Most recent paper

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Jun 5:S2451-9022(24)00141-1. doi: 10.1016/j.bpsc.2024.05.008. Online ahead of print.

ABSTRACT

BACKGROUND: Understanding how brain function and structure relate to one another, compared to conventional unimodal analysis, opens a new biologically-relevant assessment of neural mechanisms. However, how function-structure dependencies evolve throughout typical and abnormal neurodevelopment remains elusive. The 22q11.2 deletion syndrome (22q11.2DS) offers an important opportunity to study the development of function-structure dependencies and their specific association to the pathophysiology of psychosis.

METHODS: Previously, we used graph signal processing to combine brain activity and structural connectivity measures in adults, quantifying functional-structural dependency (FSD). Here, we combined FSD with longitudinal multivariate partial least squares correlation (PLS-C) to evaluate FSD alterations across groups and among patients with and without mild to moderate positive psychotic symptoms (PPS). We assessed 391 longitudinally repeated resting-state functional and diffusion-weighted magnetic resonance imaging from 194 healthy controls and 197 deletion carriers (age span 7-34, data collected over a span of 12 years) RESULTS: Relative to controls, patients with 22q11.2DS showed a persistent developmental offset from childhood, with regions of hyper- and hypo-coupling across the brain. Additionally, a second deviating developmental pattern showed an exacerbation during adolescence, presenting hypo-coupling in frontal and cingulate cortex and hyper-coupling in temporal regions for patients with 22q11.2DS. Interestingly, the observed aggravation during adolescence was strongly driven by the PPS+ group.

CONCLUSIONS: These results confirm a central role of altered FSD-maturation in the emergence of psychotic symptoms in 22q11.2DS during adolescence. The FSD deviations precede the onset of psychotic episodes and thus offer a potential early indication for behavioral interventions in individuals at risk.

PMID:38849032 | DOI:10.1016/j.bpsc.2024.05.008

Front Pharmacol. 2024 May 23;15:1387123. doi: 10.3389/fphar.2024.1387123. eCollection 2024.

ABSTRACT

Early initiation of antipsychotic treatment plays a crucial role in the management of first-episode schizophrenia (FES) patients, significantly improving their prognosis. However, limited attention has been given to the long-term effects of antipsychotic drug therapy on FES patients. In this research, we examined the changes in abnormal brain regions among FES patients undergoing long-term treatment using a dynamic perspective. A total of 98 participants were included in the data analysis, comprising 48 FES patients, 50 healthy controls, 22 patients completed a follow-up period of more than 6 months with qualified data. We processed resting-state fMRI data to calculate coefficient of variation of fractional amplitude of low-frequency fluctuations (CVfALFF), which reflects the brain regional activity stability. Data analysis was performed at baseline and after long-term treatment. We observed that compared with HCs, patients at baseline showed an elevated CVfALFF in the supramarginal gyrus (SMG), parahippocampal gyrus (PHG), caudate, orbital part of inferior frontal gyrus (IOG), insula, and inferior frontal gyrus (IFG). After long-term treatment, the instability in SMG, PHG, caudate, IOG, insula and inferior IFG have ameliorated. Additionally, there was a positive correlation between the decrease in dfALFF in the SMG and the reduction in the SANS total score following long-term treatment. In conclusion, FES patients exhibit unstable regional activity in widespread brain regions at baseline, which can be ameliorated with long-term treatment. Moreover, the extent of amelioration in SMG instability is associated with the amelioration of negative symptoms.

PMID:38846088 | PMC:PMC11153814 | DOI:10.3389/fphar.2024.1387123

World Neurosurg. 2024 Jun 4:S1878-8750(24)00950-1. doi: 10.1016/j.wneu.2024.06.003. Online ahead of print.

ABSTRACT

BACKGROUND: Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings.

METHODS: We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted.

RESULTS: We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions.

CONCLUSIONS: fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.

PMID:38843969 | DOI:10.1016/j.wneu.2024.06.003

Neuroimage Clin. 2024 Jun 2;43:103627. doi: 10.1016/j.nicl.2024.103627. Online ahead of print.

