Most recent paper

A coordinate-based meta-analysis of human amygdala connectivity alterations related to early life adversities

Mon, 10/02/2023 - 18:00

Sci Rep. 2023 Oct 2;13(1):16541. doi: 10.1038/s41598-023-43057-2.

ABSTRACT

By affecting core neurobiological systems early in development, early life adversities (ELAs) might confer latent vulnerability to future psychopathologies. This coordinate-based meta-analysis aims to identify significant convergent alterations in functional connectivity of the amygdala related to ELAs across resting-state and task-based fMRI-studies. Five electronic databases were systematically searched until 22 October 2020, retrieving 49 eligible studies (n = 3162 participants). Convergent alterations in functional connectivity related to ELAs between the amygdala and the anterior cingulate cortex (ACC) and left hippocampus were found. Sub-analyses based on hemisphere and direction showed that connectivity seeded in the right amygdala was affected and, moreover, revealed that connectivity with ACC was decreased. Analyses based on paradigm and age showed that amygdala-ACC coupling was altered during resting state and that amygdala-left hippocampus connectivity was mostly affected during task-based paradigms and in adult participants. While both regions showed altered connectivity during emotion processing and following adverse social postnatal experiences such as maltreatment, amygdala-ACC coupling was mainly affected when ELAs were retrospectively assessed through self-report. We show that ELAs are associated with altered functional connectivity of the amygdala with the ACC and hippocampus. As such, ELAs may embed latent vulnerability to future psychopathologies by systematically affecting important neurocognitive systems.

PMID:37783710 | DOI:10.1038/s41598-023-43057-2

The neural substrates of how model-based learning affects risk taking: Functional coupling between right cerebellum and left caudate

Mon, 10/02/2023 - 18:00

Brain Cogn. 2023 Sep 30;172:106088. doi: 10.1016/j.bandc.2023.106088. Online ahead of print.

ABSTRACT

Higher executive control capacity allows people to appropriately evaluate risk and avoid both excessive risk aversion and excessive risk-taking. The neural mechanisms underlying this relationship between executive function and risk taking are still unknown. We used voxel-based morphometry (VBM) analysis combined with resting-state functional connectivity (rs-FC) to evaluate how one component of executive function, model-based learning, relates to risk taking. We measured individuals' use of the model-based learning system with the two-step task, and risk taking with the Balloon Analogue Risk Task. Behavioral results indicated that risk taking was positively correlated with the model-based weighting parameter ω. The VBM results showed a positive association between model-based learning and gray matter volume in the right cerebellum (RCere) and left inferior parietal lobule (LIPL). Functional connectivity results suggested that the coupling between RCere and the left caudate (LCAU) was correlated with both model-based learning and risk taking. Mediation analysis indicated that RCere-LCAU functional connectivity completely mediated the effect of model-based learning on risk taking. These results indicate that learners who favor model-based strategies also engage in more appropriate risky behaviors through interactions between reward-based learning, error-based learning and executive control subserved by a caudate, cerebellar and parietal network.

PMID:37783018 | DOI:10.1016/j.bandc.2023.106088

Predicting executive functioning from brain networks: modality specificity and age effects

Mon, 10/02/2023 - 18:00

Cereb Cortex. 2023 Sep 29:bhad338. doi: 10.1093/cercor/bhad338. Online ahead of print.

ABSTRACT

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

PMID:37782935 | DOI:10.1093/cercor/bhad338

High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle

Mon, 10/02/2023 - 18:00

Netw Neurosci. 2023 Oct 1;7(3):1181-1205. doi: 10.1162/netn_a_00307. eCollection 2023.

