Most recent paper

Hum Brain Mapp. 2023 Feb 27. doi: 10.1002/hbm.26243. Online ahead of print.

ABSTRACT

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

PMID:36852459 | DOI:10.1002/hbm.26243

Neuroimage. 2023 Feb 25:119981. doi: 10.1016/j.neuroimage.2023.119981. Online ahead of print.

ABSTRACT

Neural oscillations in distinct frequency bands are ubiquitous in the brain and play a role in many cognitive processes. The "communication by coherence" hypothesis, poses that the synchronization through phase coupling of frequency-specific neural oscillations regulate information flow across distribute brain regions. Specifically, the posterior alpha frequency band (7-12 Hz) is thought to gate bottom-up visual information flow by inhibition during visual processing. Evidence shows that increased alpha phase coherency positively correlates with functional connectivity in resting state connectivity networks, supporting alpha mediates neural communication through coherency. However, these findings have mainly been derived from spontaneous changes in the ongoing alpha rhythm. In this study, we experimentally modulate the alpha rhythm by targeting individuals' intrinsic alpha frequency with sustained rhythmic light to investigate alpha-mediated synchronous cortical activity in both EEG and fMRI. We hypothesize increased alpha coherency and fMRI connectivity should arise from modulation of the intrinsic alpha frequency (IAF) as opposed to control frequencies in the alpha range. Sustained rhythmic and arrhythmic stimulation at the IAF and at neighboring frequencies within the alpha band range (7-12 Hz) was implemented and assessed in a separate EEG and fMRI study. We observed increased cortical alpha phase coherency in the visual cortex during rhythmic stimulation at the IAF as in comparison to rhythmic stimulation of control frequencies. In the fMRI, we found increased functional connectivity for stimulation at the IAF in visual and parietal areas as compared to other rhythmic control frequencies by correlating time courses from a set of regions of interest for the different stimulation conditions and applying network-based statistics. This suggests that rhythmic stimulation at the IAF frequency induces a higher degree of synchronicity of neural activity across the occipital and parietal cortex, which supports the role of the alpha oscillation in gating information flow during visual processing.

PMID:36848971 | DOI:10.1016/j.neuroimage.2023.119981

Alcohol Alcohol. 2023 Feb 27:agad006. doi: 10.1093/alcalc/agad006. Online ahead of print.

ABSTRACT

AIMS: Maintaining abstinence from alcohol use disorder (AUD) is extremely challenging, partially due to increased symptoms of anxiety and stress that trigger relapse. Rodent models of AUD have identified that the bed nucleus of the stria terminalis (BNST) contributes to symptoms of anxiety-like behavior and drug-seeking during abstinence. In humans, however, the BNST's role in abstinence remains poorly understood. The aims of this study were to assess BNST network intrinsic functional connectivity in individuals during abstinence from AUD compared to healthy controls and examine associations between BNST intrinsic functional connectivity, anxiety and alcohol use severity during abstinence.

METHODS: The study included resting state fMRI scans from participants aged 21-40 years: 20 participants with AUD in abstinence and 20 healthy controls. Analyses were restricted to five pre-selected brain regions with known BNST structural connections. Linear mixed models were used to test for group differences, with sex as a fixed factor given previously shown sex differences.

RESULTS: BNST-hypothalamus intrinsic connectivity was lower in the abstinent group relative to the control group. There were also pronounced sex differences in both the group and individual analyses; many of the findings were specific to men. Within the abstinent group, anxiety was positively associated with BNST-amygdala and BNST-hypothalamus connectivity, and men, not women, showed a negative relationship between alcohol use severity and BNST-hypothalamus connectivity.

CONCLUSIONS: Understanding differences in connectivity during abstinence may help explain the clinically observed anxiety and depression symptoms during abstinence and may inform the development of individualized treatments.

PMID:36847484 | DOI:10.1093/alcalc/agad006

Dev Psychopathol. 2023 Feb 27:1-10. doi: 10.1017/S0954579423000111. Online ahead of print.

