Brain Networks Research Articles

Cochlear implant induced changes in cortical networks associated with tinnitus severity

We investigated tinnitus-related cortical networks in cochlear implant users who experience tinnitus and whose perception of tinnitus changes with use of their implant. &#xD;&#xD;Tinnitus, the perception of unwanted sounds which are not present externally, can be a debilitating condition. In individuals with cochlear implants, use of the implant is known to modulate tinnitus, often improving symptoms but worsening them in some cases. Little is known about underlying cortical changes with use of the implant, which lead to changes in tinnitus perception. In this study we investigated whether changes in brain networks with the cochlear implant turned on and off, were associated with changes in tinnitus perception, as rated subjectively.&#xD;&#xD;Approach: Using functional near-infrared spectroscopy (fNIRS), we recorded cortical activity at rest, from 14 cochlear implant users who experienced tinnitus. Recordings were performed with the cochlear implant turned off and on. For each condition, participants rated the loudness and annoyance of their tinnitus using a visual rating scale. Changes in neural synchrony have been reported in humans and animal models of tinnitus. To assess neural synchrony, functional connectivity networks with the implant turned on and off, were compared using two network features: node strength and diversity coefficient. &#xD;&#xD;Main results: Changes in subjective ratings of loudness were significantly correlated with changes in node strength, averaged across occipital channels (r=-0.65,p = 0.01). Changes in both loudness and annoyance were significantly correlated with changes in diversity coefficient averaged across all channels (r=-0.79,p<0.001 and r = -0.86,p<0.001). More distributed connectivity with the implant on, compared to implant off, was associated with a reduction in tinnitus loudness and annoyance. &#xD;&#xD;Significance: A better understanding of neural mechanisms underlying tinnitus suppression with cochlear implant use, could lead to their application as a tinnitus treatment and pave the way for effective use of other less invasive stimulation-based treatments. &#xD.

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distributed neuromorphic system, however, remains time-consuming and challenging due to considerations such as network topology, connection rules, and compatibility with multiple programming languages. We proposed a multiscale distributed neural computing model database (NCMD), which is a framework designed for ARM-based multi-core hardware. Various neural computing components, including ion channels, synapses, and neurons, are encompassed in NCMD. We demonstrated how NCMD constructs and deploys multi-compartmental detailed neuron models as well as spiking neural networks (SNNs) in BrainS, a distributed multi-ARM neuromorphic system. We demonstrated that the electrodiffusive Pinsky–Rinzel (edPR) model developed by NCMD is well-suited for BrainS. All dynamic properties, such as changes in membrane potential and ion concentrations, can be easily explored. In addition, SNNs constructed by NCMD can achieve an accuracy of 86.67% on the test set of the Iris dataset. The proposed NCMD offers an innovative approach to applying BrainS in neuroscience, cognitive decision-making, and artificial intelligence research.

Distinct neural pathway and its information flow for blind individual's Braille reading

Natural Braille reading presents significant challenges to the brain networks of late blind individuals, yet its underlying neural mechanisms remain largely unexplored. Using natural Braille texts in behavioral assessments and functional MRI, we sought to pinpoint the neural pathway and information flow crucial for Braille reading performance in late blind individuals. In the resting state, we discovered a unique neural connection between the higher-order ‘visual’ cortex, the lateral occipital cortex (LOC), and the inferior frontal cortex (IFC) in late blind individuals, but not in sighted controls. The left-lateralized LOC-IFC connectivity was correlated with individual Braille reading proficiency. Prolonged Braille reading practice led to increased strength of this connectivity. During a natural Braille reading task, bidirectional information flow between the LOC and the IFC was positively modulated, with a predominantly stronger top-down modulation from the IFC to the LOC. This stronger top-down modulation contributed to higher Braille reading proficiency. We thus proposed a two-predictor multiple regression model to predict individual Braille reading proficiency, incorporating both static connectivity and dynamic top-down communication between the LOC-IFC link. This work highlights the dual contributions of the occipito-frontal neural pathway and top-down cognitive strategy to superior natural Braille reading performance, offering guidance for training late blind individuals.

Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.

Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modality of data, resulting in limited information extraction and poor stability. In this paper, we propose a bilinear perceptual fusion (BPF) algorithm that leverages data from multiple modalities. In our algorithm, different schemes are used to extract features according to the characteristics of functional and structural data. Through bilinear operations, the associations between the functional and structural features of each region of interest (ROI) are captured. Then the associations are used to integrate the feature representation. Graph convolutional neural networks (GCNs) can effectively utilize topology and node features in brain network analysis. Therefore, we design a deep learning framework called BPF-GCN and conduct experiments on publicly available ASD dataset. The results show that the classification accuracy of BPF-GCN reached 82.35%, surpassing existing methods. This demonstrates the superiority of its classification performance, and the framework can extract ROIs related to ASD. Our work provides a valuable reference for the timely diagnosis and treatment of ASD.

A distinct neuronal ensemble of prelimbic cortex mediates spontaneous pain in rats with peripheral inflammation

The absence of a comprehensive understanding of the neural basis of spontaneous pain limits the development of therapeutic strategies targeting this primary complaint of patients with chronic pain. Here we report a distinct neuronal ensemble within the prelimbic cortex which processes signals related to spontaneous pain in rats with chronic inflammatory pain. This neuronal ensemble specifically encodes spontaneous pain-related behaviors, independently of other locomotive and evoked behaviors. Activation of this neuronal ensemble elicits marked spontaneous pain-like behaviors and enhances nociceptive responses, whereas prolonged silencing of its activities alleviates spontaneous pain and promotes overall recovery from inflammatory pain. Notably, afferents from the primary somatosensory cortex and infralimbic cortex bidirectionally modulate the activities of the spontaneous pain-responsive prelimbic cortex neuronal ensemble and pain behaviors. These findings reveal the cortical basis of spontaneous pain at the neuronal level, highlighting a distinct neuronal ensemble within the prelimbic cortex and its associated pain-regulatory brain networks.

Functional Connectivity Profiles of Ten Sub-Regions within the Premotor and Supplementary Motor Areas: Insights into Neurophysiological Integration.

Objectives: This study aimed to comprehensively investigate the functional connectivity of ten sub-regions within the premotor and supplementary motor areas (Right and Left Premotor 6d1, 6d2, 6d3, and Right and Left pre-Supplementary Motor (presma) and SMA). Using advanced magnetic resonance imaging (MRI), the objective was to understand the neurophysiological integrative characteristics of these regions by examining their connectivity with eight distinct functional brain networks. While previous studies have largely treated these areas as homogeneous entities, there is a significant gap in our understanding of the specific roles and connectivity profiles of their distinct sub-regions. The goal was to uncover the roles of these regions beyond conventional motor functions, contributing to a more holistic understanding of brain functioning. Methods: The study involved 198 healthy volunteers, with the primary methodology being functional connectivity analysis using advanced MRI techniques. Ten sub-regions within the premotor and supplementary motor areas served as seed regions, and their connectivity with eight distinct brain regional functional networks, including the Sensorimotor, Dorsal Attention, Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks, was investigated. This approach allowed for the exploration of synchronized activity between these critical brain areas, shedding light on their integrated functioning and relationships with other brain networks. Results: The study revealed a nuanced landscape of functional connectivity for the premotor and supplementary motor areas with the main functional brain networks. Despite their high functional connectedness within the motor network, these regions displayed diverse functional integrations with other networks. There was moderate connectivity with the Sensorimotor and Dorsal Attention networks, highlighting their roles in motor execution and attentional processes. However, connectivity with the Language, Frontoparietal, Default Mode, Cerebellar, Visual, and Salience networks was generally low, indicating a primary focus on motor-related tasks. Conclusions: This study emphasized the multifaceted roles of the sub-regions of the premotor and supplementary motor areas. Beyond their crucial involvement in motor functions, these regions exhibited varied functional integrations with different brain networks. The observed disparities, especially in the Sensorimotor and Dorsal Attention networks, indicated a nuanced and specialized involvement of these regions in diverse cognitive functions. By delineating the specific connectivity profiles of these sub-regions, this study addresses the existing knowledge gap and suggests unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. The results suggested unique and distinct roles for each brain area in sophisticated cognitive tasks beyond their conventional motor functions. This study underscores the importance of considering the broader neurophysiological landscape to comprehend the intricate roles of these brain areas, contributing to ongoing efforts in unravelling the complexities of brain function.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal–external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

Beauty is in the brain networks of the beholder: An exploratory functional magnetic resonance imaging study.

