysis highlighted genes involved in synaptic signaling, ion channels, and immune function as potential contributors to CID pathophysiology through their influence on connectivity. Furthermore, our findings demonstrate that different sex compositions significantly affect study outcomes and higher diagnostic performance in sex-specific rsFC data than combined sex.

CONCLUSIONS

This study uncovered both shared and sex-specific connectivity alterations in CID, providing molecular insights into its pathophysiology and suggesting considering sex differences in future fMRI-based diagnostic and treatment strategies.

Normative structural connectome constrains spreading transient brain activity in generalized epilepsy

BACKGROUND

Genetic generalized epilepsy is characterized by transient episodes of spontaneous abnormal neural activity in anatomically distributed brain regions that ultimately propagate to wider areas. However, the connectome-based mechanisms shaping these abnormalities remain largely unknown. We aimed to investigate how the normative structural connectome constrains abnormal brain activity spread in genetic generalized epilepsy with generalized tonic-clonic seizure (GGE-GTCS).

METHODS

Abnormal transient activity patterns between individuals with GGE-GTCS (n = 97) and healthy controls (n = 141) were estimated from the amplitude of low-frequency fluctuations measured by resting-state functional MRI. The normative structural connectome was derived from diffusion-weighted images acquired in an independent cohort of healthy adults (n = 326). Structural neighborhood analysis was applied to assess the degree of constraints between activity vulnerability and structural connectome. Dominance analysis was used to determine the potential molecular underpinnings of these constraints. Furthermore, a network-based diffusion model was utilized to simulate the spread of pathology and identify potential disease epicenters.

RESULTS

Brain activity abnormalities among patients with GGE-GTCS were primarily located in the temporal, cingulate, prefrontal, and parietal cortices. The collective abnormality of structurally connected neighbors significantly predicted regional activity abnormality, indicating that white matter network architecture constrains aberrant activity patterns. Molecular fingerprints, particularly laminar differentiation and neurotransmitter receptor profiles, constituted key predictors of these connectome-constrained activity abnormalities. Network-based diffusion modeling effectively replicated transient pathological activity spreading patterns, identifying the limbic-temporal, dorsolateral prefrontal, and occipital cortices as putative disease epicenters. These results were robust across different clinical factors and individual patients.

CONCLUSIONS

Our findings suggest that the structural connectome shapes the spatial patterning of brain activity abnormalities, advancing our understanding of the network-level mechanisms underlying vulnerability to abnormal brain activity onset and propagation in GGE-GTCS.

Exploring morphological and microstructural signatures across the Alzheimer's spectrum and risk factors

Neural alterations, including myelin degeneration and inflammation-related iron burden, may accompany early Alzheimer's disease (AD) pathophysiology. This study aims to identify multi-modal signatures associated with MRI-derived atrophy and quantitative MRI (qMRI) measures of myelin and iron in a unique dataset of 158 participants across the AD spectrum, including those without cognitive impairment, at familial risk for AD, with mild cognitive impairment, and with AD dementia. Our results revealed a brain pattern with decreased cortical thickness, indicating increased neuronal death, and compromised hippocampal integrity due to reduced myelin content. This pattern was associated with lifestyle factors such as smoking, high blood pressure, high cholesterol, and anxiety, as well as older age, AD progression, and APOE-ɛ4 carrier status. These findings underscore the value of qMRI metrics as a non-invasive tool, offering sensitivity to lifestyle-related modifiable risk factors and medical history, even in preclinical stages of AD.

White matter functional connectome gradient dysfunction in major depressive disorder

Background

Major depressive disorder (MDD) is a prevalent psychiatric disorder with disruptions in brain white matter (WM). While much research has focused on WM structure, the dysfunctional organization of WM in MDD remains poorly understood.

Methods

Using resting-state functional magnetic resonance imaging data from 48 MDD patients and 68 healthy controls (HC), we characterized the WM functional connectome gradients across participants and identified both global and regional alterations in MDD. Furthermore, we examined the relationship between gradient properties and depressive symptom severity. External validation and sensitivity analyses were finally conducted to ensure the reliability of results.

Results

The principal WM connectome gradient extended from the forceps major and superior longitudinal fasciculus to the uncinate fasciculus (UF) and anterior thalamic radiation (ATR), exhibiting a superficial-to-deep pattern in both groups. Compared to HC, MDD patients displayed a narrower gradient range and lower spatial variation, indicating a contracted WM hierarchy. At the tract-specific level, MDD patients exhibited lower gradient scores in the forceps minor, left ATR and UF, and bilateral cingulate gyrus and cingulum hippocampus, but higher gradient scores in the forceps major, bilateral inferior longitudinal fasciculus and superior longitudinal fasciculus. WM tract gradient patterns explained 37.2% of the variance in clinical severity, with the strongest contributions from the inferior fronto-occipital fasciculus, cingulum hippocampus, ATR, UF, and corticospinal tract.

Conclusions

These findings highlight altered WM functional connectome gradient in MDD and their association with clinical severity, offering novel insights into the neurobiological mechanisms of the disorder and potential biomarkers for symptom evaluation.

Multimodal gradients unify local and global cortical organization

Functional specialization of brain areas and subregions, as well as their integration into large-scale networks, are key principles in neuroscience. Consolidating both local and global perspectives on cortical organization, however, remains challenging. Here, we present an approach to integrate inter- and intra-areal similarities of microstructure, structural connectivity, and functional interactions. Using high-field in-vivo 7 tesla (7 T) Magnetic Resonance Imaging (MRI) data and a probabilistic post-mortem atlas of cortical cytoarchitecture, we derive multimodal gradients that capture cortex-wide organization. Inter-areal similarities follow a canonical sensory-fugal gradient, linking cortical integration with functional diversity across tasks. However, intra-areal heterogeneity does not follow this pattern, with greater variability in association cortices. Findings are replicated in an independent 7 T dataset and a 100-subject 3 tesla (3 T) cohort. These results highlight a robust coupling between local arealization and global cortical motifs, advancing our understanding of how specialization and integration shape human brain function.

Visual Statistical Learning Alters Low-Dimensional Cortical Architecture

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

Cortical structure in relation to empathy and psychopathy in 800 incarcerated men

Background

Reduced affective empathy is a hallmark of psychopathy, which incurs major interpersonal and societal costs. Advancing our neuroscientific understanding of this reduction and other psychopathic traits is crucial for improving their treatment.

