Most recent paper

Elevated serum platelet count inhibits the effects of brain functional changes on cognitive function in patients with mild cognitive impairment: A resting-state functional magnetic resonance imaging study

Thu, 04/13/2023 - 18:00

Front Aging Neurosci. 2023 Mar 27;15:1088095. doi: 10.3389/fnagi.2023.1088095. eCollection 2023.


OBJECTIVE: Brain function remodeling has been observed in patients with mild cognitive impairment (MCI) and is closely associated with cognitive performance. However, it is not clear if this relationship is influenced by complete blood counts. This study investigated the role of complete blood counts in the relationship between brain function and cognitive performance.

METHODS: Twenty-two MCI patients and eighteen controls were enrolled. All subjects underwent resting-state functional magnetic resonance imaging. A neuropsychological battery [Mini-Mental Status Examination, Auditory Verbal Learning Test (AVLT), Symbol Digit Modalities Test, Boston Naming Test (BNT), Shape Trails Test B (STT-B), Rey Complex Figure Test (RCFT), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Scale] was used to assess cognitive function, and MCI patients received complete blood counts tests for red blood cells (RBC), white blood cells, hemoglobin (HGB), monocytes, and platelet counts (PLT).

RESULTS: Compared with controls, MCI patients demonstrated significantly decreased amplitude of low-frequency fluctuation (ALFF) values in the left dorsolateral superior frontal gyrus, left post orbitofrontal cortex, right medial superior frontal gyrus, right insula, and left triangular inferior frontal gyrus. In the MCI group, there were associations between ALFF values of the left hippocampus (HIP.L) and AVLT (p = 0.003) and AVLT-N5 scores (p = 0.001); ALFF values of the right supramarginal gyrus (SMG.R) and BNT scores (p = 0.044); ALFF values of the right superior temporal gyrus (STG.R) and BNT scores (p = 0.022); ALFF values of the left precuneus (PCUN.L) and STT-B time (p = 0.012); and ALFF values of the left caudate nucleus (CAU.L) and RCFT-time (p = 0.036). Moreover, the HAMA scores were negatively correlated with RBC and HGB levels, and positively correlated with monocyte count. The PLT count was positively correlated with STT-B time. Additionally, high PLT count inhibited the effect of ALFF values of the PCUN. L on STT-B performance in MCI patients (p = 0.0207).

CONCLUSION: ALFF values of the HIP. L, SMG.R, STG. R, PCUN.L, and CAU. L were associated with decreased memory, language, executive function, and visuospatial ability in MCI patients. Notably, elevated PLT count could inhibit the effect of brain functional changes in the PCUN.L on executive function in MCI patients.

PMID:37051376 | PMC:PMC10083369 | DOI:10.3389/fnagi.2023.1088095

Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume

Thu, 04/13/2023 - 18:00

Front Neurosci. 2023 Mar 27;17:1132607. doi: 10.3389/fnins.2023.1132607. eCollection 2023.


OBJECTIVE: Schizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.

METHODS: A total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.

RESULTS: The relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.

CONCLUSION: Using an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.

PMID:37051145 | PMC:PMC10083255 | DOI:10.3389/fnins.2023.1132607

Effects of post-acute COVID-19 syndrome on the functional brain networks of non-hospitalized individuals

Thu, 04/13/2023 - 18:00

Front Neurol. 2023 Mar 27;14:1136408. doi: 10.3389/fneur.2023.1136408. eCollection 2023.


INTRODUCTION: The long-term impact of COVID-19 on brain function remains poorly understood, despite growing concern surrounding post-acute COVID-19 syndrome (PACS). The goal of this cross-sectional, observational study was to determine whether there are significant alterations in resting brain function among non-hospitalized individuals with PACS, compared to symptomatic individuals with non-COVID infection.