ABSTRACT

Internalizing and externalizing problems that emerge during adolescence differentially increase boys' and girls' risk for developing psychiatric disorders. It is not clear, however, whether there are sex differences in the intrinsic functional architecture of the brain that underlie changes in the severity of internalizing and externalizing problems in adolescents. Using resting-state fMRI data and self-reports of behavioral problems obtained from 128 adolescents (73 females; 9-14 years old) at two timepoints, we conducted multivoxel pattern analysis to identify resting-state functional connectivity markers at baseline that predict changes in the severity of internalizing and externalizing problems in boys and girls 2 years later. We found sex-differentiated involvement of the default mode network in changes in internalizing and externalizing problems. Whereas changes in internalizing problems were associated with the dorsal medial subsystem in boys and with the medial temporal subsystem in girls, changes in externalizing problems were predicted by hyperconnectivity between core nodes of the DMN and frontoparietal network in boys and hypoconnectivity between the DMN and affective networks in girls. Our results suggest that different neural mechanisms predict changes in internalizing and externalizing problems in adolescent boys and girls and offer insights concerning mechanisms that underlie sex differences in the expression of psychopathology in adolescence.

PMID:36847268 | DOI:10.1017/S0954579423000111

Front Psychiatry. 2023 Feb 9;14:1083465. doi: 10.3389/fpsyt.2023.1083465. eCollection 2023.

ABSTRACT

BACKGROUND: Despite the demonstrated association between empathy and gambling at the behavioral level, limited neuroimaging research on empathy and gambling disorder (GD) has been conducted. Whether and how the brain network of empathy and that of gambling interact in disordered gamblers has not been investigated. This study aimed to address this research gap by examining the hierarchical organizational patterns, in which the differences of causal interactions of these networks between disordered gamblers and healthy controls were revealed.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data of 32 disordered gamblers and 56 healthy controls were included in the formal analysis. Dynamic causal modeling was used to examine the effective connectivity within and between empathy and gambling networks among all participants.

RESULTS: All participants showed significant effective connectivity within and between empathy and gambling networks. However, compared with healthy controls, disordered gamblers displayed more excitatory effective connectivity within the gambling network, the tendency to display more excitatory effective connectivity from the empathy network to the gambling network, and reduced inhibitory effective connectivity from the gambling network to the empathy network.

CONCLUSION: The exploratory study was the first to examine the effective connectivity within and between empathy and gambling networks among disordered gamblers and healthy controls. These results provided insights into the causal relationship between empathy and gambling from the neuroscientific perspective and further confirmed that disordered gamblers show altered effective connectivity within and between these two brain networks, which may be considered to be a potential neural index for GD identification. In addition, the altered interactions between empathy and gambling networks may also indicate the potential targets for the neuro-stimulation intervention approach (e.g., transcranial magnetic stimulation).

PMID:36846215 | PMC:PMC9947716 | DOI:10.3389/fpsyt.2023.1083465

Drug Alcohol Depend Rep. 2022 May 14;3:100065. doi: 10.1016/j.dadr.2022.100065. eCollection 2022 Jun.

ABSTRACT

BACKGROUND: The global prevalence of methamphetamine use disorder (MUD) and the associated economic burden are increasing, but effective pharmacological treatment is lacking. Therefore, understanding the neurological mechanisms underlying MUD is essential to develop clinical strategies and improve patient care. Individuals with MUD can show static brain network abnormalities during the resting state, but their alterations in dynamic functional network connectivity (dFNC) are unclear.

METHODS: In this study, we obtained resting-state functional magnetic resonance imaging from 42 males with MUD and 41 healthy controls. Sliding-window and spatial independent component analyses with a k-means clustering algorithm were used to assess the recurring functional connectivity states. The temporal properties of the dFNC, including fraction and dwelling time of each state and the number of transitions between different states, were compared between the two groups. In addition, the relationships between the temporal properties of the dFNC and clinical characteristics of the MUDs, including their anxiety and depressive symptoms, were further explored.

RESULTS: While the two groups shared many similarities in their dFNC, the occurrence of a highly integrated functional network state and a state featuring balanced integration and segregation in the MUDs significantly correlated with the total drug usage (Spearman's rho = 0.47, P = 0.002) and duration of abstinence (Spearman's rho = 0.38, P = 0.013), respectively.

CONCLUSIONS: The observed results in our study demonstrate that methamphetamines can affect dFNC, which may reflect the drug's influence on cognitive abilities. Our study justifies further studies into the effects of MUD on dynamic neural mechanisms.

PMID:36845989 | PMC:PMC9949309 | DOI:10.1016/j.dadr.2022.100065

Front Neurosci. 2023 Feb 9;17:1053114. doi: 10.3389/fnins.2023.1053114. eCollection 2023.