Methods

In 804 incarcerated adult men, we administered the Perspective Taking (IRI-PT) and Empathic Concern (IRI-EC) subscales of the Interpersonal Reactivity Index, Hare Psychopathy Checklist-Revised (PCL-R; two factors), and T1-weighted MRI to quantify cortical thickness (CT) and surface area (SA). We also included the male sample of the Human Connectome Project (HCP; N = 501) to replicate patterns of macroscale structural organization.

Results

Factor 1 (Interpersonal/Affective) uniquely negatively related to IRI-EC, while Factor 2 (Lifestyle/Antisocial) uniquely negatively related to IRI-PT. Cortical structure did not relate to either IRI subscale, although there was effect-size differentiation by microstructural class and/or functional network. CT related to Factor 1 (mostly positively), SA related to both factors (only positively), and both cortical indices demonstrated out-of-sample predictive utility for Factor 1. The high-psychopathy group (N = 178) scored uniquely lower on IRI-EC while having increased SA (but not CT). Regionally, these SA increases localized primarily in the paralimbic class and somatomotor network, with meta-analytic task-based activations corroborating affective-sensory importance. High psychopathy also showed "compressed" global and/or network-level organization of both cortical indices, and this organization in the total sample replicated in HCP. All findings accounted for age, IQ, and/or total intracranial volume.

Conclusions

Psychopathy had negative relationships with affective empathy and positive relationships with paralimbic/somatomotor SA, highlighting the role of affect and sensation.

The multiscale brain structural re-organization that occurs from childhood to adolescence correlates with cortical morphology maturation and functional specialization

From childhood to adolescence, the structural organization of the human brain undergoes dynamic and regionally heterogeneous changes across multiple scales, from synapses to macroscale white matter pathways. However, during this period, the developmental process of multiscale structural architecture, its association with cortical morphological changes, and its role in the maturation of functional organization remain largely unknown. Here, using two independent multimodal imaging developmental datasets aged 6-14 years, we investigated developmental process of multiscale cortical organization by constructing an in vivo multiscale structural connectome model incorporating white matter tractography, cortico-cortical proximity, and microstructural similarity. By employing the gradient mapping method, the principal gradient derived from the multiscale structural connectome effectively recapitulated the sensory-association axis. Our findings revealed a continuous expansion of the multiscale structural gradient space during development, characterized by enhanced differentiation between primary sensory and higher-order transmodal regions along the principal gradient. This age-related differentiation paralleled regionally heterogeneous changes in cortical morphology. Furthermore, the developmental changes in coupling between multiscale structural and functional connectivity were correlated with functional specialization refinement, as evidenced by changes in the participation coefficient. Notably, the differentiation of the principal multiscale structural gradient was associated with improved cognitive abilities, such as enhanced working memory and attention performance, and potentially underpinned by synaptic and hormone-related biological processes. These findings advance our understanding of the intricate maturation process of brain structural organization and its implications for cognitive performance.

Dynamic and Static Functional Gradient in Temporal Lobe Epilepsy With Hippocampal Sclerosis Versus Healthy Controls

AIMS

The gradient captures the continuous transitions in connectivity, representing an intrinsic hierarchical architecture of the brain. Previous works hinted at the dynamics of the gradient but did not verify them. Cognitive impairment is a common comorbidity of temporal lobe epilepsy (TLE). Gradient techniques provide a framework that could promote the understanding of the neural correlations of cognitive decline.

METHODS

Thirty patients with TLE and hippocampal sclerosis and 29 matched healthy controls (HC) were investigated with verbal fluency task-based functional MRI and gradient techniques. The correlation between task-based activation/deactivation and healthy gradients, task-based gradients, and dynamic features calculated with sliding window approaches was compared between HC and TLE.

RESULTS

The allegiance in the real data of HC and TLE was more widespread compared to static null models. TLE has lower dynamic recruitment of gradient, atypical activation-gradient correlation, and contracted principal gradient. Correlation analysis proved that the reconfiguration of principal gradient did not drive the reorganization of activation. The atypical activation pattern and impaired recruitment were correlated with cognition scales in TLE.

DISCUSSION

The principal gradient is dynamic. TLE disrupted activation/deactivation patterns, the principal gradient, and the dynamics of the gradient, which were correlated with cognitive decline.

Multimodal precision MRI of the individual human brain at ultra-high fields

Multimodal neuroimaging, in particular magnetic resonance imaging (MRI), allows for non-invasive examination of human brain structure and function across multiple scales. Precision neuroimaging builds upon this foundation, enabling the mapping of brain structure, function, and connectivity patterns with high fidelity in single individuals. Highfield MRI, operating at magnetic field strengths of 7 Tesla (T) or higher, increases signal-to-noise ratio and opens up possibilities for gains spatial resolution. Here, we share a multimodal Precision Neuroimaging and Connectomics (PNI) 7 T MRI dataset. Ten healthy individuals underwent a comprehensive MRI protocol, including T1 relaxometry, magnetization transfer imaging, T2*-weighted imaging, diffusion MRI, and multi-state functional MRI paradigms, aggregated across three imaging sessions. Alongside anonymized raw MRI data, we release cortex-wide connectomes from different modalities across multiple parcellation scales, and supply "gradients" that compactly characterize spatial patterning of cortical organization. Our precision MRI dataset will advance our understanding of structure-function relationships in the individual human brain and is publicly available via the Open Science Framework.

Contractions in human cerebellar-cortical manifold structure underlie motor reinforcement learning

How the brain learns new motor commands through reinforcement involves distributed neural circuits beyond known frontal-striatal pathways, yet a comprehensive understanding of this broader neural architecture remains elusive. Here, using human functional MRI (N = 46, 27 females) and manifold learning techniques, we identified a low-dimensional neural space that captured the dynamic changes in whole-brain functional organization during a reward-based trajectory learning task. By quantifying participants' learning rates through an Actor-Critic model, we discovered that periods of accelerated learning were characterized by significant manifold contractions across multiple brain regions, including areas of limbic and hippocampal cortex, as well as the cerebellum. This contraction reflected enhanced network integration, with notably stronger connectivity between several of these regions and the sensorimotor cerebellum correlating with higher learning rates. These findings challenge the traditional view of the cerebellum as solely involved in error-based learning, supporting the emerging view that it coordinates with other brain regions during reinforcement learning.Significance Statement This study reveals how distributed brain systems, including the cerebellum and hippocampus, alter their functional connectivity to support motor learning through reinforcement. Using advanced manifold learning techniques on functional MRI data, we examined changes in regional connectivity during reward-based learning and their relationship to learning rate. For several brain regions, we found that periods of heightened learning were associated with increased cerebellar connectivity, suggesting a key role for the cerebellum in reward-based motor learning. These findings challenge the traditional view of the cerebellum as solely involved in supervised (error-based) learning and add to a growing rodent literature supporting a role for cerebellar circuits in reward-driven learning.