Beauty is in the brain networks of the beholder: An exploratory functional magnetic resonance imaging study.

Resting-state brain networks alterations in adolescents with Internet Gaming Disorder associate with cognitive control impairments

ObjectiveResearch indicates that cognitive control is compromised in individuals with internet gaming disorder (IGD). However, the neural mechanisms behind it are still unclear. This study aims to investigate alterations in resting-state brain networks in adolescents with IGD and the potential neurobiological mechanisms underlying cognitive dysfunction.Materials and methodsA total of 44 adolescent IGD subjects (male/female: 38/6) and 50 healthy controls (male/female: 40/10) were enrolled. Participants underwent demographic assessments, Young’s Internet Addiction Scale, Barratt Impulsiveness Scale 11 Chinese Revised Version, the Chinese Adolescents’ Maladaptive Cognitions Scale, exploratory eye movement tests, and functional magnetic resonance imaging (fMRI). FMRI data were analyzed using the GIFT software for independent component analysis, focusing on functional connectivity within and between resting-state brain networks.ResultsIn comparison to the control group, impulsivity in adolescent IGD subjects showed a positive correlation with the severity of IGD (r=0.6350, p &amp;lt; 0.001), linked to impairments in the Executive Control Network (ECN) and a decrease in functional connectivity between the Salience Network (SN) and ECN (r=0.4307, p=0.0021; r=-0.5147, p=0.0034). Decreased resting state activity of the dorsal attention network (DAN) was associated with attentional dysregulation of IGD in adolescents (r=0.4071, p=0.0017), and ECN increased functional connectivity with DAN. The degree of IGD was positively correlated with enhanced functional connectivity between the ECN and DAN (r=0.4283, p=0.0037).ConclusionsThis research demonstrates that changes in the ECN and DAN correlate with heightened impulsivity and attentional deficits in adolescents with IGD. The interaction between cognitive control disorders and resting-state brain networks in adolescent IGD is related.

Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging

Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

Frontostriatal salience network expansion in individuals in depression

Decades of neuroimaging studies have shown modest differences in brain structure and connectivity in depression, hindering mechanistic insights or the identification of risk factors for disease onset1. Furthermore, whereas depression is episodic, few longitudinal neuroimaging studies exist, limiting understanding of mechanisms that drive mood-state transitions. The emerging field of precision functional mapping has used densely sampled longitudinal neuroimaging data to show behaviourally meaningful differences in brain network topography and connectivity between and in healthy individuals2–4, but this approach has not been applied in depression. Here, using precision functional mapping and several samples of deeply sampled individuals, we found that the frontostriatal salience network is expanded nearly twofold in the cortex of most individuals with depression. This effect was replicable in several samples and caused primarily by network border shifts, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was stable over time, unaffected by mood state and detectable in children before the onset of depression later in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in frontostriatal circuits that tracked fluctuations in specific symptoms and predicted future anhedonia symptoms. Together, these findings identify a trait-like brain network topology that may confer risk for depression and mood-state-dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

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.

Glymphatic influx and clearance are perturbed in Huntington's disease.

The accumulation of mutant huntingtin protein aggregates in neurons is a pathological hallmark of Huntington's disease (HD). The glymphatic system, a brain-wide perivascular network, facilitates the exchange of interstitial fluid (ISF) and cerebrospinal fluid (CSF), supporting interstitial solute clearance of brain wastes. In this study, we employed dynamic glucose-enhanced (DGE) MRI to measure D-glucose clearance from CSF as a tool to predict glymphatic function in a mouse model of HD. We found significantly diminished CSF clearance efficiency in HD mice prior to phenotypic onset. The impairment of CSF clearance efficiency worsened with disease progression. These DGE MRI findings in compromised glymphatic function were further confirmed with fluorescence-based imaging of CSF tracer influx, suggesting an impaired glymphatic function in premanifest HD. Moreover, expression of the astroglial water channel aquaporin-4 (AQP4) in the perivascular compartment, a key mediator of glymphatic function, was significantly diminished in both HD mouse brain and human HD brain. Our data, acquired using a clinically translatable MRI, indicate a perturbed glymphatic network in the HD brain. Further validation of these findings in clinical studies will provide insights into the potential of glymphatic clearance as a therapeutic target as well as an early biomarker in HD.