METHODS: Data were collected for 51 individuals who tested positive for COVID-19 (mean age 41±12 yrs., 34 female) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19 (mean age 41±14 yrs., 9 female), with both groups assessed an average of 4-5 months after COVID testing. None of the participants had prior neurologic, psychiatric, or cardiovascular illness. Resting brain function was assessed via functional magnetic resonance imaging (fMRI), and self-reported symptoms were recorded.

RESULTS: Individuals with COVID-19 had lower temporal and subcortical functional connectivity relative to controls. A greater number of ongoing post-COVID symptoms was also associated with altered functional connectivity between temporal, parietal, occipital and subcortical regions.

DISCUSSION: These results provide preliminary evidence that patterns of functional connectivity distinguish PACS from non-COVID infection and correlate with the severity of clinical outcome, providing novel insights into this highly prevalent disorder.

PMID:37051059 | PMC:PMC10083436 | DOI:10.3389/fneur.2023.1136408

Diverse Task Classification from Activation Patterns of Functional Neuro-Images Using Feature Fusion Module

Thu, 04/13/2023 - 18:00

Sensors (Basel). 2023 Mar 23;23(7):3382. doi: 10.3390/s23073382.


One of the emerging fields in functional magnetic resonance imaging (fMRI) is the decoding of different stimulations. The underlying idea is to reveal the hidden representative signal patterns of various fMRI tasks for achieving high task-classification performance. Unfortunately, when multiple tasks are processed, performance remains limited due to several challenges, which are rarely addressed since the majority of the state-of-the-art studies cover a single neuronal activity task. Accordingly, the first contribution of this study is the collection and release of a rigorously acquired dataset, which contains cognitive, behavioral, and affective fMRI tasks together with resting state. After a comprehensive analysis of the pitfalls of existing systems on this new dataset, we propose an automatic multitask classification (MTC) strategy using a feature fusion module (FFM). FFM aims to create a unique signature for each task by combining deep features with time-frequency representations. We show that FFM creates a feature space that is superior for representing task characteristics compared to their individual use. Finally, for MTC, we test a diverse set of deep-models and analyze their complementarity. Our results reveal higher classification accuracy compared to benchmarks. Both the dataset and the code are accessible to researchers for further developments.

PMID:37050440 | PMC:PMC10098749 | DOI:10.3390/s23073382

Dynamic changes of resting state functional network following acute ischemic stroke

Wed, 04/12/2023 - 18:00

J Chem Neuroanat. 2023 Apr 10;130:102272. doi: 10.1016/j.jchemneu.2023.102272. Online ahead of print.


Stroke, the second common cause of death in the world, is commonly considered to the well-known phenomenon of diaschisis. After stroke, regions far from the lesion can show altered neural activity. However, the comprehensive treatment recovery mechanism of acute ischemic stroke remains unclear. This study aims to investigate the impact of comprehensive treatment on resting state brain functional connectivity to reveal the therapeutic mechanism through a three time points study design. Twenty-one acute ischemic stroke patients and twenty matched healthy controls (HC) were included. Resting state functional magnetic resonance imaging (fMRI) and clinical evaluations were assessed in three stages: baseline (less than 72 h after stroke onset), post-first month and post-third month. Amplitude of low-frequency fluctuations (ALFF) and Independent component analysis (ICA) were conducted. We found: 1) stroke patients had decreased ALFF in the right cuneus (one of the important parts of the visual network). After three months, ALFF increased to the normal level; 2) the decreased functional connectivity in the right cuneus within the visual network and restored three months after onset. However, the decreased functional connectivity in the right precuneus within the default mode network restored one month after onset; 3) a significant association was found between the clinical scale score change over time and improvement in the cuneus and precuneus functional connectivity. Our results demonstrate the importance of the cuneus and precuneus within the visual network and default mode network in stroke recovery. These findings suggest that the different restored patterns of neural functional networks may contribute to the neurological function recovery. It has potential applications from stroke onset through rehabilitation because different rehabilitation phase corresponds to specific strategies.