ABSTRACT

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and brain connectivity. However, how hormonal fluctuations impact fast changes in brain network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations between the activity of pairs of brain regions at a framewise timescale. In previous studies we showed that time points corresponding to high-amplitude co-fluctuations disproportionately contributed to the time-averaged functional connectivity pattern and that these co-fluctuation patterns could be clustered into a low-dimensional set of recurring "states." Here, we assessed the relationship between these network states and quotidian variation in hormone concentrations. Specifically, we were interested in whether the frequency with which network states occurred was related to hormone concentration. We addressed this question using a dense-sampling dataset (N = 1 brain). In this dataset, a single individual was sampled over the course of two endocrine states: a natural menstrual cycle and while the subject underwent selective progesterone suppression via oral hormonal contraceptives. During each cycle, the subject underwent 30 daily resting-state fMRI scans and blood draws. Our analysis of the imaging data revealed two repeating network states. We found that the frequency with which state 1 occurred in scan sessions was significantly correlated with follicle-stimulating and luteinizing hormone concentrations. We also constructed representative networks for each scan session using only "event frames"-those time points when an event was determined to have occurred. We found that the weights of specific subsets of functional connections were robustly correlated with fluctuations in the concentration of not only luteinizing and follicle-stimulating hormones, but also progesterone and estradiol.

PMID:37781152 | PMC:PMC10473261 | DOI:10.1162/netn_a_00307

Controversies and progress on standardization of large-scale brain network nomenclature

Mon, 10/02/2023 - 18:00

Netw Neurosci. 2023 Oct 1;7(3):864-905. doi: 10.1162/netn_a_00323. eCollection 2023.

ABSTRACT

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

PMID:37781138 | PMC:PMC10473266 | DOI:10.1162/netn_a_00323

Gender and cytoarchitecture differences: Functional connectivity of the hippocampal sub-regions

Mon, 10/02/2023 - 18:00

Heliyon. 2023 Sep 21;9(10):e20389. doi: 10.1016/j.heliyon.2023.e20389. eCollection 2023 Oct.

ABSTRACT

INTRODUCTION: The hippocampus plays a significant role in learning, memory encoding, and spatial navigation. Typically, the hippocampus is investigated as a whole region of interest. However, recent work has developed fully detailed atlases based on cytoarchitecture properties of brain regions, and the hippocampus has been sub-divided into seven sub-areas that have structural differences in terms of distinct numbers of cells, neurons, and other structural and chemical properties. Moreover, gender differences are of increasing concern in neuroscience research. Several neuroscience studies have found structural and functional variations between the brain regions of females and males, and the hippocampus is one of these regions.

AIM: The aim of this study to explore whether the cytoarchitecturally distinct sub-regions of the hippocampus have varying patterns of functional connectivity with different networks of the brain and how these functional connections differ in terms of gender differences.

METHOD: This study investigated 200 healthy participants using seed-based resting-state functional magnetic resonance imaging (rsfMRI). The primary aim of this study was to explore the resting connectivity and gender distinctions associated with specific sub-regions of the hippocampus and their relationship with major functional brain networks.

RESULTS: The findings revealed that the majority of the seven hippocampal sub-regions displayed functional connections with key brain networks, and distinct patterns of functional connectivity were observed between the hippocampal sub-regions and various functional networks within the brain. Notably, the default and visual networks exhibited the most consistent functional connections. Additionally, gender-based analysis highlighted evident functional resemblances and disparities, particularly concerning the anterior section of the hippocampus.

CONCLUSION: This study highlighted the functional connectivity patterns and involvement of the hippocampal sub-regions in major brain functional networks, indicating that the hippocampus should be investigated as a region of multiple distinct functions and should always be examined as sub-regions of interest. The results also revealed clear gender differences in functional connectivity.

PMID:37780771 | PMC:PMC10539667 | DOI:10.1016/j.heliyon.2023.e20389

Multi-view graph network learning framework for identification of major depressive disorder

Sat, 09/30/2023 - 18:00

Comput Biol Med. 2023 Sep 25;166:107478. doi: 10.1016/j.compbiomed.2023.107478. Online ahead of print.

ABSTRACT

Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures' high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.

PMID:37776730 | DOI:10.1016/j.compbiomed.2023.107478

Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis

Fri, 09/29/2023 - 18:00

Brain Res. 2023 Sep 27:148605. doi: 10.1016/j.brainres.2023.148605. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermodule effects of brain functional networks.

METHODS: Resting-state functional MRI(rs-fMRI) imaging data was collected from 30 ANMDARE and 30 healthy controls (HCs). A brain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity (FC) within and between modules was calculated, and whole-brain functional topology was analyzed. Finally, the association of brain functional with cognitive function in ANMDARE was further analyzed.