Eight brain structures mediate the age-related alterations of the working memory: forward and backward digit span tasks.

Working memory (WM) as one of the executive functions is an essential neurocognitive ability for daily life. Findings have suggested that aging is often associated with working memory and neural decline, but the brain structures and resting-state brain networks that mediate age-related differences in WM remain unclear. A sample consisting of 252 healthy participants in the age range of 20 to 70years was used. Several cognitive tasks, including the n-back task and the forward and backward digit span tests were used. Also, resting-state functional imaging, as well as structural imaging using a 3T MRI scanner, were performed, resulting in 85 gray matter volumes and five resting-state networks, namely the anterior and posterior default mode, the right and left executive control, and the salience networks. Also, mediation analyses were used to investigate the role of gray matter volumes and resting-state networks in the relationship between age and WM. Behaviorally, aging was associated with decreased performance in the digit span task. Also, aging was associated with a decreased gray matter volume in 80 brain regions, and with a decreased activity in the anterior default mode network, executive control, and salience networks. Importantly, the path analysis showed that the GMV of the medial orbitofrontal, precentral, parieto-occipital, amygdala, middle occipital, posterior cingulate, and thalamus areas mediated the age-related differences in the forward digit span task, and the GMV of superior temporal gyrus mediated the age-related differences in the backward digit span task. This study identified the brain structures mediating the relationship between age and working memory, and we hope that our research provides an opportunity for early detection of individuals at risk of age-related memory decline.

Structure-function coupling in highly sampled individual brains.

Structural connectivity (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to SC may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78min of diffusion-weighted MRI for SC and 360min of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas.

Neuroimaging Biomarkers in Addiction.

As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.

Comparative study on structural and functional brain differences in mild cognitive impairment patients with tinnitus.

Tinnitus may be associated with various brain changes. However, the degenerative changes in patients with tinnitus have not been extensively investigated. We aimed to evaluate degenerative, structural, and functional brain changes in patients with mild cognitive impairment (MCI) who also suffer from tinnitus. This study included participants aged 60 to 80 years with MCI and a hearing level better than 40 dB. The participants were classified into two groups: MCI with tinnitus (MCI-T) and MCI without tinnitus (MCI-NT). All patients underwent Tinnitus Handicap Inventory (THI), 3 T brain MRI, F18-florapronol PET, and F18-FDG PET. The MCI-T group exhibited higher β-amyloid deposition in the superior temporal gyrus, temporal pole, and middle temporal gyrus compared to the MCI-NT group (p < 0.05 for all). Additionally, the MCI-T group showed increased metabolism in the inferior frontal gyrus, insula, and anterior cingulate cortex (ACC) (p < 0.005 for all). The THI score was strongly correlated with increased volume in the insula, ACC, superior frontal gyrus, supplementary motor area, white matter near the hippocampus, and precentral gyrus (p < 0.05 for all). Moreover, the MCI-T group demonstrated higher metabolic activity in the default mode network (DMN) and lower activity in the executive control network (ECN) (p < 0.05 for all). In the MCI-T group, the posterior DMN was positively correlated with the visual network and negatively with the ECN, whereas in the MCI-NT group, it correlated positively with the ECN. The MCI-T group exhibited greater β-amyloid accumulation in the auditory cortex and more extensive changes across various brain networks compared with the MCI-NT group, potentially leading to diverse clinical symptoms such as dementia with semantic deficits or depression. Tinnitus in MCI patients may serve as a biomarker for degenerative changes in the temporal lobe and alterations in brain network dynamics.

Progress towards understanding risk factor mechanisms in the development of autism spectrum disorders.