ABSTRACT

BACKGROUND: Previous studies on primary angle-closure glaucoma (PACG) primarily focused on local brain regions or global abnormal brain activity; however, the alteration of interhemispheric functional homotopy and its possible cause of brain-wide functional connectivity abnormalities have not been well-studied. Little is known about whether brain functional alteration could be used to differentiate from healthy controls (HCs) and its correlation with neurocognitive impairment.

METHODS: Forty patients with PACG and 40 age- and sex-matched healthy controls were recruited for this study; resting-state functional magnetic resonance imaging (rs-fMRI), and clinical data were collected. We used the voxel-mirrored homotopic connectivity (VMHC) method to explore between-group differences and selected brain regions with statistically significant differences as regions of interest for whole-brain functional connectivity analysis. Partial correlation was used to evaluate the association between abnormal VMHC values in significantly different regions and clinical parameters, with with age and sex as covariates. Finally, the support vector machine (SVM) model was performed in classification prediction of PACG.

RESULTS: Compared with healthy controls, patients with PACG exhibited significantly decreased VMHC values in the lingual gyrus, insula, cuneus, and pre- and post-central gyri; no regions exhibited increased VMHC values. Subsequent functional connectivity analysis revealed extensive functional changes in functional networks, particularly the default mode, salience, visual, and sensorimotor networks. The SVM model showed good performance in classification prediction of PACG, with an area under curve (AUC) of 0.85.

CONCLUSION: Altered functional homotopy of the visual cortex, sensorimotor network, and insula may lead to impairment of visual function in PACG, suggesting that patients with PACG may have visual information interaction and integration dysfunction.

PMID:36845423 | PMC:PMC9947534 | DOI:10.3389/fnins.2023.1053114

Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12036:1203608. doi: 10.1117/12.2611557. Epub 2022 Apr 4.

ABSTRACT

Brain age prediction based on functional magnetic resonance imaging (fMRI) data has the potential to serve as a biomarker for quantifying brain health. To predict the brain age based on fMRI data robustly and accurately, we curated a large dataset (n = 4259) of fMRI scans from seven different data acquisition sites and computed personalized functional connectivity measures at multiple scales from each subject's fMRI scan. Particularly, we computed personalized large-scale functional networks and generated functional connectivity measures at multiple scales to characterize each fMRI scan. To account for inter-site effects on the functional connectivity measures, we harmonized the functional connectivity measures in their tangent space and then built brain age prediction models on the harmonized functional connectivity measures. We compared the brain age prediction models with alternatives that were built on the functional connectivity measures computed at a single scale and harmonized using different strategies. Comparison results have demonstrated that the best brain age prediction performance was achieved by the prediction model built on the multi-scale functional connectivity measures that were harmonized in tangent space, indicating that multi-scale functional connectivity measures provided richer information than those computed at any single scales and the harmonization of functional connectivity measures in tangent space improved the brain age prediction.

PMID:36845412 | PMC:PMC9951555 | DOI:10.1117/12.2611557

J Atten Disord. 2023 Feb 26:10870547231155436. doi: 10.1177/10870547231155436. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of present research was to make a Meta-Thinking educational program based on mental-brain simulation and to evaluate its effectiveness on executive functions, emotion regulation and impulsivity in children with ADHD.

METHODS: The research method was Embedded Design: Embedded Experimental Model. The research sample included 32 children with ADHD who were randomly assigned to two experimental and control groups. The intervention was implemented for eight sessions of 1.5 hr for the experimental group, and fMRI images were taken from them, while the control group didn't receive any treatment. Finally, using semi-structured interviews, coherent information was collected from the parents of the experimental group about the changes made. Data were analyzed with SPSS-24, MAXQDA, fMRIprep, and FSL software.

RESULTS: The Meta-Thinking Educational Program had effect on performance of ADHD children and suppressed brain regions related to DMN.

CONCLUSION: The Implementation of this educational program plays a vital role in improving psychological problems of children with ADHD.

PMID:36843348 | DOI:10.1177/10870547231155436

Prog Neuropsychopharmacol Biol Psychiatry. 2023 Feb 24:110736. doi: 10.1016/j.pnpbp.2023.110736. Online ahead of print.