ABSTRACT

Neuroimaging studies on healthy subjects described the causal effective connectivity of cerebellar-cerebral social mentalizing networks, revealing the presence of closed-loops. These studies estimated effective connectivity by applying Dynamic Causal Modeling on task-related fMRI data of healthy subjects performing mentalizing tasks. Thus far, few studies have applied Dynamic Causal Modeling to resting-state fMRI (rsfMRI) data to test the effective connectivity within the cerebellar-cerebral mentalizing network in the absence of experimental manipulations, and no study applied Dynamic Causal Modeling on fMRI data of patients with cerebellar disorders typically showing social cognition deficits. Thus, in this research we applied spectral Dynamic Causal Modeling, to rsfMRI data of 13 patients affected by spinocerebellar ataxia type 2 (SCA2) and of 23 matched healthy subjects. Specifically, effective connectivity was tested between acknowledged mentalizing regions of interest: bilateral cerebellar Crus II, dorsal and ventral medial prefrontal cortex, bilateral temporo-parietal junctions and precuneus. SCA2 and healthy subjects shared some similarities in cerebellar-cerebral mentalizing effective connectivity at rest, confirming the presence of closed-loops between cerebellar and cerebral mentalizing regions in both groups. However, relative to healthy subjects, SCA2 patients showed effective connectivity variations mostly in cerebellar-cerebral closed loops, namely weakened inhibitory connectivity from the cerebellum to the cerebral cortex, but stronger inhibitory connectivity from the cerebral cortex to the cerebellum. The present study demonstrated that effective connectivity changes affect a function-specific mentalizing network in SCA2 patients, allowing to deepen the direction and strength of the causal effective connectivity mechanisms driven by the cerebellar damage associated with SCA2.

PMID:38843759 | DOI:10.1016/j.nicl.2024.103627

BMC Musculoskelet Disord. 2024 Jun 6;25(1):450. doi: 10.1186/s12891-024-07539-2.

ABSTRACT

OBJECTIVE: To investigate the brain mechanism of non-correspondence between imaging presentations and clinical symptoms in cervical spondylotic myelopathy (CSM) patients and to test the utility of brain imaging biomarkers for predicting prognosis of CSM.

METHODS: Forty patients with CSM (22 mild-moderate CSM, 18 severe CSM) and 25 healthy controls (HCs) were recruited for rs-fMRI and cervical spinal cord diffusion tensor imaging (DTI) scans. DTI at the spinal cord (level C2/3) with fractional anisotropy (FA) and degree centrality (DC) were recorded. Then one-way analysis of covariance (ANCOVA) was conducted to detect the group differences in the DC and FA values across the three groups. Pearson correlation analysis was then separately performed between JOA with FA and DC.

RESULTS: Among them, degree centrality value of left middle temporal gyrus exhibited a progressive increase in CSM groups compared with HCs, the DC value in severe CSM group was higher compared with mild-moderate CSM group. (P < 0.05), and the DC values of the right superior temporal gyrus and precuneus showed a decrease after increase. Among them, DC values in the area of precuneus in severe CSM group were significantly lower than those in mild-moderate CSM and HCs. (P < 0.05). The fractional anisotropy (FA) values of the level C2/3 showed a progressive decrease in different clinical stages, that severe CSM group was the lowest, significantly lower than those in mild-moderate CSM and HCs (P < 0.05). There was negative correlation between DC value of left middle temporal gyrus and JOA scores (P < 0.001), and the FA values of dorsal column in the level C2/3 positively correlated with the JOA scores (P < 0.001).

CONCLUSION: Structural and functional changes have taken place in the cervical spinal cord and brain of CSM patients. The Brain reorganization plays an important role in maintaining the symptoms and signs of CSM, aberrant DC values in the left middle temporal gyrus may be the possible mechanism of inconsistency between imaging findings and clinical symptoms. Degree centrality is a potentially useful prognostic functional biomarker in cervical spondylotic myelopathy.

PMID:38844898 | DOI:10.1186/s12891-024-07539-2

Brain. 2024 Jun 6:awae174. doi: 10.1093/brain/awae174. Online ahead of print.