Autism spectrum disorders (ASD) are a heterogenous set of syndromes characterised by social impairment and cognitive symptoms. Currently, there are limited treatment options available to help people with ASD manage their symptoms. Understanding the biological mechanisms that result in ASD diagnosis and symptomatology is an essential step in developing new interventional strategies. Human genetic studies have identified common gene variants of small effect and rare risk genes and copy number variants (CNVs) that substantially increase the risk of developing ASD. Reverse translational studies using rodent models based on these genetic variants provide new insight into the biological basis of ASD. Here we review recent findings from three ASD associated CNV mouse models (16p11.2, 2p16.3 and 22q11.2 deletion) that show behavioural and cognitive phenotypes relevant to ASD. These models have identified disturbed excitation-inhibition neurotransmitter balance, evidenced by dysfunctional glutamate and GABA signalling, as a key aetiological mechanism. These models also provide emerging evidence for serotoninergic neurotransmitter system dysfunction, although more work is needed to clarify the nature of this. At the brain network level, prefrontal cortex (PFC) dysfunctional connectivity is also evident across these models, supporting disturbed PFC function as a key nexus in ASD aetiology. Overall, published data highlight the utility and valuable insight gained into ASD aetiology from preclinical CNV mouse models. These have identified key aetiological mechanisms that represent putative novel therapeutic targets for the treatment of ASD symptoms, making them useful translational models for future drug discovery, development and validation.

Cognitive subgroups of affective and non-affective psychosis show differences in medication and cortico-subcortical brain networks

Cognitive deficits are prevalent in individuals with psychosis and are associated with neurobiological changes, potentially serving as an endophenotype for psychosis. Using the HCP-Early-Psychosis-dataset (n = 226), we aimed to investigate cognitive subtypes (deficit/intermediate/spared) through data-driven clustering in affective (AP) and non-affective psychosis patients (NAP) and controls (HC). We explored differences between three clusters in symptoms, cognition, medication, and grey matter volume. Applying principal component analysis, we selected features for clustering. Features that explained most variance were scores for intelligence, verbal recognition and comprehension, auditory attention, working memory, reasoning and executive functioning. Fuzzy K-Means clustering on those features revealed that the subgroups significantly varied in cognitive impairment, clinical symptoms, and, importantly, also in medication and grey matter volume in fronto-parietal and subcortical networks. The spared cluster (86%HC, 37%AP, 17%NAP) exhibited unimpaired cognition, lowest symptoms/medication, and grey matter comparable to controls. The deficit cluster (4%HC, 10%AP, 47%NAP) had impairments across all domains, highest symptoms scores/medication dosage, and pronounced grey matter alterations. The intermediate deficit cluster (11%HC, 54%AP, 36%NAP) showed fewer deficits than the second cluster, but similar symptoms/medication/grey matter to the spared cluster. Controlling for medication, cognitive scores correlated with grey matter changes and negative symptoms across all patients. Our findings generally emphasize the interplay between cognition, brain structure, symptoms, and medication in AP and NAP, and specifically suggest a possible mediating role of cognition, highlighting the potential of screening cognitive changes to aid tailoring treatments and interventions.

A brain structure learning-guided multi-view graph representation learning for brain network analysis.

Resting-state brain networks represent the interconnectivity of different brain regions during rest. Utilizing brain network analysis methods to model these networks can enhance our understanding of how different brain regions collaborate and communicate without explicit external stimuli. However, analyzing resting-state brain networks faces challenges due to high heterogeneity and noise correlation between subjects. This study proposes a brain structure learning-guided multi-view graph representation learning method to address the limitations of current brain network analysis and improve the diagnostic accuracy (ACC) of mental disorders. We first used multiple thresholds to generate different sparse levels of brain networks. Subsequently, we introduced graph pooling to optimize the brain network representation by reducing noise edges and data inconsistency, thereby providing more reliable input for subsequent graph convolutional networks (GCNs). Following this, we designed a multi-view GCN to comprehensively capture the complexity and variability of brain structure. Finally, we employed an attention-based adaptive module to adjust the contributions of different views, facilitating their fusion. Considering that the Smith atlas offers superior characterization of resting-state brain networks, we utilized the Smith atlas to construct the graph network. Experiments on two mental disorder datasets, the Autism Brain Imaging Data Exchange (ABIDE) dataset and the Mexican Cocaine Use Disorders (SUDMEX CONN) dataset, show that our model outperforms the state-of-the-art methods, achieving nearly 75% ACC and 70% area under the receiver operating characteristic curve (AUC) on both datasets. These findings demonstrate that our method of combining multi-view graph learning and brain structure learning can effectively capture crucial structural information in brain networks while facilitating the acquisition of feature information from diverse perspectives, thereby improving the performance of brain network analysis.