ABSTRACT

In recent decades, the primary intention of neuroscientists and psychiatrics is to evaluate the connectivity between brain regions and psychiatric disorders. The amygdala has central immersion in memory alliance, stress response, emotional perception, and automatic responses to emotional stimuli. This paper uses a meta-analysis approach to establish the relationship between structural resting state and functional amygdala connectivity with depression and suicide ideation with suicide behavior. In addition, this study explores the moderating effect of patients' demographic characteristics (gender and age) based on 30 studies. The results show that structural amygdala connectivity is positively related to the instability of depression, while for resting and task functional connectivity amygdala shows a significant negative connection with depression. Furthermore, the amygdala showed a partial activation for non-suicide self-injuries and suicide ideation. From structural and functional magnetic imaging, the current findings also support the moderating effect of the age of the participants on the amygdala connectivity with psychiatric conditions. Generally, amygdala connectivity with psychiatric disorders was not significantly moderate with the role of gender, however, this study enhances the existing hypothetical review articles and confirms the connectivity of the psychological condition with the amygdala region. It concludes that the amygdala plays a vital role in regulating and responding to emotions.

PMID:36842608 | DOI:10.1016/j.pnpbp.2023.110736

Sci Rep. 2023 Feb 25;13(1):3284. doi: 10.1038/s41598-023-30195-w.

ABSTRACT

Pediatric concussion has a rising incidence and can lead to long-term symptoms in nearly 30% of children. Resting state functional magnetic resonance imaging (rs-fMRI) disturbances are a common pathological feature of pediatric concussion, though no studies have explicitly examined sex-differences with respect to this outcome, precluding a sex-specific understanding of the functional neuropathology of pediatric concussion. Therefore, we performed a secondary data analysis of rs-fMRI data collected on children with concussion (n = 29) recruited from in a pediatric hospital setting, with greater than 12:1 matched control data accessed from the open-source ABIDE-II database. Seed-based and region of interest (ROI) analyses were used to examine sex-based rs-fMRI differences; threshold-free cluster enhancement (TFCE) and a family-wise error (FWE) corrected p-values were used to identify significantly different clusters. In comparing females with concussion to healthy females, groupwise differences were observed irrespective of seed selected. Notably, we observed (in order of largest effect) hypo-connectivity between the anterior cingulate cortex of the salience network and the thalamus and precuneus (TFCE = 1473.5, p-FWE < 0.001) and the cingulate gyrus (TFCE = 769.3, p-FWE = 0.009), and the seed (posterior cingulate cortex (PCC)) of the default mode network and the paracingulate gyrus (TFCE = 1275.7, p-FWE < 0.001), occipital pole right (TFCE = 1045.0, p-FWE = 0.001), and sub-callosal cortex (TFCE = 844.9, p-FWE = 0.005). Hyper-connectivity was observed between the salience network seed and the cerebellum (TFCE = 1719.3, p-FWE < 0.001) and the PCC and the thalamus (TFCE = 1198.3, p-FWE < 0.001), cuneal cortex (1070.9, p-FWE = 0.001), and lateral occipital cortex left (TFCE = 832.8, p-FWE = 0.006). ROI analyses showed 10 and 5 significant clusters of hypo- and hyper-connectivity in females, respectively. Only one cluster of difference was found between males with concussion and healthy males on seed-based analyses, and 3 clusters on ROI analyses. There are alterations in rs-fMRI in females with concussion at one-month post-injury that are minimally present in males, which provides further evidence that recovery timelines in pediatric concussion may differ by sex.

PMID:36841854 | DOI:10.1038/s41598-023-30195-w

J Affect Disord. 2023 Feb 23:S0165-0327(23)00251-3. doi: 10.1016/j.jad.2023.02.083. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the altered interhemispheric functional connectivity in the resting state in patients with recurrent major depressive disorder (MDD).

METHODS: Voxel-mirrored homotopic connectivity (VMHC), a measure of the functional connectivity between any pair of symmetrical interhemispheric voxels, and pattern classification were examined in 41 recurrent MDD patients (22 during the depressive state and 19 during the remitted state) and 60 age, sex, and education level-matched healthy controls (HC) using resting-state functional magnetic resonance imaging (fMRI).

RESULTS: Compared with HC, the recurrent MDD patients exhibited decreased VMHC values in the bilateral fusiform, inferior occipital gyrus, posterior insula, precentral gyrus, precuneus, superior temporal gyrus, and thalamus. A significant negative correlation between the VMHC value of the bilateral posterior insula and illness duration in recurrent MDD was identified. Support vector machine (SVM) analysis showed that VMHC in the fusiform and posterior insula could be used to distinguish recurrent MDD patients from HC with a sensitivity and accuracy >0.6.