ABSTRACT

4-repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R-tauopathies are progressive-supranuclear-palsy (PSP) and corticobasal-degeneration (CBD) characterized by subcortical tau accumulation and cortical neuronal dysfunction, as shown by PET-assessed hypoperfusion and glucose hypometabolism. Yet, there is a spatial mismatch between subcortical tau deposition patterns and cortical neuronal dysfunction, and it is unclear how these two pathological brain changes are interrelated. Here, we hypothesized that subcortical tau pathology induces remote neuronal dysfunction in functionally connected cortical regions to test a pathophysiological model that mechanistically links subcortical tau accumulation to cortical neuronal dysfunction in 4R tauopathies. We included 51 Aβ-negative patients with clinically diagnosed PSP variants (n=26) or Corticobasal Syndrome (CBS; n=25) who underwent structural MRI and 18F-PI-2620 tau-PET. 18F-PI-2620 tau-PET was recorded using a dynamic one-stop-shop acquisition protocol, to determine an early 0.5-2.5 min post-tracer-injection perfusion window for assessing cortical neuronal dysfunction, as well as a 20-40 min post-tracer-injection window to determine 4R-tau load. Perfusion-PET (i.e. early-window) was assessed in 200 cortical regions, and tau-PET was assessed in 32 subcortical regions of established functional brain atlasses. We determined tau epicenters as subcortical regions with highest 18F-PI-2620 tau-PET signal and assessed the connectivity of tau epicenters to cortical ROIs using a resting-state fMRI-based functional connectivity template derived from 69 healthy elderly controls from the ADNI cohort. Using linear regression, we assessed whether i) higher subcortical tau-PET was associated with reduced cortical perfusion and ii) whether cortical perfusion reductions were observed preferentially in regions closely connected to subcortical tau epicenters. As hypothesized, higher subcortical tau-PET was associated with overall lower cortical perfusion, which remained consistent when controlling for cortical tau-PET. Using group-average and subject-level PET data, we found that the seed-based connectivity pattern of subcortical tau epicenters aligned with cortical perfusion patterns, where cortical regions that were more closely connected to the tau epicenter showed lower perfusion. Together, subcortical tau-accumulation is associated with remote perfusion reductions indicative of neuronal dysfunction in functionally connected cortical regions in 4R-tauopathies. This suggests that subcortical tau pathology may induce cortical dysfunction, which may contribute to clinical disease manifestation and clinical heterogeneity.

PMID:38842726 | DOI:10.1093/brain/awae174

Brain Behav. 2024 Jun;14(6):e3550. doi: 10.1002/brb3.3550.

ABSTRACT

BACKGROUND: Cerebral specialization and interhemispheric cooperation are two vital features of the human brain. Their dysfunction may be associated with disease progression in patients with Alzheimer's disease (AD), which is featured as progressive cognitive degeneration and asymmetric neuropathology.

OBJECTIVE: This study aimed to examine and define two inherent properties of hemispheric function in patients with AD by utilizing resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: Sixty-four clinically diagnosed AD patients and 52 age- and sex-matched cognitively normal subjects were recruited and underwent MRI and clinical evaluation. We calculated and compared brain specialization (autonomy index, AI) and interhemispheric cooperation (connectivity between functionally homotopic voxels, CFH).

RESULTS: In comparison to healthy controls, patients with AD exhibited enhanced AI in the left middle occipital gyrus. This increase in specialization can be attributed to reduced functional connectivity in the contralateral region, such as the right temporal lobe. The CFH of the bilateral precuneus and prefrontal areas was significantly decreased in AD patients compared to controls. Imaging-cognitive correlation analysis indicated that the CFH of the right prefrontal cortex was marginally positively related to the Montreal Cognitive Assessment score in patients and the Auditory Verbal Learning Test score. Moreover, taking abnormal AI and CFH values as features, support vector machine-based classification achieved good accuracy, sensitivity, specificity, and area under the curve by leave-one-out cross-validation.

CONCLUSION: This study suggests that individuals with AD have abnormal cerebral specialization and interhemispheric cooperation. This provides new insights for further elucidation of the pathological mechanisms of AD.

PMID:38841739 | DOI:10.1002/brb3.3550

J Neurosci. 2024 Jun 5:e2322232024. doi: 10.1523/JNEUROSCI.2322-23.2024. Online ahead of print.

