Brain Hubs Research Articles

Structural brain network organization in children with prenatal alcohol exposure

IntroductionThere is growing evidence suggesting that children with prenatal alcohol exposure (PAE) struggle with cognitively demanding tasks, such as learning, attention, and language. Complex structural network analyses can provide insight into the neurobiological underpinnings of these functions, as they may be sensitive for characterizing the effects of PAE on the brain. However, investigations on how PAE affects brain networks are limited. We aim to compare diffusion magnetic resonance imaging (MRI) tractography-based structural networks between children with low-to-moderate PAE in trimester 1 only (T1) or throughout all trimesters (T1-T3) with those without alcohol exposure prenatally. MethodsOur cohort included three groups of children aged 6 to 8 years: 1) no PAE (n = 24), 2) low-to-moderate PAE during T1 only (n = 30), 3) low-to-moderate PAE throughout T1-T3 (n = 36). Structural networks were constructed using the multi-shell multi-tissue constrained spherical deconvolution tractography technique. Quantitative group-wise analyses were conducted at three levels: (a) at the whole-brain network level, using both network-based statistical analyses and network centrality; and then using network centrality at (b) the modular level, and (c) per-region level, including the regions identified as brain hubs. ResultsCompared with the no PAE group, widespread brain network alterations were observed in the PAE T1-T3 group using network-based statistics, but no alterations were observed for the PAE T1 group. Network alterations were also detected at the module level in the PAE T1-T3 compared with the no PAE group, with lower eigenvector centrality in the module that closely represented the right cortico-basal ganglia-thalamo-cortical network. No significant group differences were found in network centrality at the per-region level, including the hub regions. ConclusionsThis study demonstrated that low-to-moderate PAE throughout pregnancy may alter brain structural connectivity, which may explain the neurodevelopmental deficits associated with PAE. It is possible that timing and duration of alcohol exposure are crucial, as PAE in T1 only did not appear to alter brain structural connectivity.

New Insights into Freezing of Gait in Parkinson's Disease from Spectral Dynamic Causal Modeling.

Freezing of gait is one of the most disturbing motor symptoms of Parkinson's disease (PD). However, the effective connectivity between key brain hubs that are associated with the pathophysiological mechanism of freezing of gait remains elusive. The aim of this study was to identify effective connectivity underlying freezing of gait. This study applied spectral dynamic causal modeling (DCM) of resting-state functional magnetic resonance imaging in dedicated regions of interest determined using a data-driven approach. Abnormally increased functional connectivity between the bilateral dorsolateral prefrontal cortex (DLPFC) and the bilateral mesencephalic locomotor region (MLR) was identified in freezers compared with nonfreezers. Subsequently, spectral DCM analysis revealed that increased top-down excitatory effective connectivity from the left DLPFC to bilateral MLR and an independent self-inhibitory connectivity within the left DLPFC in freezers versus nonfreezers (>99% posterior probability) were inversely associated with the severity of freezing of gait. The lateralization of these effective connectivity patterns was not attributable to the initial dopaminergic deficit nor to structural changes in these regions. We have identified novel effective connectivity and an independent self-inhibitory connectivity underlying freezing of gait. Our findings imply that modulating the effective connectivity between the left DLPFC and MLR through neurostimulation or other interventions could be a target for reducing freezing of gait in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder.

Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.

Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

Trade-offs between Complexity and Hub Presence in Network-Based Brain Models

In the emerging field of computational neuroscience, the architecture of brain networks is a subject of intense study and debate. While models that only consider complex systems provide significant insights into neuronal interconnections, they often overlook the pivotal role of brain hubs—central nodes that manage a large number of connections. On the other hand, giving too much importance to brain hubs can lead to an oversimplification of the true complexity found in neuronal networks. This paper explores the challenges and trade-offs of incorporating both complexity and hubs in brain models. Through a custom-built model featuring five hubs with varying weights and distances, we investigate how these elements interact and influence the emergent network properties such as alpha brain wave patterns. Our findings suggest that a balanced approach that considers both complexity and the presence of hubs yields a more accurate and nuanced understanding of brain network architecture.

Sleep and quiet wakefulness signify an idling brain hub for creative insights.

