Most recent paper

A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity

Tue, 05/28/2024 - 18:00

J Affect Disord. 2024 May 26:S0165-0327(24)00868-1. doi: 10.1016/j.jad.2024.05.125. Online ahead of print.


BACKGROUND: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci.

AIMS: Evaluate discriminative performance and generalizability of functional connectivity markers for BPD.

METHOD: Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data.

RESULTS: Full-brain connectivity allowed classification (~70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (~61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (~70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level.

LIMITATIONS: The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability.

CONCLUSIONS: Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

PMID:38806064 | DOI:10.1016/j.jad.2024.05.125

Acute hypernatremia increases functional connectivity of NaCl sensing regions in the human brain: An fMRI pilot study

Tue, 05/28/2024 - 18:00

Auton Neurosci. 2024 May 19;254:103182. doi: 10.1016/j.autneu.2024.103182. Online ahead of print.


Rodent studies demonstrated specialized sodium chloride (NaCl) sensing neurons in the circumventricular organs, which mediate changes in sympathetic nerve activity, arginine vasopressin, thirst, and blood pressure. However, the neural pathways involved in NaCl sensing in the human brain are incompletely understood. The purpose of this pilot study was to determine if acute hypernatremia alters the functional connectivity of NaCl-sensing regions of the brain in healthy young adults. Resting-state fMRI scans were acquired in 13 participants at baseline and during a 30 min hypertonic saline infusion (HSI). We used a seed-based approach to analyze the data, focusing on the subfornical organ (SFO) and the organum vasculosum of the lamina terminalis (OVLT) as regions of interest (ROIs). Blood chemistry and perceived thirst were assessed pre- and post-infusion. As expected, serum sodium increased from pre- to post-infusion in the HSI group. The primary finding of this pilot study was that the functional connectivity between the SFO and a cluster within the OVLT increased from baseline to the late-phase of the HSI. Bidirectional connectivity changes were found with cortical regions, with some regions showing increased connectivity with sodium-sensing regions while others showed decreased connectivity. Furthermore, the functional connectivity between the SFO and the posterior cingulate cortex (a control ROI) did not change from baseline to the late-phase of the HSI. This finding indicates a distinct response within the NaCl sensing network in the human brain specifically related to acute hypernatremia that will need to be replicated in large-scale studies.

PMID:38805791 | DOI:10.1016/j.autneu.2024.103182

Acute effects of different types of cannabis on young adult and adolescent resting-state brain networks

Tue, 05/28/2024 - 18:00

Neuropsychopharmacology. 2024 May 28. doi: 10.1038/s41386-024-01891-6. Online ahead of print.


Adolescence is a time of rapid neurodevelopment and the endocannabinoid system is particularly prone to change during this time. Cannabis is a commonly used drug with a particularly high prevalence of use among adolescents. The two predominant phytocannabinoids are Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), which affect the endocannabinoid system. It is unknown whether this period of rapid development makes adolescents more or less vulnerable to the effects of cannabis on brain-network connectivity, and whether CBD may attenuate the effects of THC. Using fMRI, we explored the impact of vaporized cannabis (placebo, THC: 8 mg/75 kg, THC + CBD: 8 mg/75 kg THC & 24 mg/75 kg CBD) on resting-state networks in groups of semi-regular cannabis users (usage frequency between 0.5 and 3 days/week), consisting of 22 adolescents (16-17 years) and 24 young adults (26-29 years) matched for cannabis use frequency. Cannabis caused reductions in within-network connectivity in the default mode (F[2,88] = 3.97, P = 0.022, η² = 0.018), executive control (F[2,88] = 18.62, P < 0.001, η² = 0.123), salience (F[2,88] = 12.12, P < 0.001, η² = 0.076), hippocampal (F[2,88] = 14.65, P < 0.001, η² = 0.087), and limbic striatal (F[2,88] = 16.19, P < 0.001, η² = 0.102) networks compared to placebo. Whole-brain analysis showed cannabis significantly disrupted functional connectivity with cortical regions and the executive control, salience, hippocampal, and limbic striatal networks compared to placebo. CBD did not counteract THC's effects and further reduced connectivity both within networks and the whole brain. While age-related differences were observed, there were no interactions between age group and cannabis treatment in any brain network. Overall, these results challenge the assumption that CBD can make cannabis safer, as CBD did not attenuate THC effects (and in some cases potentiated them); furthermore, they show that cannabis causes similar disruption to resting-state connectivity in the adolescent and adult brain.