CONCLUSION: Our findings revealed a reduction in the resting-state brain activity across several neural networks in patients with recurrent MDD, including within the posterior insula. Lower VMHC values in the posterior insula were associated with longer illness duration, suggesting that impairment in interhemispheric synchronization within the salience network may be due to the accumulated pathology of depression and may contribute to future depression relapse. VMHC changes in the posterior insula may serve as a potential imaging marker to discriminate recurrent MDD patients from HC.

PMID:36841304 | DOI:10.1016/j.jad.2023.02.083

Hum Brain Mapp. 2023 Feb 25. doi: 10.1002/hbm.26234. Online ahead of print.

ABSTRACT

Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRI scans of individuals with schizophrenia and controls (M = 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5 min) reference time course were sufficiently approximated with just 3-3.5 min of data (r = 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2 min (r = 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5 min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5 min of scan time than those generated from the last 5 min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ min of scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2 min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5 min show good clinical utility without significant loss of individual FNC information of longer scan lengths.

PMID:36840728 | DOI:10.1002/hbm.26234

Life (Basel). 2023 Feb 20;13(2):590. doi: 10.3390/life13020590.

ABSTRACT

Benign childhood epilepsy with centrotemporal spikes (BECTS) is one of the most common childhood epilepsy syndromes and may be associated with language deficits. Resting-state functional magnetic resonance imaging (fMRI) data were collected from a total of 78 children: 52 patients with BECTS (28 drug-naïve and 24 medicated) and 26 healthy controls (HC). Granger causality analysis (GCA) was used to investigate alterations in effective connectivity (EC) between the language network core node (Broca's area) and the whole brain. EC from Broca's area to the left Heschl's gyrus (HG), right putamen, and anterior cingulate cortex (ACC) was significantly increased, while EC from the bilateral putamen and left ACC to Broca's area was significantly decreased in BECTS. Moreover, altered EC of Broca's area to the right putamen was significantly positively correlated with verbal IQ (VIQ), while altered EC of Broca's area to the ACC showed significantly negative correlations with the frequency of seizures. Altered EC from the left putamen to Broca's area was also significantly negatively correlated with performance IQ (PIQ) and full-scale IQ (FSIQ) in the drug-naïve group. In addition, there was a significant positive correlation between the EC of Broca's area to the left HG and the number of seizures, as well as between the EC of Broca's area to the right putamen and the age at onset in the medicated group. These findings suggest abnormal causal effects on the language network related to Broca's area in children with BECTS. Longitudinal investigation of language network development and further follow-up may be needed to illuminate the changes in organization and rebalancing over time.

PMID:36836947 | DOI:10.3390/life13020590

J Pers Med. 2023 Feb 19;13(2):370. doi: 10.3390/jpm13020370.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is among the newest techniques of advanced neuroimaging that offer the opportunity for neuroradiologists, neurophysiologists, neuro-oncologists, and neurosurgeons to pre-operatively plan and manage different types of brain lesions. Furthermore, it plays a fundamental role in the personalized evaluation of patients with brain tumors or patients with an epileptic focus for preoperative planning. While the implementation of task-based fMRI has increased in recent years, the existing resources and evidence related to this technique are limited. We have, therefore, conducted a comprehensive review of the available resources to compile a detailed resource for physicians who specialize in managing patients with brain tumors and seizure disorders. This review contributes to the existing literature because it highlights the lack of studies on fMRI and its precise role and applicability in observing eloquent cerebral areas in surgical oncology and epilepsy patients, which we believe is underreported. Taking these considerations into account would help to better understand the role of this advanced neuroimaging technique and, ultimately, improve patient life expectancy and quality of life.

PMID:36836604 | DOI:10.3390/jpm13020370

J Pers Med. 2023 Jan 29;13(2):251. doi: 10.3390/jpm13020251.

ABSTRACT

Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson's correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN "features" that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality.

PMID:36836485 | DOI:10.3390/jpm13020251

J Clin Med. 2023 Feb 9;12(4):1377. doi: 10.3390/jcm12041377.

ABSTRACT

Von Economo neurons (VENs) are rod, stick, or corkscrew cells mostly located in layer V of the frontoinsular and anterior cingulate cortices. VENs are projection neurons related to human-like social cognitive abilities. Post-mortem histological studies found VEN alterations in several neuropsychiatric disorders, including schizophrenia (SZ). This pilot study aimed to evaluate the role of VEN-containing areas in shaping patterns of resting-state brain activation in patients with SZ (n = 20) compared to healthy controls (HCs; n = 20). We performed a functional connectivity analysis seeded in the cortical areas with the highest density of VENs followed by fuzzy clustering. The alterations found in the SZ group were correlated with psychopathological, cognitive, and functioning variables. We found a frontotemporal network that was shared by four clusters overlapping with the salience, superior-frontal, orbitofrontal, and central executive networks. Differences between the HC and SZ groups emerged only in the salience network. The functional connectivity of the right anterior insula and ventral tegmental area within this network were negatively correlated with experiential negative symptoms and positively correlated with functioning. This study provides some evidence to show that in vivo, VEN-enriched cortical areas are associated with an altered resting-state brain activity in people with SZ.