Detection of structural-functional coupling abnormalities using multimodal brain networks in Alzheimer's disease: A comparison of three computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the disconnection of white matter fibers and disrupted functional connectivity of gray matter; however, the pathological mechanisms linking structural and functional changes remain unclear. This study aimed to explore the interaction between the structural and functional brain network in AD using advanced structural-functional coupling (S-F coupling) models to assess whether these changes correlate with cognitive function, Aβ deposition levels, and gene expression. In this study, we utilized multimodal magnetic resonance imaging data from 41 individuals with AD, 112 individuals with mild cognitive impairment, and 102 healthy controls to explore these mechanisms. We applied different computational models to examine the changes in the S-F coupling associated with AD. Our results showed that the communication and graph harmonic models demonstrated greater heterogeneity and were more sensitive than the statistical models in detecting AD-related pathological changes. In addition, S-F coupling increases with AD progression at the global, subnetwork, and regional node levels, especially in the medial prefrontal and anterior cingulate cortices. The S-F coupling of these regions also partially mediated cognitive decline and Aβ deposition. Furthermore, gene enrichment analysis revealed that changes in S-F coupling were strongly associated with the regulation of cellular catabolic processes. This study advances our understanding of the interaction between structural and functional connectivity and highlights the importance of S-F coupling in elucidating the neural mechanisms underlying cognitive decline in AD.

Functional differentiation of the default and frontoparietal control networks predicts individual differences in creative achievement: evidence from macroscale cortical gradients

Much of the research on the neural correlates of creativity has emphasized creative cognition, and growing evidence suggests that creativity is related to functional properties of the default and frontoparietal control networks. The present work expands on this body of evidence by testing associations of creative achievement with connectivity profiles of brain networks assessed using macroscale cortical gradients. Using resting-state connectivity functional magnetic resonance imaging in 2 community samples (N's = 236 and 234), we found evidence that creative achievement is positively associated with greater functional dissimilarity between core regions of the default and frontoparietal control networks. These results suggest that creative achievement is supported by the ability of these 2 networks to carry out distinct cognitive roles. This research provides further evidence, using a cortical gradient approach, that individual differences in creative achievement can be predicted from functional properties of brain networks involved in higher-order cognition, and it aligns with past research on the functional connectivity correlates of creative task performance.

Developmental decorrelation of local cortical activity through adolescence supports high-dimensional encoding and working memory

Adolescence is a key period for the maturation of cognitive control during which cortical circuitry is refined through processes such as synaptic pruning, but how these refinements modulate local functional dynamics to support cognition remains only partially characterized. Here, we used data from a longitudinal, adolescent cohort (N = 134 individuals ages 10-31 years, N = 202 total sessions) that completed MRI scans at ultra-high field (7 Tesla). We used resting state fMRI data to compute surface-based regional homogeneity (ReHo)-a measure of time-dependent correlations in fMRI activity between a vertex and its immediate neighbors-as an index of local functional connectivity across the cortex. We found widespread decreases in ReHo, suggesting increasing heterogeneity and specialization of functional circuits through adolescence. Decreases in ReHo included a spatial component which overlapped with sensorimotor and cingulo-opercular networks, in which ReHo decreases were associated with developmental stabilization of working memory performance. We show that decreases in ReHo are associated with higher intrinsic coding dimensionality, demonstrating how functional specialization of these circuits may confer computational benefits by facilitating increased capacity for encoding information. These results suggest a remodeling of cortical activity in adolescence through which local functional circuits become increasingly specialized, higher-dimensional, and more capable of supporting adult-like cognitive functioning.

Temporal Lobe Epilepsy Perturbs the Brain-Wide Excitation-Inhibition Balance: Associations with Microcircuit Organization, Clinical Parameters, and Cognitive Dysfunction

Excitation-inhibition (E/I) imbalance is theorized as a key mechanism in the pathophysiology of epilepsy, with ample research focusing on elucidating its cellular manifestations. However, few studies investigate E/I imbalance at the macroscale, whole-brain level, and its microcircuit-level mechanisms and clinical significance remain incompletely understood. Here, the Hurst exponent, an index of the E/I ratio, is computed from resting-state fMRI time series, and microcircuit parameters are simulated using biophysical models. A broad decrease in the Hurst exponent is observed in pharmaco-resistant temporal lobe epilepsy (TLE), suggesting more excitable network dynamics. Connectome decoders point to temporolimbic and frontocentral cortices as plausible network epicenters of E/I imbalance. Furthermore, computational simulations reveal that enhancing cortical excitability in TLE reflects atypical increases in recurrent connection strength of local neuronal ensembles. Mixed cross-sectional and longitudinal analyses show stronger E/I ratio elevation in patients with longer disease duration, more frequent electroclinical seizures as well as interictal epileptic spikes, and worse cognitive functioning. Hurst exponent-informed classifiers discriminate patients from healthy controls with high accuracy (72.4% [57.5%-82.5%]). Replicated in an independent dataset, this work provides in vivo evidence of a macroscale shift in E/I balance in TLE patients and points to progressive functional imbalances that relate to cognitive decline.

The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.

Fiber length distribution characterizes the brain network maturation during early school-age

Environmental and social changes during early school age have a profound impact on brain development. However, it remains unclear how the brains of typically-developing children adjust white matter to optimize network topology during this period. This study proposes fiber length distribution as a novel nodal metric to capture the continuous maturation of brain network. We acquired dMRI data from N = 30 typically developing children in their first year of primary school and a one-year follow-up. We assessed the longitudinal changes in fiber length distribution, characterized by the median length of connected fibers for each brain region. The length median was positively correlated with degree and betweenness centrality, while negatively correlated with clustering coefficient and local efficiency. From ages 7 to 8, we observed significant decreases in length median in the temporal, superior parietal, anterior cingulate, and medial prefrontal cortices, accompanied by a reduction in long-range connections and an increase in short-range connections. Meta-analytic decoding revealed that the widespread decrease in length median occurred in regions responsible for sensory processing, whereas a more localized increase in length median was observed in regions involved in memory and cognitive control. Finally, simulation tests on healthy adults further supported that the decrease in long-range connections and increase in short-range connections contributed to enhanced network segregation and integration, respectively. Our results suggest that the dual process of short- and long-range fiber changes reflects a cost-efficient strategy for optimizing network organization during this critical developmental stage.

