Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We ...aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response.
In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability. In phase 2, we examined the stability (and replicability) of our phase 1 results in 4 groups (N = 105) representing a cross-sequential gradation of schizophrenia based on treatment response: risperidone responders, clozapine responders, clozapine nonresponders, and clozapine nonresponders following electroconvulsive therapy. Hypothesis-free whole-brain within- and between-network connectivity were examined.
Phase 1 identified posterior and anterior cerebellar hypoconnectivity and limbic hyperconnectivity in schizophrenia at a familywise error rate–corrected cluster significance threshold of p < .01. These network aberrations had unique associations with positive symptoms, cognition, and disability. During phase 2, we replicated the phase 1 results while comparing each of the 4 schizophrenia groups to the healthy participants. The participants in 2 longitudinal subdatasets did not demonstrate a significant change in these network aberrations following risperidone or electroconvulsive therapy. Posterior cerebellar hypoconnectivity (with thalamus and cingulate) emerged as the most consistent finding; it was replicated across different stages of treatment response (Cohen’s d range −0.95 to −1.44), reproduced using different preprocessing techniques, and not confounded by educational attainment.
Posterior cerebellar-thalamo-cingulate hypoconnectivity is a consistent and stable state-independent neural marker of schizophrenia.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Advances in neuroimaging have facilitated the study of EF development early in life.•Recent research continues to identify the pivotal role of the PFC in EF.•Important changes occur in the ...prefrontal cortex across development.•The importance of brain connectivity for EF is becoming increasingly clear.•Functional brain specialisation for EF continues to develop into adolescence.
In the last decade, advances in neuroimaging technologies have given rise to a large number of research studies that investigate the neural underpinnings of executive function (EF). EF has long been associated with the prefrontal cortex (PFC) and involves both a unified, general element, as well as the distinct, separable elements of working memory, inhibitory control and set shifting. We will highlight the value of utilising advances in neuroimaging techniques to uncover answers to some of the most pressing questions in the field of early EF development. First, this review will explore the development and neural substrates of each element of EF. Second, the structural, anatomical and biochemical changes that occur in the PFC during infancy and throughout childhood will be examined, in order to address the importance of these changes for the development of EF. Third, the importance of connectivity between regions of the PFC and other brain areas in EF development is reviewed. Finally, throughout this review more recent developments in neuroimaging techniques will be addressed, alongside the implications for further elucidating the neural substrates of early EF development in the future.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Functional and structural maturation of networks comprised of discrete regions is an important aspect of brain development. The default-mode network (DMN) is a prominent network which includes the ...posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), medial temporal lobes (MTL), and angular gyrus (AG). Despite increasing interest in DMN function, little is known about its maturation from childhood to adulthood. Here we examine developmental changes in DMN connectivity using a multimodal imaging approach by combining resting-state fMRI, voxel-based morphometry and diffusion tensor imaging-based tractography. We found that the DMN undergoes significant developmental changes in functional and structural connectivity, but these changes are not uniform across all DMN nodes. Convergent structural and functional connectivity analyses suggest that PCC-mPFC connectivity along the cingulum bundle is the most immature link in the DMN of children. Both PCC and mPFC also showed gray matter volume differences, as well as prominent macrostructural and microstructural differences in the dorsal cingulum bundle linking these regions. Notably, structural connectivity between PCC and left MTL was either weak or non-existent in children, even though functional connectivity did not differ from that of adults. These results imply that functional connectivity in children can reach adult-like levels despite weak structural connectivity. We propose that maturation of PCC-mPFC structural connectivity plays an important role in the development of self-related and social-cognitive functions that emerge during adolescence. More generally, our study demonstrates how quantitative multimodal analysis of anatomy and connectivity allows us to better characterize the heterogeneous development and maturation of brain networks.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. ...However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD.
MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated.
The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions.
The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.