Long-term potentiation of synaptic strength is a fundamental aspect of learning and memory. Memories are believed to be stored within specific populations of neurons known as engram cells, which are subsequently reactivated during sleep, facilitating the consolidation of stored information. However, sleep and offline reactivations are associated not only with past experiences but also with anticipation of future events. During periods of offline reactivation, which occur during sleep and quiet wakefulness, the brain exhibits a capability to form novel connections. This process links various past experiences, often leading to the emergence of qualitatively new information that was not initially available. Brain activity during sleep and quiet wakefulness is referred to as the 'idling brain'. Idling brain activity is believed to play a pivotal role in abstracting essential information, comprehending underlying rules, generating creative ideas and fostering insightful thoughts. In this review, we will explore the current state of research and future directions in understanding how sleep and idling brain activity are interconnected with various cognitive functions, especially creative insights. These insights have profound implications for our daily lives, impacting our ability to process information, make decisions and navigate complex situations effectively. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Development of segregation and integration of functional connectomes during the first 1,000 days

The first 1,000days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.

Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex.

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.

The brain network hub degeneration in Alzheimer's disease.

Alzheimer's disease (AD) has been conceptualized as a syndrome of brain network dysfunction. Recent imaging connectomics studies have provided unprecedented opportunities to map structural and functional brain networks in AD. By reviewing molecular, imaging, and computational modeling studies, we have shown that highly connected brain hubs are primarily distributed in the medial and lateral prefrontal, parietal, and temporal regions in healthy individuals and that the hubs are selectively and severely affected in AD as manifested by increased amyloid-beta deposition and regional atrophy, hypo-metabolism, and connectivity dysfunction. Furthermore, AD-related hub degeneration depends on the imaging modality with the most notable degeneration in the medial temporal hubs for morphological covariance networks, the prefrontal hubs for structural white matter networks, and in the medial parietal hubs for functional networks. Finally, the AD-related hub degeneration shows metabolic, molecular, and genetic correlates. Collectively, we conclude that the brain-network-hub-degeneration framework is promising to elucidate the biological mechanisms of network dysfunction in AD, which provides valuable information on potential diagnostic biomarkers and promising therapeutic targets for the disease.

Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia.

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.

Long-range connections damage in white matter hyperintensities affects information processing speed.

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

Creating evidence-based engaging online learning resources in neuroanatomy.

Online anatomical resources are rising in popularity since the COVID-19 pandemic, but the pedagogical principles and effectiveness of their use remain unclear. This article aims to demonstrate evidence-informed ways in which fellow educators can create engaging online learning resources in clinical neuroanatomy and compare the effectiveness of text-based and online learning resources. Data were analyzed from the Soton Brain Hub (SBH) YouTube page. Separately, a cross-sectional study comparing the learning gain of using text-based and video resources was done. The knowledge gain and retention were compared between groups using a pre-teaching and post-teaching multiple choice questions. YouTube analytics showed the average time a viewer spends on a video was found to be highly correlated to the length of the video, r = 0.77, p < 0.001 (0.69-0.82). The cross-sectional study indicated a significant difference in mean normalized learning gain of video resources 61.9% (n = 53, CI 56.0-67.7%) versus text resources 49.6% (n = 23, CI 39.1-60.1%) (p = 0.030). However, there was no difference in retained learning gain between video resources 39.1% (n = 29, CI 29.2-49.0%) versus text-based 40.0% (n = 13, CI 23.9-56.1%) (p = 0.919). Students engage most with short videos less than 5 min which reduces the intrinsic load of learning. Online resources are as effective as text-based resources in providing learning gain and retention. In the future, the continued rise in popularity of online learning resources may result in further reduction in traditional face-to-face teaching.

Electroconvulsive therapy enhances degree centrality in the orbitofrontal cortex in depressive rumination

Depressive rumination has been implicated in the onset, duration, and treatment response of refractory depression. Electroconvulsive therapy (ECT) is remarkably effective in treatment of refractory depression by modulating the functional coordination between brain hubs. However, the mechanisms by which ECT regulates depressive rumination remain unsolved. We investigated degree centrality (DC) in 32 pre- and post-ECT depression patients as well as 38 matched healthy controls. An identified brain region was defined as the seed to calculate functional connectivity (FC) in whole brains. Rumination was measured by the Ruminative Response Scale (RRS) and its relationships with identified DC and FC alterations were examined. We found a significant negative correlation between DC of the right orbitofrontal cortex (rOFC) before ECT and brooding level before and after treatment. Moreover, rOFC DC increased after ECT. DC of the left superior temporal gyrus (lSTG) was positively correlated with reflective level before intervention, while lSTG DC decreased after ECT. Patients showed elevated FC in the rOFC with default mode network. No significant association was found between decreased RRS scores and changes in DC and FC. Our findings suggest that functional changes in rOFC and lSTG may be associated with the beneficial effects of ECT on depressive rumination.