RESULTS: Compared to HCs, ANMDARE had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale.

CONCLUSIONS: Patients with ANMDARE are manifested by enhanced intramodular FC and intermodular connectivity changes in the brain. This may help to understand the pathophysiological mechanisms of the disease from a global perspective.

PMID:37775074 | DOI:10.1016/j.brainres.2023.148605

Network biomarkers in recovered psychosis patients who discontinued antipsychotics

Fri, 09/29/2023 - 18:00

Mol Psychiatry. 2023 Sep 29. doi: 10.1038/s41380-023-02279-6. Online ahead of print.

ABSTRACT

There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.

PMID:37773447 | DOI:10.1038/s41380-023-02279-6

Abnormal functional connectivity in radiologically isolated syndrome: A resting-state fMRI study

Fri, 09/29/2023 - 18:00

Mult Scler. 2023 Sep 29:13524585231195851. doi: 10.1177/13524585231195851. Online ahead of print.

ABSTRACT

BACKGROUND: Radiologically isolated syndrome (RIS) patients might have psychiatric and cognitive deficits, which suggests an involvement of major resting-state functional networks. Notwithstanding, very little is known about the neural networks involved in RIS.

OBJECTIVE: To examine functional connectivity differences between RIS and healthy controls using resting-state functional magnetic resonance imaging (fMRI).

METHODS: Resting-state fMRI data in 25 RIS patients and 28 healthy controls were analyzed using an independent component analysis; in addition, seed-based correlation analysis was used to obtain more information about specific differences in the functional connectivity of resting-state networks. Participants also underwent neuropsychological testing.

RESULTS: RIS patients did not differ from the healthy controls regarding age, sex, and years of education. However, in memory (verbal and visuospatial) and executive functions, RIS patients' cognitive performance was significantly worse than the healthy controls. In addition, fluid intelligence was also affected. Twelve out of 25 (48%) RIS patients failed at least one cognitive test, and six (24.0%) had cognitive impairment. Compared to healthy controls, RIS patients showed higher functional connectivity between the default mode network and the right middle and superior frontal gyri and between the central executive network and the right thalamus (pFDR < 0.05; corrected). In addition, the seed-based correlation analysis revealed that RIS patients presented higher functional connectivity between the posterior cingulate cortex, an important hub in neural networks, and the right precuneus.

CONCLUSION: RIS patients had abnormal brain connectivity in major resting-state neural networks and worse performance in neurocognitive tests. This entity should be considered not an "incidental finding" but an exclusively non-motor (neurocognitive) variant of multiple sclerosis.

PMID:37772510 | DOI:10.1177/13524585231195851

The unity and diversity of verbal and visuospatial creativity: Dynamic changes in hemispheric lateralisation

Fri, 09/29/2023 - 18:00

Hum Brain Mapp. 2023 Sep 29. doi: 10.1002/hbm.26494. Online ahead of print.

ABSTRACT

The investigation of similarities and differences in the mechanisms of verbal and visuospatial creative thinking has long been a controversial topic. Prior studies found that visuospatial creativity was primarily supported by the right hemisphere, whereas verbal creativity relied on the interaction between both hemispheres. However, creative thinking also involves abundant dynamic features that may have been ignored in the previous static view. Recently, a new method has been developed that measures hemispheric laterality from a dynamic perspective, providing new insight into the exploration of creative thinking. In the present study, dynamic lateralisation index was calculated with resting-state fMRI data. We combined the dynamic lateralisation index with sparse canonical correlation analysis to examine similarities and differences in the mechanisms of verbal and visuospatial creativity. Our results showed that the laterality reversal of the default mode network, fronto-parietal network, cingulo-opercular network and visual network contributed significantly to both verbal and visuospatial creativity and consequently could be considered the common neural mechanisms shared by these creative modes. In addition, we found that verbal creativity relied more on the language network, while visuospatial creativity relied more on the somatomotor network, which can be considered a difference in their mechanism. Collectively, these findings indicated that verbal and visuospatial creativity may have similar mechanisms to support the basic creative thinking process and different mechanisms to adapt to the specific task conditions. These findings may have significant implications for our understanding of the neural mechanisms of different types of creative thinking.