Aberrant functional connectome gradient and its neurotransmitter basis in Parkinson's disease

Patients with Parkinson's disease (PD) exhibit heterogenous clinical deficits not only in motor function, other deficits in both sensory and higher-order cognitive processing are also involved. Connectome studies have suggested a primary-to-transmodal gradient and a primary-to-primary gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether these gradients are altered in PD patients and how these alterations associate with neurotransmitter profiles remain unknown. By constructing functional network and calculating its gradient in 134 PD patients and 172 normal controls, we compared functional connectivity gradients between groups and performed spearman correlation to explore the association between neurotransmitter expression and functional network gradient-based alternations in PD. Decreased first gradients were detected mainly in association cortex, including frontal cortex, insula, cingulate, and parietal cortex, corresponding to the decrement of frontoparietal/ventral attention network observed in network-level analyses. Decreased second gradients were observed in primary motor and somatosensory cortex, meeting the decrement of somatomotor network at the network level. Besides, network-level comparisons revealed the increment of visual network in the first gradient and increment of ventral attention network in the second gradient. Transcription-neuroimaging association analyses showed that changes of the first gradient were mainly negatively correlated with nondopaminergic system, while alterations of the second gradient were positively correlated with both dopaminergic and nondopaminergic systems. These results highlight the connectome gradient dysfunction in PD and its linkage with neurotransmitter expression profiles, providing insight into the molecular mechanisms for functional alterations underlying PD.

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

Neuronal communication relies on the anatomy of the brain, yet it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints on effective connectivity. Here, we assess a hierarchical empirical Bayes model that builds on a well-established dynamic causal model by integrating structural connectivity into resting-state effective connectivity via priors. In silico analyses show that the model successfully recovers ground-truth effective connectivity and compares favorably with a prominent alternative. Analyses of empirical data reveal that a positive, monotonic relationship between structural connectivity and the prior probability of group-level effective connectivity generalizes across sessions and samples. Finally, attesting to the model's biological plausibility, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal-transmodal hierarchy. These findings underscore the value of integrating structural and effective connectivity to enhance the understanding of functional integration in health and disease.

Cortical reorganization in neuropathic pain due to peripheral nerve degeneration: altered cortical surface morphometry and hierarchical topography

ABSTRACT

Degeneration of peripheral nerves causes neuropathic pain. Previous studies have documented structural and functional brain alterations in peripheral neuropathy, which may be attributed to maladaptive plasticity following chronic neuropathic pain. Nevertheless, the effects of peripheral neuropathic pain on the macroscale organization of the cerebral cortex have not been explored. This study investigated altered surface morphology and topographic hierarchy of the cerebral cortex in patients with neuropathic pain due to peripheral neuropathy. T1-weighted magnetic resonance imaging data were acquired from 52 patients with peripheral neuropathic pain and 50 age- and sex-matched healthy controls. Cortical morphometric features including thickness and gyrification index were obtained using surface-based morphometry. A topographic gradient encoding interregional similarity in cortical thickness was extracted using a machine-learning technique named diffusion map embedding. Compared with controls, patients with neuropathic pain exhibited cortical thinning in the frontal and sensorimotor cortices, with the severity increasing with greater neuropathic pain. The patients also showed decreased gyrification in the insula, with a greater reduction in gyrification linked to more severe skin nerve degeneration. Moreover, the patients exhibited altered topographic organization of the cerebral cortex, where the direction of the topographic gradient deviated from the occipital-to-frontal axis observed in the controls in this study and reported in the literature. Our findings provided a novel perspective for macroscale cortical structural reorganization after neuropathic pain, showing thinning and gyral flattening in pain-related areas and deviation from the normal topographic axis of the cerebral cortex.

From histology to macroscale function in the human amygdala

The amygdala is a subcortical region in the mesiotemporal lobe that plays a key role in emotional and sensory functions. Conventional neuroimaging experiments treat this structure as a single, uniform entity, but there is ample histological evidence for subregional heterogeneity in microstructure and function. The current study characterized subregional structure-function coupling in the human amygdala, integrating post-mortem histology and in vivo MRI at ultra-high fields. Core to our work was a novel neuroinformatics approach that leveraged multiscale texture analysis as well as non-linear dimensionality reduction techniques to identify salient dimensions of microstructural variation in a 3D post-mortem histological reconstruction of the human amygdala. We observed two axes of subregional variation in this region, describing inferior-superior as well as mediolateral trends in microstructural differentiation that in part recapitulated established atlases of amygdala subnuclei. Translating our approach to in vivo MRI data acquired at 7 Tesla, we could demonstrate the generalizability of these spatial trends across 10 healthy adults. We then cross-referenced microstructural axes with functional blood-oxygen-level dependent (BOLD) signal analysis obtained during task-free conditions, and revealed a close association of structural axes with macroscale functional network embedding, notably the temporo-limbic, default mode, and sensory-motor networks. Our novel multiscale approach consolidates descriptions of amygdala anatomy and function obtained from histological and in vivo imaging techniques.

Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout

Genetic information is involved in the gradual emergence of cortical areas since the neural tube begins to form, shaping the heterogeneous functions of neural circuits in the human brain. Informed by invasive tract-tracing measurements, the cortex exhibits marked interareal variation in connectivity profiles, revealing the heterogeneity across cortical areas. However, it remains unclear about the organizing principles possibly shared by genetics and cortical wiring to manifest the spatial heterogeneity across the cortex. Instead of considering a complex one-to-one mapping between genetic coding and interareal connectivity, we hypothesized the existence of a more efficient way that the organizing principles are embedded in genetic profiles to underpin the cortical wiring space. Leveraging vertex-wise tractography in diffusion-weighted MRI, we derived the global connectopies (GCs) in both female and male to reliably index the organizing principles of interareal connectivity variation in a low-dimensional space, which captured three dominant topographic patterns along the dorsoventral, rostrocaudal, and mediolateral axes of the cortex. More importantly, we demonstrated that the GCs converge with the gradients of a vertex-by-vertex genetic correlation matrix on the phenotype of cortical morphology and the cortex-wide spatiomolecular gradients. By diving into the genetic profiles, we found that the critical role of genes scaffolding the GCs was related to brain morphogenesis and enriched in radial glial cells before birth and excitatory neurons after birth. Taken together, our findings demonstrated the existence of a genetically determined space that encodes the interareal connectivity variation, which may give new insights into the links between cortical connections and arealization.