PMID:38806583 | DOI:10.1038/s41386-024-01891-6

Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture

Tue, 05/28/2024 - 18:00

Natl Sci Rev. 2024 Mar 1;11(5):nwae080. doi: 10.1093/nsr/nwae080. eCollection 2024 May.


A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex (V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart, despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

PMID:38803564 | PMC:PMC11129584 | DOI:10.1093/nsr/nwae080

Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy

Mon, 05/27/2024 - 18:00

ArXiv [Preprint]. 2024 May 13:arXiv:2405.07835v1.


We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.

PMID:38800648 | PMC:PMC11118617

Functional network antagonism and consciousness

Mon, 05/27/2024 - 18:00

Netw Neurosci. 2022 Oct 1;6(4):998-1009. doi: 10.1162/netn_a_00244. eCollection 2022.


Spontaneous brain activity changes across states of consciousness. A particular consciousness-mediated configuration is the anticorrelations between the default mode network and other brain regions. What this antagonistic organization implies about consciousness to date remains inconclusive. In this Perspective Article, we propose that anticorrelations are the physiological expression of the concept of segregation, namely the brain's capacity to show selectivity in the way areas will be functionally connected. We postulate that this effect is mediated by the process of neural inhibition, by regulating global and local inhibitory activity. While recognizing that this effect can also result from other mechanisms, neural inhibition helps the understanding of how network metastability is affected after disrupting local and global neural balance. In combination with relevant theories of consciousness, we suggest that anticorrelations are a physiological prior that can work as a marker of preserved consciousness. We predict that if the brain is not in a state to host anticorrelations, then most likely the individual does not entertain subjective experience. We believe that this link between anticorrelations and the underlying physiology will help not only to comprehend how consciousness happens, but also conceptualize effective interventions for treating consciousness disorders in which anticorrelations seem particularly affected.

PMID:38800457 | PMC:PMC11117090 | DOI:10.1162/netn_a_00244

Constraining functional coactivation with a cluster-based structural connectivity network

Mon, 05/27/2024 - 18:00

Netw Neurosci. 2022 Oct 1;6(4):1032-1065. doi: 10.1162/netn_a_00242. eCollection 2022.


In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints from the structural connectivity network. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest (ROIs), and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network. The coactivations of ROIs and their clusters can be studied by correlations between factors, which can largely differ by ongoing cognitive task. We provide a simulation study to validate that the pipeline can recover the underlying structural and functional network. We also apply the proposed pipeline to empirical data to explore the structural network of ROIs obtained by the Gordon parcellation and study their functional coactivations across eight cognitive tasks and a resting-state condition.

PMID:38800456 | PMC:PMC11117093 | DOI:10.1162/netn_a_00242

Hypothalamic effective connectivity at rest is associated with body weight and energy homeostasis

Mon, 05/27/2024 - 18:00

Netw Neurosci. 2022 Oct 1;6(4):1316-1333. doi: 10.1162/netn_a_00266. eCollection 2022.