PMID:37772359 | DOI:10.1002/hbm.26494

Graph signal smoothness based feature learning of brain functional networks in schizophrenia

Thu, 09/28/2023 - 18:00

IEEE Trans Neural Syst Rehabil Eng. 2023 Sep 28;PP. doi: 10.1109/TNSRE.2023.3320135. Online ahead of print.

ABSTRACT

In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a subject-level brain functional network that describes the similarity between subjects from a graph signal processing (GSP) perspective. Based on the subject graph, we introduce the concept of graph signal smoothness to analyze the abnormal brain regions (feature brain regions) in which schizophrenic patients produce abnormal functional connections and to quantitatively rank the degree of abnormality of brain regions. We find that in the patients' brain networks, many new connections appear and some common connections are strengthened. The feature brain regions can be easily found according to the value of connection differences. Finally, we validate the learned feature brain regions by the results of two types of statistical analyses (ROI-to-ROI analysis and seed-to-voxel analysis), and the feature brain regions derived from graph signal smoothness are indeed the brain regions with significant differences in the statistical analysis, which illustrates the potential of graph signal smoothness for use in quantitative analysis of brain networks.

PMID:37768796 | DOI:10.1109/TNSRE.2023.3320135

Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis

Thu, 09/28/2023 - 18:00

Top Magn Reson Imaging. 2023 Sep 28. doi: 10.1097/RMR.0000000000000307. Online ahead of print.

ABSTRACT

OBJECTIVES: Locally low-rank (LLR) denoising of functional magnetic resonance imaging (fMRI) time series image data is extended to multi-echo (ME) data. The proposed method extends the capabilities of non-physiologic noise suppression beyond single-echo applications with a dedicated ME algorithm.

MATERIALS AND METHODS: Following an institutional review board (IRB) approved protocol, resting-state fMRI data were acquired in 7 healthy subjects. A compact 3T scanner enabled whole-brain acquisition of multiband ME fMRI data at high spatial resolution (1.4 × 1.4 × 2.8 mm3) with a 1810 ms repetition time (TR). Image data were denoised with ME-LLR preceding functional processing. The results of connectivity maps generated from denoised data were compared with maps generated with equivalent processing of non-denoised images. To assess ME-LLR as a method to reduce scan time, comparisons were made between maps computed from image data with full and retrospectively truncated durations. Assessments were completed with seed-based connectivity analyses using echo-combined image data. In a feasibility assessment, nondenoised and denoised full-duration echo-combined data were equivalently processed with independent component analysis (ICA) and compared.

RESULTS: ME-LLR denoising yielded strengthened resting-state network connectivity maps after nuisance regression and seed-based connectivity analysis. In assessing ME-LLR as a scan reduction mechanism, maps generated from denoised data at half scan time showed comparable quality with maps generated from full-duration, non-denoised data, at both single subject and group levels. ME-LLR substantially increased temporal signal-to-noise ratio (tSNR) for image data respective to each individual echo and for image data after nuisance regression. Among echo-specific image volumes, increases in tSNR yielded by ME-LLR were most pronounced for image data with the longest echo time and thereby lowest SNR. ICA showed resting-state networks consistently identified between non-denoised and denoised data, with clearer demarcation of networks for ME-LLR.

CONCLUSIONS: ME-LLR is demonstrated to suppress non-physiologic noise, enhance functional connectivity map quality, and could potentially facilitate scan time reduction in ME-fMRI.

PMID:37768305 | DOI:10.1097/RMR.0000000000000307

Identification of brain functional connectivity during acute transcutaneous tibial nerve stimulation: A 3T fMRI study

Thu, 09/28/2023 - 18:00

Neurourol Urodyn. 2023 Sep 28. doi: 10.1002/nau.25293. Online ahead of print.

ABSTRACT

OBJECTIVES: A feasibility proof-of-concept study was conducted to assess the effects of acute tibial nerve stimulation (TNS) on the central nervous system in healthy volunteers using functional magnetic resonance imaging (fMRI).