Functional gradient of the fusiform gyrus and its underlying molecular basis

Evidence has evinced the functional complexity, anatomical heterogeneity, connectivity diversity, and clinical relevance of the fusiform gyrus. We aimed to investigate the hierarchical organization of the fusiform gyrus and its underlying molecular basis. Resting-state functional MRI data of 793 healthy subjects were collected from a discovery dataset and two independent cross-scanner, cross-race validation datasets. Functional gradients of the fusiform gyrus were calculated based on the voxel-wise fusiform gyrus-to-cerebrum functional connectivity to reflect its functional organization. Transcription-neuroimaging spatial correlation analysis was performed to determine genes with expression levels tracking the fusiform gyrus functional gradient. The dominant functional gradient that explained the greatest connectivity variance showed an anterior-posterior axis across the fusiform gyrus. More important, there was a strong association between the fusiform gyrus-dominant gradient and gene expression profiles, with two gene sets contributing significantly to this association yet differing in their specific expression and functional annotation. In addition, the fusiform gyrus-dominant gradient was linked closely to intrinsic geometry, slightly to cortical morphology, and gradually to behavioral domains from high-level cognitive processes to low-level sensory functions. Our findings add to the extant knowledge regarding the topographic organization of the fusiform gyrus by informing a novel conceptualization of how functional heterogeneity and multiplicity co-occur within the fusiform gyrus.

A multimodal characterization of low-dimensional thalamocortical structural connectivity patterns

The human thalamus is a heterogeneous subcortical structure coordinating whole-brain activity. Investigations of its internal organization reveal differentiable subnuclei, however, a consensus on subnuclei boundaries remains absent. Recent work suggests that thalamic organization additionally reflects continuous axes transcending nuclear boundaries. Here, we study how low-dimensional axes of thalamocortical structural connectivity relate to intrathalamic microstructural features, functional connectivity, and structural covariance. Using diffusion MRI, we compute a thalamocortical structural connectome and derive two main axes of thalamic organization. The principal axis, extending from medial to lateral, relates to intrathalamic myelin, and functional connectivity organization. The secondary axis corresponds to the core-matrix cell distribution. Lastly, exploring multimodal associations globally, we observe the principal axis consistently differentiating limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varies. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.

Cognition-related connectome gradient dysfunctions of thalamus and basal ganglia in drug-naïve first-episode major depressive disorder

BACKGROUND

Subcortical functional abnormalities are believed to contribute to clinical symptoms and cognitive impairments in major depressive disorder (MDD). By introducing functional gradient mapping, the present study evaluated subcortical gradients in MDD patients and their association with cognitive features.

METHODS

Organization patterns and between-group differences in the principal subcortical gradient were investigated in 145 never-treated first-episode MDD patients and 145 healthy controls (HCs) across limbic, thalamic, and basal ganglia (BG) systems and their structural and functional subregions. We also assessed the associations between significant gradient alterations and clinical characteristics and neuropsychological functioning.

RESULTS

Overall, MDD patients showed a relatively compressed and disturbed gradient organization than HCs, with limbic and BG regions located at the two extreme ends of the principal gradient. Specifically, MDD patients had lower principal gradient values in thalamus and limbic system but higher values in BG than HCs. These gradient alterations, associated with intrinsic Euclidian distance and functional connectivity patterns, manifested as spatial rearrangements of gradient values within each respective subregion. Lower gradient values in thalamic subregion projecting to default mode network were associated with higher principal gradient values in BG subregion projecting to ventral attention network, and these gradient alterations were correlated with poorer episodic memory performance in MDD patients.

LIMITATIONS

The specific neuropathological mechanisms driving the gradient alterations still require further investigation.

CONCLUSIONS

Opposing gradient alterations in the thalamic and BG regions synergistically impact episodic memory performance in MDD, revealing an internally differentiated and cognition related pattern of subcortical gradient dysfunction in MDD.

Individual Variability in the Structural Connectivity Architecture of the Human Brain

The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the heteromodal association cortex but less variable in the unimodal cortices. Structural connectivity (SC) is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in SC (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1,724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the SC exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain SC and link to the prediction of individual cognitive function in adult subjects.

Abnormal alterations in structure-function coupling at the modular level in patients with postherpetic neuralgia

To investigate the presence of modular loss of coupling and abnormal alterations in functional and structural networks in the brain networks of patients with postherpetic neuralgia (PHN). We collected resting-state functional magnetic resonance imaging data and diffusion tensor imaging data from 82 healthy controls (HCs) and 71 PHN patients, generated structural connectivity (SC) and functional connectivity (FC) networks, and assessed the corresponding clinical information assessment. Based on AAL(90) mapping, the brain network was divided into 9 modules, and the structural-functional connectivity (SC-FC) coupling was compared at the whole-brain level and within the modules, as well as alterations in the topological properties of the brain network in the patient group. Finally, correlation analyses were performed using the following clinical scales: Visual Analogue Scale (VAS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD). Compared with HCs, patients with PHN had reduced global efficiency (Eg) and local efficiency (Eloc) of structural and functional networks. The FC in the PHN group presented abnormal node clustering coefficients (NCp), local node efficiencies (NLe), and node efficiencies (Ne), and the SC presented abnormal node degrees (Dc), NCp, NLe, characteristic path lengths (NLp), and Ne. In addition, SC-FC coupling was reduced in the patient default network (DMN), salient network (SN), and visual network (VIS). Moreover, the degree of impairment of graph theory indicators was significantly positively correlated with scales such as VAS scores, and the coupling of modules was significantly negatively correlated with the early course of the patient's disease. Large-scale impaired topological properties of the FC and SC networks were observed in patients with PHN, and SC-FC decoupling was detected in these modules of the DMN, SN, and VIS. These aberrant alterations may have led to over-transmission of pain information or central sensitization of pain.