ABSTRACT

Complex auditory scenes pose a challenge to attentive listening, rendering listeners slower and more uncertain in their perceptual decisions. How can we explain such behaviors from the dynamics of cortical networks that pertain to the control of listening behavior? We here follow up on the hypothesis that human adaptive perception in challenging listening situations is supported by modular reconfiguration of auditory-control networks in a sample of N=40 participants (13 males) who underwent resting-state and task functional magnetic resonance imaging (fMRI). Individual titration of a spatial selective auditory attention task maintained an average accuracy of ∼70% but yielded considerable inter-individual differences in listeners' response speed and reported confidence in their own perceptual decisions. Whole-brain network modularity increased from rest to task by reconfiguring auditory, cinguloopercular, and dorsal attention networks. Specifically, interconnectivity between the auditory network and cinguloopercular network decreased during the task relative to the resting state. Additionally, interconnectivity between the dorsal attention network and cinguloopercular network increased. These interconnectivity dynamics were predictive of individual differences in response confidence, the degree of which was more pronounced after incorrect judgments. Our findings uncover the behavioral relevance of functional crosstalk between auditory and attentional-control networks during metacognitive assessment of one's own perception in challenging listening situations and suggest two functionally dissociable cortical networked systems that shape the considerable metacognitive differences between individuals in adaptive listening behavior.Significance Statement The ability to communicate in challenging listening situations varies not only objectively between individuals but also in terms of their subjective perceptual confidence. Using fMRI and a challenging auditory task, we demonstrate that this variability in the metacognitive aspect of listening behavior is reflected on a cortical level through the modular reconfiguration of brain networks. Importantly, task-related modulation of interconnectivity between the cinguolopercular network and each auditory and dorsal attention network can explain for individuals' differences in response confidence. This suggests two dissociable cortical networked systems that shape the individual evaluation of one's own perception during listening, promising new opportunities to better understand and intervene in deficits of auditory perception such as age-related hearing loss or auditory hallucinations.

PMID:38839303 | DOI:10.1523/JNEUROSCI.2322-23.2024

Asian J Psychiatr. 2024 May 28;98:104079. doi: 10.1016/j.ajp.2024.104079. Online ahead of print.

ABSTRACT

BACKGROUND: In order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment.

METHODS: A total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks' taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients.

RESULTS: This study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856.

CONCLUSION: The study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

PMID:38838458 | DOI:10.1016/j.ajp.2024.104079

Sci Rep. 2024 Jun 5;14(1):12957. doi: 10.1038/s41598-024-63765-7.

ABSTRACT

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice. The study included individuals experienced in meditation and/or yogic practices, referred to as 'meditators' (n = 30), and novice controls (n = 31). The GLM analysis, based on audio instructions, demonstrated activation related to auditory cues without concurrent default mode network (DMN) deactivation. DMN seed based functional connectivity (FC) analysis revealed significant reductions in connectivity among meditators during YN as compared to controls. We did not find differences between the two groups during the pre and post resting state scans. Moreover, when DMN-FC was compared between the YN state and resting state, meditators showed distinct decoupling, whereas controls showed increased DMN-FC. Finally, participants exhibit a remarkable correlation between reduced DMN connectivity during YN and self-reported hours of cumulative meditation and yoga practice. Together, these results suggest a unique neural modulation of the DMN in meditators during YN which results in being restful yet aware, aligned with their subjective experience of the practice. The study deepens our understanding of the neural mechanisms of YN, revealing distinct DMN connectivity decoupling in meditators and its relationship with meditation and yoga experience. These findings have interdisciplinary implications for neuroscience, psychology, and yogic disciplines.

PMID:38839877 | DOI:10.1038/s41598-024-63765-7

Eur J Neurosci. 2024 Jun 4. doi: 10.1111/ejn.16390. Online ahead of print.

ABSTRACT

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

PMID:38837814 | DOI:10.1111/ejn.16390

Neurosurgery. 2024 Jun 5. doi: 10.1227/neu.0000000000003012. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Precise mapping of functional networks in patients with brain tumor is essential for tailoring personalized treatment strategies. Resting-state functional MRI (rs-fMRI) offers an alternative to task-based fMRI, capable of capturing multiple networks within a single acquisition, without necessitating task engagement. This study demonstrates a strong concordance between preoperative rs-fMRI maps and the gold standard intraoperative direct electric stimulation (DES) mapping during awake surgery.

METHODS: We conducted an analysis involving 28 patients with glioma who underwent awake surgery with DES mapping. A total of 100 DES recordings were collected to map sensorimotor (SMN), language (LANG), visual (VIS), and speech articulation cognitive domains. Preoperative rs-fMRI maps were generated using an updated version of the ReStNeuMap software, specifically designed for rs-fMRI data preprocessing and automatic detection of 7 resting-state networks (SMN, LANG, VIS, speech articulation, default mode, frontoparietal, and visuospatial). To evaluate the agreement between these networks and those mapped with invasive cortical mapping, we computed patient-specific distances between them and intraoperative DES recordings.

RESULTS: Automatically detected preoperative functional networks exhibited excellent agreement with intraoperative DES recordings. When we spatially compared DES points with their corresponding networks, we found that SMN, VIS, and speech articulatory DES points fell within the corresponding network (median distance = 0 mm), whereas for LANG a median distance of 1.6 mm was reported.