MATERIALS AND METHODS: Fourteen healthy volunteers were included in a prospective, single-site study conducted on a clinical 3T MRI scanner. Four scans of functional MRI, each lasting 6 min, were acquired: two resting-state fMRI scans (prior and following the TNS intervention) and in-between two fMRI scans, both consisting of alternating rest periods and noninvasive acute transcutaneous TNS (TTNS). Whole brain seed-based functional connectivity (FC) correlation analysis was performed comparing TTNS stimulation with rest periods. Cluster-level familywise error (FWE) corrected p and a minimal cluster size of 200 voxels were used to explore FC patterns.

RESULTS: Increased FC is reported between inferior frontal gyrus, posterior cingulate gyrus, and middle temporal gyrus with the precuneus as central receiving node. In addition, decreased FC in the cerebellum, hippocampus, and parahippocampal areas was observed.

CONCLUSIONS: Altered FC is reported in areas which have been described to be also involved in lower urinary tract control. Although conducted with healthy controls, the assumption that the underlying therapeutic effect of TNS involves the central nervous system is supported and has to be further examined in patients with incomplete spinal cord injury.

PMID:37767637 | DOI:10.1002/nau.25293

Neuroimaging markers of Alice in Wonderland syndrome in patients with migraine with aura

Thu, 09/28/2023 - 18:00

Front Neurol. 2023 Aug 24;14:1210811. doi: 10.3389/fneur.2023.1210811. eCollection 2023.

ABSTRACT

BACKGROUND: The Alice in Wonderland syndrome (AIWS) is a transient neurological disturbance characterized by sensory distortions most frequently associated with migraine in adults. Some lines of evidence suggest that AIWS and migraine might share common pathophysiological mechanisms, therefore we set out to investigate the common and distinct neurophysiological alterations associated with these conditions in migraineurs.

METHODS: We conducted a case-control study acquiring resting-state fMRI data from 12 migraine patients with AIWS, 12 patients with migraine with typical aura (MA) and 24 age-matched healthy controls (HC). We then compared the interictal thalamic seed-to-voxel and ROI-to-ROI cortico-cortical resting-state functional connectivity between the 3 groups.

RESULTS: We found a common pattern of altered thalamic connectivity in MA and AIWS, compared to HC, with more profound and diffuse alterations observed in AIWS. The ROI-to-ROI functional connectivity analysis highlighted an increased connectivity between a lateral occipital region corresponding to area V3 and the posterior part of the superior temporal sulcus (STS) in AIWS, compared to both MA and HC.

CONCLUSION: The posterior STS is a multisensory integration area, while area V3 is considered the starting point of the cortical spreading depression (CSD), the neural correlate of migraine aura. This interictal hyperconnectivity might increase the probability of the CSD to directly diffuse to the posterior STS or deactivating it, causing the AIWS symptoms during the ictal phase. Taken together, these results suggest that AIWS in migraineurs might be a form of complex migraine aura, characterized by the involvement of associative and multisensory integration areas.

PMID:37767534 | PMC:PMC10520557 | DOI:10.3389/fneur.2023.1210811

Broadly applicable methods for the detection of artefacts in electroencephalography acquired simultaneously with hemodynamic recordings

Thu, 09/28/2023 - 18:00

MethodsX. 2023 Sep 14;11:102376. doi: 10.1016/j.mex.2023.102376. eCollection 2023 Dec.

ABSTRACT

Electroencephalography (EEG) data, acquired simultaneously with magnetic resonance imaging (MRI), must be corrected for artefacts related to MR gradient switches (GS) and the cardioballistic (CB) effect. Canonical approaches require additional signal acquisition for artefact detection (e.g., MR volume onsets, ECG), without which the EEG data would be rendered uncleanable from these artefacts.•We present two broadly applicable methods for artefact detection based on peak detection combined with temporal constraints with respect to periodicity directly from the EEG data itself; no additional signals are required. We validated the performance of our methods versus the two canonical approaches for detection of GS/CB artefact, respectively, on 26 healthy human EEG-functional MRI resting-state datasets. Utilising various performance metrics, we found our methods to perform as well as - and sometimes better than - the canonical standard approaches. With as little as one EEG channel recording, our methods can be applied to detect GS/CB artefacts in EEG data acquired simultaneously with MRI in the absence of MR volume onsets and/or an ECG recording. The detected artefact onsets can then be fed into the standard artefact correction software.