A simulated annealing algorithm for randomizing weighted networks

Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.

Structural MRI of brain similarity networks

Recent advances in structural MRI analytics now allow the network organization of individual brains to be comprehensively mapped through the use of the biologically principled metric of anatomical similarity. In this Review, we offer an overview of the measurement and meaning of structural MRI similarity, especially in relation to two key assumptions that often underlie its interpretation: (i) that MRI similarity can be representative of architectonic similarity between cortical areas and (ii) that similar areas are more likely to be axonally connected, as predicted by the homophily principle. We first introduce the historical roots and technical foundations of MRI similarity analysis and compare it with the distinct MRI techniques of structural covariance and tractography analysis. We contextualize this empirical work with two generative models of homophilic networks: an economic model of cost-constrained connectional homophily and a heterochronic model of ontogenetically phased cortical maturation. We then review (i) studies of the genetic and transcriptional architecture of MRI similarity in population-averaged and disorder-specific contexts and (ii) developmental studies of normative cohorts and clinical studies of neurodevelopmental and neurodegenerative disorders. Finally, we prioritize knowledge gaps that must be addressed to consolidate structural MRI similarity as an accessible, valid marker of the architecture and connectivity of an individual brain network.

White Matter-Gray Matter Correlation Analysis Based on White Matter Functional Gradient

BACKGROUND

The spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals of the brain's gray matter (GM) have been interpreted as representations of neural activity variations. In previous research, white matter (WM) signals, often considered noise, have also been demonstrated to reflect characteristics of functional activity and interactions among different brain regions. Recently, functional gradients have gained significant attention due to their success in characterizing the functional organization of the whole brain. However, previous studies on brain functional gradients have predominantly focused on GM, neglecting valuable functional information within WM.

METHODS

In this paper, we have elucidated the symmetrical nature of the functional hierarchy in the left and right brain hemispheres in healthy individuals, utilizing the principal functional gradient of the whole-brain WM while also accounting for gender differences.

RESULTS

Interestingly, both males and females exhibit a similar degree of asymmetry in their brain regions, albeit with distinct regional variations. Additionally, we have thoroughly examined and analyzed the distribution of functional gradient values in the spatial structure of the corpus callosum (CC) independently, revealing that a simple one-to-one correspondence between structure and function is absent. This phenomenon may be associated with the intricacy of their internal structural connectivity.

CONCLUSIONS

We suggest that the functional gradients within the WM regions offer a fresh perspective for investigating the structural and functional characteristics of WM and may provide insights into the regulation of neural activity between GM and WM.

Functional Connectome Hierarchy in Schizotypy and Its Associations With Expression of Schizophrenia-Related Genes

BACKGROUND AND HYPOTHESIS

Schizotypy has been conceptualized as a continuum of symptoms with marked genetic, neurobiological, and sensory-cognitive overlaps to schizophrenia. Hierarchical organization represents a general organizing principle for both the cortical connectome supporting sensation-to-cognition continuum and gene expression variability across the cortex. However, a mapping of connectome hierarchy to schizotypy remains to be established. Importantly, the underlying changes of the cortical connectome hierarchy that mechanistically link gene expressions to schizotypy are unclear.

STUDY DESIGN

The present study applied novel connectome gradient on resting-state fMRI data from 1013 healthy young adults to investigate schizotypy-associated sensorimotor-to-transmodal connectome hierarchy and assessed its similarity with the connectome hierarchy of schizophrenia. Furthermore, normative and differential postmortem gene expression data were utilized to examine transcriptional profiles linked to schizotypy-associated connectome hierarchy.

STUDY RESULTS

We found that schizotypy was associated with a compressed functional connectome hierarchy. Moreover, the pattern of schizotypy-related hierarchy exhibited a positive correlation with the connectome hierarchy observed in schizophrenia. This pattern was closely colocated with the expression of schizophrenia-related genes, with the correlated genes being enriched in transsynaptic, receptor signaling and calcium ion binding.

CONCLUSIONS

The compressed connectome hierarchy suggests diminished functional system differentiation, providing a novel and holistic system-level basis for various sensory-cognition deficits in schizotypy. Importantly, its linkage with schizophrenia-altered hierarchy and schizophrenia-related gene expression yields new insights into the neurobiological continuum of psychosis. It also provides mechanistic insight into how gene variation may drive alterations in functional hierarchy, mediating biological vulnerability of schizotypy to schizophrenia.

Brain maps of general cognitive function and spatial correlations with neurobiological cortical profiles

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|β| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing - and derive new - cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Multimodal neural correlates of childhood psychopathology

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples and robust to variations in analytical parameters. Although model parameters yielded statistically significant brain-behavior associations in unseen data, generalizability of the model was rather limited for all three latent components (r change from within- to out-of-sample statistics: LC1within = 0.36, LC1out = 0.03; LC2within = 0.34, LC2out = 0.05; LC3within = 0.35, LC3out = 0.07). Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

Disruption of network hierarchy pattern in bulimia nervosa reveals brain information integration disorder

The human brain works as a hierarchical organization that is a continuous axis spanning sensorimotor cortex to transmodal cortex (referring to cortex that integrates multimodal sensory information and participates in complex cognitive functions). Previous studies have demonstrated abnormalities in several specific networks that may account for their multiple behavioral deficits in patients with bulimia nervosa (BN), but whether and how the network hierarchical organization changes in BN remain unknown. This study aimed to investigate alterations of the hierarchy organization in BN network and their clinical relevance. Connectome gradient analyses were applied to depict the network hierarchy patterns of fifty-nine patients with BN and thirty-nine healthy controls (HCs). Then, we evaluated the network- and voxel-level gradient alterations of BN by comparing gradient values in each network and each voxel between patients with BN and HCs. Finally, the association between altered gradient values and clinical variables was explored. In the principal gradient, patients with BN exhibited reduced gradient values in dorsal attention network and increased gradient values in subcortical regions compared to HCs. In the secondary gradient, patients with BN showed decreased gradient values in ventral attention network and increased gradient values in limbic network. Regionally, the areas with altered principal or secondary gradient values in BN group were mainly located in transmodal networks, i.e., the default-mode and frontoparietal network. In BN group, the principal gradient values of right inferior frontal gyrus were negatively associated with external eating behavior. This study revealed the disordered network hierarchy patterns in patients with BN, which suggested a disturbance of brain information integration from attention network and subcortical regions to transmodal networks in these patients. These findings may provide insight into the neurobiological underpinnings of BN.