Functional Connectivity and White Matter integrity of intercommunity hub nodes in Subjective Cognitive Decline

AbstractBackgroundSubjective Cognitive Decline (SCD) is considered an early preclinical stage of Alzheimer’s Disease and related dementias (ADRD) which may show promise for targeted preventative treatments. Subtle changes in functional connectivity and white matter integrity (WMI) may occur in SCD. Few studies have applied both functional graph theory (GT) and diffusional kurtosis imaging (DKI) techniques in tandem to identify differences in vulnerable network hubs. We addressed these issues by applying GT and DKI to study differences in intercommunity brain hubs. The hypothesis was that in hubs identified in healthy controls, SCD subjects’ hubs would show decreased GT diversity coefficient (DC) and DKI mean kurtosis (MK).MethodImages were acquired on a Siemens PRISMA scanner. Matlab was used to calculate DC and identify 5 hubs in 107 healthy controls (HC) and DC in 30 SCD (93 females, mean age = 67.51). SCD classification was determined by a comprehensive clinician evaluation or Everyday Cognition average item score&gt;1.6, with objectively healthy cognitive performance (Montreal Cognitive Assessment score&gt;22). Hub DC was the target in GLMM analysis, with diagnosis as predictor. Mean Kurtosis (MK) was analyzed in DSI Studio’s cross‐sectional connectometry using the hubs as seeds. Only the covariate of gray matter volume approached significance (p = .059), and was included in analysis. Mean age, education, head motion, and sex balance did not significantly differ between groups.ResultAll hubs had lower DC in SCD compared to HC (p ≤ .006). Connectometry identified positive correlation between MK and diagnosis in Left hemisphere tracts associated with two hubs: Inferior Fronto‐Occipital, Extreme capsule, and Uncinate tracts from Insular cortex, and Superior Longitudinal, Superior Corticalstriatal, Arcuate, Frontal Aslant, and Corticalspinal tracts from Middle Frontal gyrus.Conclusion Conclusion Rs‐fMRI findings show that intercommunity functional hubs are weaker in SCD compared to healthy individuals. Furthermore, the study identified a relationship of higher MK values in SCD for hubs located in insular cortex and middle frontal gyrus, all left hemisphere biased. Higher MK in SCD also runs counter to our hypothesis but is supported by recent paradoxical increase theories of decline. WMI differences between groups may be subtler than functional changes, warranting examination of the 2 modalities’ relationship.

Immediate neural impact and incomplete compensation after semantic hub disconnection

The human brain extracts meaning using an extensive neural system for semantic knowledge. Whether broadly distributed systems depend on or can compensate after losing a highly interconnected hub is controversial. We report intracranial recordings from two patients during a speech prediction task, obtained minutes before and after neurosurgical treatment requiring disconnection of the left anterior temporal lobe (ATL), a candidate semantic knowledge hub. Informed by modern diaschisis and predictive coding frameworks, we tested hypotheses ranging from solely neural network disruption to complete compensation by the indirectly affected language-related and speech-processing sites. Immediately after ATL disconnection, we observed neurophysiological alterations in the recorded frontal and auditory sites, providing direct evidence for the importance of the ATL as a semantic hub. We also obtained evidence for rapid, albeit incomplete, attempts at neural network compensation, with neural impact largely in the forms stipulated by the predictive coding framework, in specificity, and the modern diaschisis framework, more generally. The overall results validate these frameworks and reveal an immediate impact and capability of the human brain to adjust after losing a brain hub.

Atypical Hierarchical Connectivity Revealed by Stepwise Functional Connectivity in Aging.