PMID:37767154 | PMC:PMC10520509 | DOI:10.1016/j.mex.2023.102376

Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set

Thu, 09/28/2023 - 18:00

Front Hum Neurosci. 2023 Sep 12;17:1082722. doi: 10.3389/fnhum.2023.1082722. eCollection 2023.

ABSTRACT

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information.

METHOD: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation.

RESULT: The data-driven method-ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds.

CONCLUSION: This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.

PMID:37767136 | PMC:PMC10520784 | DOI:10.3389/fnhum.2023.1082722

Abnormal voxel-mirrored homotopic connectivity in first-episode major depressive disorder using fMRI: a machine learning approach

Thu, 09/28/2023 - 18:00

Front Psychiatry. 2023 Sep 12;14:1241670. doi: 10.3389/fpsyt.2023.1241670. eCollection 2023.

ABSTRACT

OBJECTIVE: To explore the interhemispheric information synergy ability of the brain in major depressive disorder (MDD) patients by applying the voxel-mirrored homotopic connectivity (VMHC) method and further explore the potential clinical diagnostic value of VMHC metric by a machine learning approach.

METHODS: 52 healthy controls and 48 first-episode MDD patients were recruited in the study. We performed neuropsychological tests and resting-state fMRI scanning on all subjects. The VMHC values of the symmetrical interhemispheric voxels in the whole brain were calculated. The VMHC alterations were compared between two groups, and the relationship between VMHC values and clinical variables was analyzed. Then, abnormal brain regions were selected as features to conduct the classification model by using the support vector machine (SVM) approach.

RESULTS: Compared to the healthy controls, MDD patients exhibited decreased VMHC values in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus. Furthermore, the VMHC value of the bilateral fusiform gyrus was positively correlated with the total Hamilton Depression Scale (HAMD). Moreover, SVM analysis displayed that a combination of all clusters demonstrated the highest area under the curve (AUC) of 0.87 with accuracy, sensitivity, and specificity values of 86.17%, 76.74%, and 94.12%, respectively.

CONCLUSION: MDD patients had reduced functional connectivity in the bilateral middle frontal gyrus, fusiform gyrus, medial superior frontal gyrus and precentral gyrus, which may be related to depressive symptoms. The abnormality in these brain regions could represent potential imaging markers to distinguish MDD patients from healthy controls.

PMID:37766927 | PMC:PMC10520785 | DOI:10.3389/fpsyt.2023.1241670

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches

Thu, 09/28/2023 - 18:00

Diagnostics (Basel). 2023 Sep 7;13(18):2871. doi: 10.3390/diagnostics13182871.

ABSTRACT

This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.

PMID:37761238 | DOI:10.3390/diagnostics13182871

Detection and Mitigation of Neurovascular Uncoupling in Brain Gliomas

Thu, 09/28/2023 - 18:00

Cancers (Basel). 2023 Sep 8;15(18):4473. doi: 10.3390/cancers15184473.

ABSTRACT

Functional magnetic resonance imaging (fMRI) with blood oxygen level-dependent (BOLD) technique is useful for preoperative mapping of brain functional networks in tumor patients, providing reliable in vivo detection of eloquent cortex to help reduce the risk of postsurgical morbidity. BOLD task-based fMRI (tb-fMRI) is the most often used noninvasive method that can reliably map cortical networks, including those associated with sensorimotor, language, and visual functions. BOLD resting-state fMRI (rs-fMRI) is emerging as a promising ancillary tool for visualization of diverse functional networks. Although fMRI is a powerful tool that can be used as an adjunct for brain tumor surgery planning, it has some constraints that should be taken into consideration for proper clinical interpretation. BOLD fMRI interpretation may be limited by neurovascular uncoupling (NVU) induced by brain tumors. Cerebrovascular reactivity (CVR) mapping obtained using breath-hold methods is an effective method for evaluating NVU potential.

PMID:37760443 | DOI:10.3390/cancers15184473

Error | Forum of resting-state fMRI

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