A network control theory pipeline for studying the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Integrating brainstem and cortical functional architectures

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.

A function-based mapping of sensory integration along the cortical hierarchy

Sensory information mainly travels along a hierarchy spanning unimodal to transmodal regions, forming multisensory integrative representations crucial for higher-order cognitive functions. Here, we develop an fMRI based two-dimensional framework to characterize sensory integration based on the anchoring role of the primary cortex in the organization of sensory processing. Sensory magnitude captures the percentage of variance explained by three primary sensory signals and decreases as the hierarchy ascends, exhibiting strong similarity to the known hierarchy and high stability across different conditions. Sensory angle converts associations with three primary sensory signals to an angle representing the proportional contributions of different sensory modalities. This dimension identifies differences between brain states and emphasizes how sensory integration changes flexibly in response to varying cognitive demands. Furthermore, meta-analytic functional decoding with our model highlights the close relationship between cognitive functions and sensory integration, showing its potential for future research of human cognition through sensory information processing.

Microstructural asymmetry in the human cortex

The human cerebral cortex shows hemispheric asymmetry, yet the microstructural basis of this asymmetry remains incompletely understood. Here, we probe layer-specific microstructural asymmetry using one post-mortem male brain. Overall, anterior and posterior regions show leftward and rightward asymmetry respectively, but this pattern varies across cortical layers. A similar anterior-posterior pattern is observed using in vivo Human Connectome Project (N = 1101) T1w/T2w microstructural data, with average cortical asymmetry showing the strongest similarity with post-mortem-based asymmetry of layer III. Moreover, microstructural asymmetry is found to be heritable, varies as a function of age and sex, and corresponds to intrinsic functional asymmetry. We also observe a differential association of language and markers of mental health with microstructural asymmetry patterns at the individual level, illustrating a functional divergence between inferior-superior and anterior-posterior microstructural axes, possibly anchored in development. Last, we could show concordant evidence with alternative in vivo microstructural measures: magnetization transfer (N = 286) and quantitative T1 (N = 50). Together, our study highlights microstructural asymmetry in the human cortex and its functional and behavioral relevance.

Macro-scale patterns in functional connectivity associated with ongoing thought patterns and dispositional traits

Complex macro-scale patterns of brain activity that emerge during periods of wakeful rest provide insight into the organisation of neural function, how these differentiate individuals based on their traits, and the neural basis of different types of self-generated thoughts. Although brain activity during wakeful rest is valuable for understanding important features of human cognition, its unconstrained nature makes it difficult to disentangle neural features related to personality traits from those related to the thoughts occurring at rest. Our study builds on recent perspectives from work on ongoing conscious thought that highlight the interactions between three brain networks - ventral and dorsal attention networks, as well as the default mode network. We combined measures of personality with state-of-the-art indices of ongoing thoughts at rest and brain imaging analysis and explored whether this 'tri-partite' view can provide a framework within which to understand the contribution of states and traits to observed patterns of neural activity at rest. To capture macro-scale relationships between different brain systems, we calculated cortical gradients to describe brain organisation in a low-dimensional space. Our analysis established that for more introverted individuals, regions of the ventral attention network were functionally more aligned to regions of the somatomotor system and the default mode network. At the same time, a pattern of detailed self-generated thought was associated with a decoupling of regions of dorsal attention from regions in the default mode network. Our study, therefore, establishes that interactions between attention systems and the default mode network are important influences on ongoing thought at rest and highlights the value of integrating contemporary perspectives on conscious experience when understanding patterns of brain activity at rest.

Bias-accounting meta-analyses overcome cerebellar neglect to refine the cerebellar behavioral topography

The cerebellum plays important roles in motor, cognitive, and emotional behaviors. Previous cerebellar coordinate-based meta-analyses and mappings have attributed different behaviors to cerebellar subareas, but an accurate behavioral topography is lacking. Here, we show overrepresentation of superior activation foci, which may be exacerbated by historical cerebellar neglect. Unequal foci distributions render the null hypothesis of standard activation likelihood estimation unsuitable. Our new method, cerebellum-specific activation-likelihood estimation (C-SALE), finds behavioral convergence beyond baseline activation rates. It does this by testing experimental foci versus null models sampled from a data-driven, biased probability distribution of finding foci at any cerebellar location. Cerebellar mappings were made across five BrainMap task domains and thirty-five subdomains, illustrating improved specificity of the new method. Twelve of forty (sub)domains reached convergence in specific cerebellar subregions, supporting dual motor representations and placing cognition in posterior-lateral regions. Repeated subsampling revealed that whereas action, language and working memory were relatively stable, other behaviors produced unstable meta-analytic maps. Lastly, meta-analytic connectivity modeling in the same debiased framework was used to reveal coactivation networks of cerebellar behavioral clusters. In sum, we created a new method for cerebellar meta-analysis that accounts for data biases and can be flexibly adapted to any part of the brain. Our findings provide a refined understanding of cerebellar involvement in human behaviors, highlighting regions for future investigation in both basic and clinical applications.

Differential reorganization of episodic and semantic memory systems in epilepsy-related mesiotemporal pathology

Declarative memory encompasses episodic and semantic divisions. Episodic memory captures singular events with specific spatiotemporal relationships, whereas semantic memory houses context-independent knowledge. Behavioural and functional neuroimaging studies have revealed common and distinct neural substrates of both memory systems, implicating mesiotemporal lobe (MTL) regions such as the hippocampus and distributed neocortices. Here, we explored declarative memory system reorganization in patients with unilateral temporal lobe epilepsy (TLE) as a human disease model to test the impact of variable degrees of MTL pathology on memory function. Our cohort included 31 patients with TLE and 60 age- and sex-matched healthy controls, and all participants underwent episodic and semantic retrieval tasks during a multimodal MRI session. The functional MRI tasks were closely matched in terms of stimuli and trial design. Capitalizing on non-linear connectome gradient-mapping techniques, we derived task-based functional topographies during episodic and semantic memory states, in both the MTL and neocortical networks. Comparing neocortical and hippocampal functional gradients between TLE patients and healthy controls, we observed a marked topographic reorganization of both neocortical and MTL systems during episodic memory states. Neocortical alterations were characterized by reduced functional differentiation in TLE across lateral temporal and midline parietal cortices in both hemispheres. In the MTL, in contrast, patients presented with a more marked functional differentiation of posterior and anterior hippocampal segments ipsilateral to the seizure focus and pathological core, indicating perturbed intrahippocampal connectivity. Semantic memory reorganization was also found in bilateral lateral temporal and ipsilateral angular regions, whereas hippocampal functional topographies were unaffected. Furthermore, leveraging MRI proxies of MTL pathology, we observed alterations in hippocampal microstructure and morphology that were associated with TLE-related functional reorganization during episodic memory. Moreover, correlation analysis and statistical mediation models revealed that these functional alterations contributed to behavioural deficits in episodic memory, but again not in semantic memory in patients. Altogether, our findings suggest that semantic processes rely on distributed neocortical networks, whereas episodic processes are supported by a network involving both the hippocampus and the neocortex. Alterations of such networks can provide a compact signature of state-dependent reorganization in conditions associated with MTL damage, such as TLE.