Hierarchical functional structure plays a crucial role in brain function. We aimed to investigate how aging affects hierarchical functional structure and to evaluate the relationship between such effects and molecular, microvascular, and cognitive features. We used resting-state functional magnetic resonance imaging (fMRI) data from 95 older adults (66.94 ± 7.23 years) and 44 younger adults (21.8 ± 2.53 years) and employed an innovative graph-theory-based analysis (stepwise functional connectivity (SFC)) to reveal the effects of aging on hierarchical functional structure in the brain. In the older group, an SFC pattern converged on the primary sensory-motor network (PSN) rather than the default mode network (DMN). Moreover, SFC decreased in the DMN and increased in the PSN at longer link-steps in aging, indicating a reconfiguration of brain hub systems during aging. Subsequent correlation analyses were performed between SFC values and molecular, microvascular features, and behavioral performance. Altered SFC patterns were associated with dopamine and serotonin, suggesting that altered hierarchical functional structure in aging is linked to the molecular fundament with dopamine and serotonin. Furthermore, increased SFC in the PSN, decreased SFC in the DMN, and accelerated convergence rate were all linked to poorer microvascular features and lower executive function. Finally, a mediation analysis among SFC features, microvascular features, and behavioral performance indicated that the microvascular state may influence executive function through SFC features, highlighting the interactive effects of SFC features and microvascular state on cognition.

Diverse processing of pharmacological and natural rewards by the central amygdala

The central amygdala (CeA) with its medial (CeM) and lateral (CeL) nuclei is the brain hub for processing stimuli with emotional context. CeL nucleus gives a strong inhibitory input to the CeM, and this local circuitry assigns values (positive or negative) to incoming stimuli, guiding appropriate behavior (approach or avoid). However, the particular involvement of CeA in processing such emotionally relevant information and adaptations of the CeA circuitry are not yet well understood. In this study, we examined synaptic plasticity in the CeA after exposure to two types of rewards, pharmacological (cocaine) and natural (sugar). We found that both rewards engage CeM, where they generate silent synapses resulting in the strengthening of the network. However, only cocaine triggers plasticity in the CeL, which leads to the weakening of its excitatory inputs. Finally, chemogenetic inhibition of CeM attenuates animal preference for sugar, while activation delays cocaine-induced increase in locomotor activity.

Mitofusin-2 in nucleus accumbens D2-MSNs regulates social dominance and neuronal function

The nucleus accumbens (NAc) is a brain hub regulating motivated behaviors, including social competitiveness. Mitochondrial function in the NAc links anxiety with social competitiveness, and the mitochondrial fusion protein mitofusin 2 (Mfn2) in NAc neurons regulates anxiety-related behaviors. However, it remains unexplored whether accumbal Mfn2 levels also affect social behavior and whether Mfn2 actions in the emotional and social domain are driven by distinct cell types. Here, we found that subordinate-prone highly anxious rats show decreased accumbal Mfn2 levels and that Mfn2 overexpression promotes dominant behavior. In mice, selective Mfn2 downregulation in NAc dopamine D2 receptor-expressing medium spiny neurons (D2-MSNs) induced social subordination, accompanied by decreased accumbal mitochondrial functions and decreased excitability in D2-MSNs. Instead, D1-MSN-targeted Mfn2 downregulation affected competitive ability only transiently and likely because of an increase in anxiety-like behaviors. Our results assign dissociable cell-type specific roles to Mfn2 in the NAc in modulating social dominance and anxiety.

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector ‘hubs’ are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed ‘variants’ in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

Identifying network hubs from brain anatomical similarity networks

AbstractBackgroundBrain hubs are highly connected regions important for integrating cognitive processing in the healthy brain. Brain hubs also play role in neurodegeneration causing dementia. However, it is not clear what brain morphometry is most useful in identifying brain regions critical to their involvement in neurodegeneration.MethodWe examined brain network hubs from four measures of cortical morphometry, derived using anatomical T1‐weigher MRI. From a large cohort of healthy participants (N = 260) from 20 to 85 years of age, we generated 200 networks for each measure of cortical morphometry using bootstrapping. We then tested how two measures of brain network hubness – nodal strength and clustering coefficient – were mapped onto brain network hubs across all 200 networks for each morphometric feature. Regions were identified as brain hubs if ranked two standard deviations away from the corresponding group mean.ResultWe mapped out brain hubs for each four measures (features) of brain morphometry. Nodal strength and clustering coefficient showed similarity in the behaviour across the features: gray volume, regional surface area and its thickness and thickness standard deviation. However, the differences were found in what brain region qualify as a network hub for each feature.ConclusionCortical morphometry extracted from anatomical brain scans can be used as a potential imaging marker for identification of brain hubs potentially involved in early differentiation of dementia subtypes.