CONCLUSION: Our findings show the remarkable consistency between key functional networks mapped noninvasively using presurgical rs-fMRI and invasive cortical mapping. This evidence highlights the utility of rs-fMRI for personalized presurgical planning, particularly in scenarios where awake surgery with DES is not feasible to protect eloquent areas during tumor resection. We have made the updated tool for automated functional network estimation publicly available, facilitating broader utilization of rs-fMRI mapping in various clinical contexts, including presurgical planning, functional reorganization over follow-up periods, and informing future treatments such as radiotherapy.

PMID:38836617 | DOI:10.1227/neu.0000000000003012

Front Hum Neurosci. 2024 May 21;18:1339324. doi: 10.3389/fnhum.2024.1339324. eCollection 2024.

ABSTRACT

BACKGROUND: Normative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state-network-informed evaluation in pediatric cerebral palsy.

METHODS: Cross-spectral dynamic causal modeling of resting-state-fMRI was investigated in 50 neurotypically developing 5- to 13-year-old children. Fully connected six-node network models per hemisphere included primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging informed the model; Purdue Pegboard Test scores of hand motor behavior were the covariate at the group level to determine the effective-connectivity-functional behavior relationship.

RESULTS: Although both hemispheres exhibited similar effective connectivity of motor cortico-basal ganglia-cerebellar networks, magnitudes were slightly greater on the right, except for left-sided connections of the striatum which were more numerous and of opposite polarity. Inter-nodal motor network effective connectivity remained consistent and robust across subjects. Age had a greater impact on connections to the contralateral cerebellum, bilaterally. Motor behavior, however, affected different connections in each hemisphere, exerting a more prominent effect on the left modulatory connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus.

DISCUSSION: This study revealed a consistent pattern of directed resting-state effective connectivity in healthy children aged 5-13 years within the motor network, encompassing cortical, subcortical, and cerebellar regions, correlated with motor skill proficiency. Both hemispheres exhibited similar effective connectivity within motor cortico-basal ganglia-cerebellar networks reflecting inter-nodal signal direction predicted by other modalities, mainly differing from task-dependent studies due to network differences at rest. Notably, age-related changes were more pronounced in connections to the contralateral cerebellum. Conversely, motor behavior distinctly impacted connections in each hemisphere, emphasizing its role in modulating left sided connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Motor network effective connectivity was correlated with motor behavior, validating its physiological significance. This study is the first to evaluate a normative effective connectivity model for the pediatric motor network using resting-state functional MRI correlating with behavior and serves as a foundation for identifying abnormal findings and optimizing targeted interventions like deep brain stimulation, potentially influencing future therapeutic approaches for children with movement disorders.

PMID:38835646 | PMC:PMC11148452 | DOI:10.3389/fnhum.2024.1339324

Neuroimage Clin. 2024 May 31;43:103625. doi: 10.1016/j.nicl.2024.103625. Online ahead of print.

ABSTRACT

BACKGROUND: Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored.

METHODS: This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months.

RESULTS: The results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome.

CONCLUSION: This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.

PMID:38833899 | DOI:10.1016/j.nicl.2024.103625

Nat Commun. 2024 Jun 4;15(1):4745. doi: 10.1038/s41467-024-48781-5.

ABSTRACT

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

PMID:38834553 | DOI:10.1038/s41467-024-48781-5

Front Psychiatry. 2024 May 20;15:1397093. doi: 10.3389/fpsyt.2024.1397093. eCollection 2024.

ABSTRACT

BACKGROUND: Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).

METHODS: One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.

RESULTS: We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2.

CONCLUSION: This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

PMID:38832332 | PMC:PMC11145064 | DOI:10.3389/fpsyt.2024.1397093

Front Psychiatry. 2024 May 20;15:1293514. doi: 10.3389/fpsyt.2024.1293514. eCollection 2024.

ABSTRACT

Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p<0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain's resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC's connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges.

PMID:38832325 | PMC:PMC11145276 | DOI:10.3389/fpsyt.2024.1293514

Neuroimage Clin. 2024 May 11;43:103618. doi: 10.1016/j.nicl.2024.103618. Online ahead of print.