Changes in cortical manifold structure following stroke and its relation to behavioral recovery in the male macaque

Stroke, a major cause of disability, disrupts brain function and motor skills. Previous research has mainly focused on reorganization of the motor system post-stroke, but the effects on other brain areas and their influence on recovery is poorly understood. Here, we use functional neuroimaging in a nonhuman primate model (23 male Cynomolgus Macaques), we explore how ischemic stroke affects whole-brain cortical architecture and its relation to spontaneous behavioral recovery. By projecting patterns of cortical functional connectivity onto a low-dimensional manifold space, we find that several regions in both sensorimotor cortex and higher-order transmodal cortex exhibit significant shifts in their manifold embedding from pre- to post-stroke. Furthermore, we observe that changes in default mode and limbic network regions, and not preserved sensorimotor cortical regions, are associated with animal behavioral recovery post-stroke. These results establish the whole-brain functional changes associated with stroke, and suggest an important role for higher-order transmodal cortex in post-stroke outcomes.

Multimodal gradients of basal forebrain connectivity across the neocortex

Cortical cholinergic projections originate from subregions of the basal forebrain (BF). To examine its organization in humans, we computed multimodal gradients of BF connectivity by combining 7 T diffusion and resting state functional MRI. Moving from anteromedial to posterolateral BF, we observe reduced tethering between structural and functional connectivity gradients, with the lowest tethering in the nucleus basalis of Meynert. In the neocortex, this gradient is expressed by progressively reduced tethering from unimodal sensory to transmodal cortex, with the lowest tethering in the midcingulo-insular network, and is also spatially correlated with the molecular concentration of VAChT, measured by [18F]fluoroethoxy-benzovesamicol (FEOBV) PET. In mice, viral tracing of BF cholinergic projections and [18F]FEOBV PET confirm a gradient of axonal arborization. Altogether, our findings reveal that BF cholinergic neurons vary in their branch complexity, with certain subpopulations exhibiting greater modularity and others greater diffusivity in the functional integration with their cortical targets.

Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI

We assess the potential of detecting cortical laminar patterns and areal borders by directly clustering voxel values of microstructural parameters derived from high-resolution mean apparent propagator (MAP) magnetic resonance imaging (MRI), as an alternative to conventional template-warping-based cortical parcellation methods. We acquired MAP-MRI data with 200μm resolution in a fixed macaque monkey brain. To improve the sensitivity to cortical layers, we processed the data with a local anisotropic Gaussian filter determined voxel-wise by the plane tangent to the cortical surface. We directly clustered all cortical voxels using only the MAP-derived microstructural imaging biomarkers, with no information regarding their relative spatial location or dominant diffusion orientations. MAP-based 3D cytoarchitectonic segmentation revealed laminar patterns similar to those observed in the corresponding histological images. Moreover, transition regions between these laminar patterns agreed more accurately with histology than the borders between cortical areas estimated using conventional atlas/template-warping cortical parcellation. By cross-tabulating all cortical labels in the atlas- and MAP-based segmentations, we automatically matched the corresponding MAP-derived clusters (i.e., cytoarchitectonic domains) across the left and right hemispheres. Our results demonstrate that high-resolution MAP-MRI biomarkers can effectively delineate three-dimensional cortical cytoarchitectonic domains in single individuals. Their intrinsic tissue microstructural contrasts enable the construction of whole-brain mesoscopic cortical atlases.

Macroscale Gradient Dysfunction in Alzheimer's Disease: Patterns With Cognition Terms and Gene Expression Profiles

Macroscale functional gradient techniques provide a continuous coordinate system that extends from unimodal regions to transmodal higher-order networks. However, the alterations of these functional gradients in AD and their correlations with cognitive terms and gene expression profiles remain to be established. In the present study, we directly studied the functional gradients with functional MRI data from seven scanners. We adopted data-driven meta-analytic techniques to unveil AD-associated changes in the functional gradients. The principal primary-to-transmodal gradient was suppressed in AD. Compared to NCs, AD patients exhibited global connectome gradient alterations, including reduced gradient range and gradient variation, increased gradient scores in the somatomotor, ventral attention, and frontoparietal regions, and decreased in the default mode network. More importantly, the Gene Ontology terms of biological processes were significantly enriched in the potassium ion transport and protein-containing complex remodeling. Our compelling evidence provides a new perspective in understanding the connectome alterations in AD.

Macroscale connectome topographical structure reveals the biomechanisms of brain dysfunction in Alzheimer's disease

The intricate spatial configurations of brain networks offer essential insights into understanding the specific patterns of brain abnormalities and the underlying biological mechanisms associated with Alzheimer's disease (AD), normal aging, and other neurodegenerative disorders. This study investigated alterations in the topographical structure of the brain related to aging and neurodegenerative diseases by analyzing brain gradients derived from structural MRI data across multiple cohorts (n = 7323). The analysis identified distinct gradient patterns in AD, aging, and other neurodegenerative conditions. Gene enrichment analysis indicated that inorganic ion transmembrane transport was the most significant term in normal aging, while chemical synaptic transmission is a common enrichment term across various neurodegenerative diseases. Moreover, the findings show that each disorder exhibits unique dysfunctional neurophysiological characteristics. These insights are pivotal for elucidating the distinct biological mechanisms underlying AD, thereby enhancing our understanding of its unique clinical phenotypes in contrast to normal aging and other neurodegenerative disorders.