PMID:36835913 | DOI:10.3390/jcm12041377

Brain Sci. 2023 Feb 16;13(2):339. doi: 10.3390/brainsci13020339.

ABSTRACT

Cortical visual system dysfunction is closely related to the progression of Alzheimer's Disease (AD), while retinal vascular structures play an important role in the integrity of the function of the visual network and are a potential biomarker of AD. This study explored the association between the cortical visual system and retinal vascular structures in AD-spectrum patients, and it established a screening tool to detect preclinical AD based on these parameters identified in a retinal examination. A total of 42 subjects were enrolled and were distributed into two groups: 22 patients with cognitive impairment and 20 healthy controls. All participants underwent neuropsychological tests, optical coherence tomography angiography and resting-state fMRI imaging. Seed-based functional connectivity analysis was used to construct the cortical visual network. The association of functional connectivity of the cortical visual system and retinal vascular structures was further explored in these subjects. This study found that the cognitive impairment group displayed prominently decreased functional connectivity of the cortical visual system mainly involving the right inferior temporal gyrus, left supramarginal gyrus and right postcentral gyrus. Meanwhile, we observed that retinal vascular structure characteristics deteriorated with the decline in functional connectivity in the cortical visual system. Our study provided novel insights into the aberrant cortical visual system in patients with cognitive impairment that strongly emphasized the critical role of retinal vascular structure characteristics, which could be used as potential biomarkers for diagnosing and monitoring the progression of AD.

PMID:36831883 | DOI:10.3390/brainsci13020339

Brain Sci. 2023 Feb 10;13(2):300. doi: 10.3390/brainsci13020300.

ABSTRACT

PURPOSE: Brain areas frequently implicated in language recovery after stroke comprise perilesional sites in the left hemisphere and homotopic regions in the right hemisphere. However, the neuronal mechanisms underlying language restoration are still largely unclear.

METHODS AND MATERIALS: In the present study, we investigated the brain function in 15 patients with poststroke aphasia and 30 matched control subjects by combining the regional homogeneity (ReHo) and amplitudes of low-frequency fluctuation (ALFF) analysis methods based on resting-state fMRI.

RESULTS: Compared to the control subjects, the patients with aphasia exhibited increased ReHo and ALFF values in the ipsilateral perilesional areas and increased ReHo in the contralesional right middle frontal gyrus.

CONCLUSIONS: The increased spontaneous brain activity in patients with poststroke aphasia during the recovery period, specifically in the ipsilateral perilesional regions and the homologous language regions of the right hemisphere, has potential implications for the treatment of patients with aphasia.

PMID:36831843 | DOI:10.3390/brainsci13020300

Brain Sci. 2023 Feb 6;13(2):274. doi: 10.3390/brainsci13020274.

ABSTRACT

Evidence has shown the roles of taVNS and TECS in improving depression but few studies have explored their synergistic effects on MDD. Therefore, the treatment responsivity and neurological effects of TECAS were investigated and compared to escitalopram, a commonly used medication for depression. Fifty patients with mild-to-moderate MDD (29 in the TECAS group and 21 in another) and 49 demographically matched healthy controls were recruited. After an eight-week treatment, the outcomes of TECAS and escitalopram were evaluated by the effective rate and reduction rate based on the Montgomery-Asberg Depression Rating Scale, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale. Altered brain networks were analyzed between pre- and post-treatment using independent component analysis. There was no significant difference in clinical scales between TECAS and escitalopram but these were significantly decreased after each treatment. Both treatments modulated connectivity of the default mode network (DMN), dorsal attention network (DAN), right frontoparietal network (RFPN), and primary visual network (PVN), and the decreased PVN-RFPN connectivity might be the common brain mechanism. However, there was increased DMN-RFPN and DMN-DAN connectivity after TECAS, while it decreased in escitalopram. In conclusion, TECAS could relieve symptoms of depression similarly to escitalopram but induces different changes in brain networks.

PMID:36831816 | DOI:10.3390/brainsci13020274