ABSTRACT

Extensive neuroimaging abnormalities in subcortical regions build the pathophysiological basis of Wilson's disease (WD). Yet, subcortical topographic organization fails to articulate, leaving a huge gap in understanding the neural mechanism of WD. Thus, how functional abnormalities of WD subcortical regions influence complex clinical symptoms and response to treatment remain unknown. Using resting-state functional MRI data from 232 participants (including 130 WD patients and 102 healthy controls), we applied a connectivity-based parcellation technique to develop a subcortical atlas for WD. The atlas was further used to investigate abnormalities in subcortical function (ASF) by exploring intrasubcortical functional connectivity (FC) and topographic organization of cortico-subcortical FC. We further used support vector machine (SVM) to integrate these functional abnormalities into the ASF score, which serves as a biomarker for characterizing individual subcortical dysfunction for WD. Finally, the baseline ASF score and one-year treatment data of the follow-up WD patients were used to assess treatment response. A group set of subcortical parcellations was evaluated, in which 26 bilateral regions well recapitulated the anatomical nuclei of the subcortical areas of WD. The results of cortico-subcortical FC and intrasubcortical FC reveal that dysfunction of the somatomotor networks-lenticular nucleus-thalamic pathways is involved in complex symptoms of WD. The ASF score was able to characterize disease progression and was significantly associated with treatment response of WD. Our findings provide a comprehensive elaboration of functional abnormalities of WD subcortical regions and reveal their association with clinical presentations, improving our understanding of the functional neural underpinnings in WD. Furthermore, abnormalities in subcortical function could serve as a potential biomarker for understanding the disease progression and evaluating treatment response of WD.

PMID:38830274 | DOI:10.1016/j.nicl.2024.103618

Neuroreport. 2024 Jun 3. doi: 10.1097/WNR.0000000000002056. Online ahead of print.

ABSTRACT

Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO patients, neglecting the assessment of temporal variations in local brain activity. This study aimed to characterize alterations in dynamic regional homogeneity (dReHo) in TAO patients and differentiate between TAO patients and healthy controls using support vector machine (SVM) classification. Thirty-two patients with TAO and 32 healthy controls underwent resting-state fMRI scans. We calculated dReHo using sliding-window methods to evaluate changes in regional brain activity and compared these findings between the two groups. Subsequently, we employed SVM, a machine learning algorithm, to investigate the potential use of dReHo maps as diagnostic markers for TAO. Compared to healthy controls, individuals with active TAO demonstrated significantly higher dReHo values in the right angular gyrus, left precuneus, right inferior parietal as well as the left superior parietal gyrus. The SVM model demonstrated an accuracy ranging from 65.62 to 68.75% in distinguishing between TAO patients and healthy controls based on dReHo variability in these identified brain regions, with an area under the curve of 0.70 to 0.76. TAO patients showed increased dReHo in default mode network-related brain regions. The accuracy of classifying TAO patients and healthy controls based on dReHo was notably high. These results offer new insights for investigating the pathogenesis and clinical diagnostic classification of individuals with TAO.

PMID:38829952 | DOI:10.1097/WNR.0000000000002056

J Pain Res. 2024 May 27;17:1941-1951. doi: 10.2147/JPR.S457575. eCollection 2024.

ABSTRACT

PURPOSE: This study was conducted to explore the differences in functional changes in the pain matrix in patients with chronic back pain (CBP) at different stages and identify whether these brain changes were related to the pain duration.

PATIENTS AND METHODS: In this study, 29 healthy individuals and 54 patients with CBP were recruited. According to the pain duration, 25 patients (3 to 12 months) were divided into the CBP-S group and 29 patients (≥ 24 months) were divided into the CBP-L group. All subjects completed clinical pain-related measurement and functional magnetic resonance imaging (fMRI) scans. Moreover, the amplitude of low-frequency fluctuation (ALFF), functional connectivity (FC), and correlation analysis were conducted in this study.

RESULTS: Compared with healthy controls, patients in the CBP-L group showed significantly decreased ALFF in the left precuneus. In the FC analysis, patients in the CBP-S and CBP-L groups showed significantly decreased FC in several regions in the bilateral orbitofrontal cortices (OFC) and the left ventral posterior insula. Moreover, there were significant differences in the FC between the left hyper granular insula and the probabilistic area in OFC in pairwise group comparisons. The correlation analysis results demonstrated that pain duration was correlated with these functional brain changes, and the ANCOVA results revealed that pain intensity and pain interference scores did not affect the FC analysis results.

CONCLUSION: There are different changes in the pain neural matrix in patients with chronic pain at different stages. Furthermore, the pain duration is related to brain functional changes.

PMID:38828086 | PMC:PMC11141710 | DOI:10.2147/JPR.S457575