The application of techniques from network science to create single-layer networks of the brain and mind has resulted in significant advances in the neuro- (i.e., structural and functional brain ...networks) and cognitive sciences (i.e., cognitive network science). Recent advances in network science on multilayer networks increase the possibility that a “network of networks” might finally connect the physical brain to the intangible mind, much like physical fibre optic cables and wires connect to other layers of the internet to allow intangible social networks to form in various social media platforms. Several advances in structural brain networks, functional brain networks, cognitive networks, and multilayer networks are briefly reviewed. The possibility that these single-layer networks can be connected in a multilayer network to connect the brain to the mind is discussed, as well as some of the challenges that face such an ambitious endeavour. (PsycInfo Database Record (c) 2024 APA, all rights reserved) (Source: journal abstract)
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CEKLJ, FFLJ, NUK, ODKLJ, PEFLJ, UPUK
The brain frontoparietal regions and the functional communications between them are critical in supporting working memory and other executive functions. The functional connectivity between ...frontoparietal regions are modulated by working memory loads, and are shown to be modulated by a third brain region in resting‐state. However, it is largely unknown whether the third‐region modulations remain the same during working memory tasks or were largely modulated by task demands. In the current study, we collected functional MRI (fMRI) data when the subjects were performing n‐back tasks and in resting‐state. We first used a block‐designed localizer to define the frontoparietal regions that showed higher activations in the 2‐back than the 1‐back condition. Next, we performed physiophysiological interaction (PPI) analysis using left and right middle frontal gyrus (MFG) and superior parietal lobule (SPL) regions, respectively, in three continuous‐designed runs of resting‐state, 1‐back, and 2‐back conditions. No regions showed consistent modulatory interactions with the seed pairs in the three conditions. Instead, the anterior cingulate cortex (ACC) showed different modulatory interactions with the right MFG and SPL among the three conditions. While the increased activity of the ACC was associated with decreased functional coupling between the right MFG and SPL in resting‐state, it was associated with increased functional coupling in the 2‐back condition. The observed task modulations support the functional significance of the modulations of the ACC on frontoparietal connectivity.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The use of psilocybin in scientific and experimental clinical contexts has triggered renewed interest in the mechanism of action of psychedelics. However, its time-dependent systems-level ...neurobiology remains sparsely investigated in humans.
We conducted a double-blind, randomized, counterbalanced, crossover study comprising 23 healthy human participants who received placebo and 0.2 mg/kg of psilocybin orally on 2 different test days. Participants underwent magnetic resonance imaging at 3 time points between administration and peak effects: 20 minutes, 40 minutes, and 70 minutes after administration. Resting-state functional connectivity was quantified via a data-driven global brain connectivity method and compared with cortical gene expression maps.
Psilocybin reduced associative, but concurrently increased sensory, brain-wide connectivity. This pattern emerged over time from administration to peak effects. Furthermore, we showed that baseline connectivity is associated with the extent of psilocybin-induced changes in functional connectivity. Lastly, psilocybin-induced changes correlated in a time-dependent manner with spatial gene expression patterns of the 5-HT2A (5-hydroxytryptamine 2A) and 5-HT1A (5-hydroxytryptamine 1A) receptors.
These results suggest that the integration of functional connectivity in sensory regions and the disintegration in associative regions may underlie the psychedelic state and pinpoint the critical role of the serotonin 2A and 1A receptor systems. Furthermore, baseline connectivity may represent a predictive marker of the magnitude of changes induced by psilocybin and may therefore contribute to a personalized medicine approach within the potential framework of psychedelic treatment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Migraine is a type of headache with multiple neurological symptoms. Prior neuroimaging studies in patients with migraine based on functional magnetic resonance imaging have found regional as well as ...network‐level alterations in brain function. Here, we expand on prior studies by establishing whole‐brain functional connectivity patterns in patients with migraine using dimensionality reduction techniques. We studied functional brain connectivity in 50 patients with episodic migraine and sex‐ and age‐matched healthy controls. Using dimensionality reduction techniques that project high‐dimensional functional connectivity onto low‐dimensional representations (i.e., eigenvectors), we found significant between‐group differences in the eigenvectors between patients with migraine and healthy controls, particularly in the sensory/motor and limbic cortices. Furthermore, we assessed between‐group differences in subcortical connectivity with subcortical weighted manifolds defined by subcortico‐cortical connectivity multiplied by cortical eigenvectors and revealed significant alterations in the amygdala. Finally, leveraging supervised machine learning, we moderately predicted headache frequency using cortical and subcortical functional connectivity features, again indicating that sensory and limbic regions play a particularly important role in predicting migraine frequency. Our study confirmed that migraine is a hierarchical disease of the brain that shows alterations along the sensory‐limbic axis, and therefore, the functional connectivity in these areas could be a useful marker to investigate migraine symptomatology.
We investigated functional connectome alterations in patients with migraine using dimensionality reduction techniques. We found significant between‐group differences in low‐dimensional representations of functional connectivity between patients with migraine and healthy controls, particularly in the sensory/motor and limbic cortices. Supervised machine learning indicated that imaging features of sensory/motor regions play an important role in predicting migraine frequency.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of ...human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture.
Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls.
In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies.
Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity.
This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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