Hunger and satiety drive eating behaviours via changes in brain function. The hypothalamus is a central component of the brain networks that regulate food intake. Animal research parsed the roles of the lateral hypothalamus (LH) and medial hypothalamus (MH) in hunger and satiety, respectively. Here, we examined how hunger and satiety change information flow between human LH and MH brain networks, and how these interactions are influenced by body mass index (BMI). Forty participants (16 overweight/obese) underwent two resting-state functional MRI scans while being fasted and sated. The excitatory/inhibitory influence of information flow between the MH and LH was modelled using spectral dynamic causal modelling. Our results revealed two core networks interacting across homeostatic state and weight: subcortical bidirectional connections between the LH, MH and the substantia nigra pars compacta (prSN), and cortical top-down inhibition from fronto-parietal and temporal areas. During fasting, we found higher inhibition between the LH and prSN, whereas the prSN received greater top-down inhibition from across the cortex. Individuals with higher BMI showed that these network dynamics occur irrespective of homeostatic state. Our findings reveal fasting affects brain dynamics over a distributed hypothalamic-midbrain-cortical network. This network is less sensitive to state-related fluctuations among people with obesity.

PMID:38800453 | PMC:PMC11117096 | DOI:10.1162/netn_a_00266

Functional connectivity alterations in the thalamus among patients with bronchial asthma

Mon, 05/27/2024 - 18:00

Front Neurol. 2024 May 10;15:1378362. doi: 10.3389/fneur.2024.1378362. eCollection 2024.


OBJECTIVE: Bronchial Asthma (BA) is a common chronic respiratory disease worldwide. Earlier research has demonstrated abnormal functional connectivity (FC) in multiple cognition-related cortices in asthma patients. The thalamus (Thal) serves as a relay center for transmitting sensory signals, yet the modifications in the thalamic FC among individuals with asthma remain uncertain. This research employed the resting-state functional connectivity (rsFC) approach to explore alterations in thalamic functional connectivity among individuals with BA.

PATIENTS AND METHODS: After excluding participants who did not meet the criteria, this study finally included 31 patients with BA, with a gender distribution of 16 males and 15 females. Subsequently, we recruited 31 healthy control participants (HC) matched for age, gender, and educational background. All participants underwent the Montreal Cognitive Assessment (MoCA) and the Hamilton Depression Rating Scale (HAMD) assessment. Following this, both groups underwent head magnetic resonance imaging scans, and resting-state functional magnetic resonance imaging (rs-fMRI) data was collected. Based on the AAL (Automated Anatomical Labeling) template, the bilateral thalamic regions were used as seed points (ROI) for subsequent rsFC research. Pearson correlation analysis was used to explore the relationship between thalamic functional connectivity and neuropsychological scales in both groups. After controlling for potential confounding factors such as age, gender, intelligence, and emotional level, a two-sample t-test was further used to explore differences in thalamic functional connectivity between the two groups of participants.

RESULT: Compared to the HC group, the BA group demonstrated heightened functional connectivity (FC) between the left thalamus and the left cerebellar posterior lobe (CPL), left postcentral gyrus (PCG), and right superior frontal gyrus (SFG). Concurrently, there was a decrease in FC with both the Lentiform Nucleus (LN) and the left corpus callosum (CC). Performing FC analysis with the right thalamus as the Region of Interest (ROI) revealed an increase in FC between the right thalamus and the right SFG as well as the left CPL. Conversely, a decrease in FC was observed between the right thalamus and the right LN as well as the left CC.

CONCLUSION: In our study, we have verified the presence of aberrant FC patterns in the thalamus of BA patients. When compared to HCs, BA patients exhibit aberrant alterations in FC between the thalamus and various brain areas connected to vision, hearing, emotional regulation, cognitive control, somatic sensations, and wakefulness. This provides further confirmation of the substantial role played by the thalamus in the advancement of BA.

PMID:38798710 | PMC:PMC11116975 | DOI:10.3389/fneur.2024.1378362

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults

Mon, 05/27/2024 - 18:00

bioRxiv [Preprint]. 2024 May 17:2024.05.17.594743. doi: 10.1101/2024.05.17.594743.


The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

PMID:38798606 | PMC:PMC11118564 | DOI:10.1101/2024.05.17.594743

Impact of deprivation and preferential usage on functional connectivity between early visual cortex and category selective visual regions

Mon, 05/27/2024 - 18:00

bioRxiv [Preprint]. 2024 May 17:2024.05.17.593020. doi: 10.1101/2024.05.17.593020.


Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how the removal of input changes brain function. However, an important question has yet to be answered: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. However, when central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with certain portions receiving "preferential" usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

HIGHLIGHTS: Portions of early visual cortex representing central vs. peripheral vision exhibit different patterns of connectivity to category-selective visual regions.When central vision is lost, cortical representations of peripheral vision display stronger functional connections to MT than central representations.When central vision is lost, connectivity to regions selective for tasks that involve central vision (FFA and PHA) are not significantly altered.These effects do not depend on which locations of peripheral vision are used more.

PMID:38798355 | PMC:PMC11118586 | DOI:10.1101/2024.05.17.593020

Aberrant Intra- and Inter-Network Connectivity in Idiopathic Sudden Sensorineural Hearing Loss with Tinnitus

Mon, 05/27/2024 - 18:00

Curr Med Imaging. 2024 May 24. doi: 10.2174/0115734056308400240517114616. Online ahead of print.


BACKGROUND: Idiopathic Sudden Sensorineural Hearing Loss (ISSNHL) is related to alterations in brain cortical and subcortical structures, and changes in brain functional activities involving multiple networks, which is often accompanied by tinnitus. There have been many in-depth research studies conducted concerning ISSNHL. Despite this, the neurophysiological mechanisms of ISSNHL with tinnitus are still under exploration.

OBJECTIVE: The study aimed to investigate the neural mechanism in ISSNHL patients with tinnitus based on the alterations in intra- and inter-network Functional Connectivity (FC) of multiple networks.

METHODS: Thirty ISSNHL subjects and 37 healthy subjects underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Independent Component Analysis (ICA) was used to identify 8 Resting-state Networks (RSNs). Furthermore, the study used a two-sample t-test to calculate the intra-network FC differences, while calculating Functional Network Connectivity (FNC) to detect the inter-network FC differences.

RESULTS: By using the ICA approach, tinnitus patients with ISSNHL were found to have FC changes in the following RSNs: CN, VN, DMN, ECN, SMN, and AUN. In addition, the interconnections of VN-SMN, VN-ECN, and ECN-DAN were weakened.

CONCLUSION: The present study has demonstrated changes in FC within and between networks in ISSNHL with tinnitus, providing ideas for further study on the neuropathological mechanism of the disease.

PMID:38798227 | DOI:10.2174/0115734056308400240517114616

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Sun, 05/26/2024 - 18:00

Neuroimage. 2024 May 25;295:120639. doi: 10.1016/j.neuroimage.2024.120639. Online ahead of print.


Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

PMID:38796977 | DOI:10.1016/j.neuroimage.2024.120639

BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis

Sun, 05/26/2024 - 18:00

Med Image Anal. 2024 May 22;96:103211. doi: 10.1016/ Online ahead of print.


In the medical field, datasets are mostly integrated across sites due to difficult data acquisition and insufficient data at a single site. The domain shift problem caused by the heterogeneous distribution among multi-site data makes autism spectrum disorder (ASD) hard to identify. Recently, domain adaptation has received considerable attention as a promising solution. However, domain adaptation on graph data like brain networks has not been fully studied. It faces two major challenges: (1) complex graph structure; and (2) multiple source domains. To overcome the issues, we propose an end-to-end structure-aware domain adaptation framework for brain network analysis (BrainDAS) using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed approach contains two stages: supervision-guided multi-site graph domain adaptation with dynamic kernel generation and graph classification with attention-based graph pooling. We evaluate our BrainDAS on a public dataset provided by Autism Brain Imaging Data Exchange (ABIDE) which includes 871 subjects from 17 different sites, surpassing state-of-the-art algorithms in several different evaluation settings. Furthermore, our promising results demonstrate the interpretability and generalization of the proposed method. Our code is available at

PMID:38796945 | DOI:10.1016/

Decreased gray matter volume in the anterior cerebellar of attention deficit/hyperactivity disorder comorbid oppositional defiant disorder children with associated cerebellar-cerebral hyperconnectivity: insights from a combined structural MRI and...

Sat, 05/25/2024 - 18:00

Int J Dev Neurosci. 2024 May 25. doi: 10.1002/jdn.10349. Online ahead of print.


Attention deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) are highly comorbid. Many prior investigations have found that ADHD relates to anatomical abnormalities in gray matter. The abnormal gray matter of ADHD comorbid ODD is still poorly understood. This study aimed to explore the effect of comorbid ODD on gray matter volume (GMV) and functional alterations in ADHD. All data were provided by the ADHD-200 Preprocessed Repository, including 27 ADHD-only children, 27 ADHD + ODD children, and 27 healthy controls aged 9-14 years. Voxel-based morphometry (VBM) and functional connectivity (FC) of resting-state functional magnetic resonance imaging (fMRI) were used to compare the difference in GMV and FC between ADHD + ODD, ADHD-only, and healthy children. The results showed that ADHD children with comorbid ODD had a more significant reduction in cerebellar volume, mainly in the anterior regions of the cerebellum (Cerebellum_4_5). The Cerebellum_4_5 showed increased functional connectivity with multiple cortical regions. These brain regions include numerous executive functioning (EF) and brain default mode network (DMN) nodes. The GMV abnormalities and excessive connectivity between brain regions may further exacerbate the emotional and cognitive deficits associated with ADHD.

PMID:38795021 | DOI:10.1002/jdn.10349

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Sat, 05/25/2024 - 18:00

Brain Sci. 2024 May 17;14(5):507. doi: 10.3390/brainsci14050507.


Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

PMID:38790485 | DOI:10.3390/brainsci14050507

Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning

Fri, 05/24/2024 - 18:00

J Neurooncol. 2024 May 24. doi: 10.1007/s11060-024-04715-1. Online ahead of print.


PURPOSE: High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.

METHODS: Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70).

RESULTS: The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor.

CONCLUSION: The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.

PMID:38789843 | DOI:10.1007/s11060-024-04715-1

Dynamical and individualised approach of transcranial ultrasound neuromodulation effects in non-human primates

Fri, 05/24/2024 - 18:00

Sci Rep. 2024 May 24;14(1):11916. doi: 10.1038/s41598-024-62562-6.


Low-frequency transcranial ultrasound stimulation (TUS) allows to alter brain functioning with a high spatial resolution and to reach deep targets. However, the time-course of TUS effects remains largely unknown. We applied TUS on three brain targets for three different monkeys: the anterior medial prefrontal cortex, the supplementary motor area and the perigenual anterior cingulate cortex. For each, one resting-state fMRI was acquired between 30 and 150 min after TUS as well as one without stimulation (control). We captured seed-based brain connectivity changes dynamically and on an individual basis. We also assessed between individuals and between targets homogeneity and brain features that predicted TUS changes. We found that TUS prompts heterogenous functional connectivity alterations yet retain certain consistent changes; we identified 6 time-courses of changes including transient and long duration alterations; with a notable degree of accuracy we found that brain alterations could partially be predicted. Altogether, our results highlight that TUS induces heterogeneous functional connectivity alterations. On a more technical point, we also emphasize the need to consider brain changes over-time rather than just observed during a snapshot; to consider inter-individual variability since changes could be highly different from one individual to another.

PMID:38789473 | DOI:10.1038/s41598-024-62562-6

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data

Fri, 05/24/2024 - 18:00

Biosensors (Basel). 2024 May 19;14(5):259. doi: 10.3390/bios14050259.


Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI-the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

PMID:38785733 | DOI:10.3390/bios14050259

Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery

Fri, 05/24/2024 - 18:00

J Multidiscip Healthc. 2024 May 18;17:2409-2424. doi: 10.2147/JMDH.S470809. eCollection 2024.


As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.

PMID:38784380 | PMC:PMC11111578 | DOI:10.2147/JMDH.S470809