PMID:37044352 | DOI:10.1016/j.jchemneu.2023.102272

Abnormal activation of brain regions in idiopathic trigeminal neuralgia patients by fMRI: An activation likelihood estimation meta-analysis

Wed, 04/12/2023 - 18:00

Clin Neurol Neurosurg. 2023 Apr 7;228:107710. doi: 10.1016/j.clineuro.2023.107710. Online ahead of print.


BACKGROUND: Idiopathic trigeminal neuralgia (ITN) is one of the most common types of neuropathic pain, severely affecting the physiological and psychological wellbeing of patients. Recently, fMRI has been used to examine abnormal activation of brain regions in patients with ITN. However, sample sizes have been small in these few studies, and the abnormally activated brain regions remain unclear. Therefore, in the present study, we retrieved and analyzed literature on the brain areas with abnormal or reduced activation in ITN patients, with the aim of providing insight into the neuropathological basis of the disease and to provide new targets for treatment.

METHODS: We retrieved resting state fMRI studies on trigeminal neuralgia patients from PubMed, the Web of Science and Scopus databases until November 2022, and we extracted the coordinates of the sites with increased or decreased activation. We used activation likelihood estimation (ALE) meta-analysis to identify regions of abnormal activation in ITN patients.

RESULTS: ALE meta-analysis revealed that the left caudate nucleus and right anterior ventral nucleus of the thalamus are abnormally hyperactivated in ITN patients. Moreover, ITN patients showed reduced activation in the left precuneus, middle temporal gyrus, lingual gyrus, and medial frontal gyrus.

CONCLUSION: ALE meta-analysis identified several brain regions with abnormally high or decreased activation in ITN patients. Sites with altered activation may be potential targets for non-invasive brain stimulation as adjunct therapy for ITN.

PMID:37043845 | DOI:10.1016/j.clineuro.2023.107710

Co-variations of cerebral blood volume and single neurons discharge during resting state and visual cognitive tasks in non-human primates

Wed, 04/12/2023 - 18:00

Cell Rep. 2023 Apr 11;42(4):112369. doi: 10.1016/j.celrep.2023.112369. Online ahead of print.


To better understand how the brain allows primates to perform various sets of tasks, the ability to simultaneously record neural activity at multiple spatiotemporal scales is challenging but necessary. However, the contribution of single-unit activities (SUAs) to neurovascular activity remains to be fully understood. Here, we combine functional ultrasound imaging of cerebral blood volume (CBV) and SUA recordings in visual and fronto-medial cortices of behaving macaques. We show that SUA provides a significant estimate of the neurovascular response below the typical fMRI spatial resolution of 2mm3. Furthermore, our results also show that SUAs and CBV activities are statistically uncorrelated during the resting state but correlate during tasks. These results have important implications for interpreting functional imaging findings while one constructs inferences of SUA during resting state or tasks.

PMID:37043356 | DOI:10.1016/j.celrep.2023.112369

Altered resting-state functional connectivity of the brain in children with autism spectrum disorder

Tue, 04/11/2023 - 18:00

Radiol Phys Technol. 2023 Apr 11. doi: 10.1007/s12194-023-00717-2. Online ahead of print.


Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders. Brain mapping has shown that functional brain connections are altered in autism. This study investigated the pattern of brain connection changes in autistic people compared to healthy people. This study aimed to analyze functional abnormalities within the brain due to ASD, using resting-state functional magnetic resonance imaging (fMRI). Resting-state functional magnetic resonance images of 26 individuals with ASD and 26 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The DPARSF (data processing assistant for resting-state fMRI) toolbox was used for resting-state functional image processing, and features related to functional connections were extracted from these images. Then, the extracted features from both groups were compared using an Independent Two-Sample T Test, and the features with significant differences between the two groups were identified. Compared with healthy controls, individuals with ASD showed hyper-connectivity in the frontal lobe, anterior cingulum, parahippocampal, left precuneus, angular, caudate, superior temporal, and left pallidum, as well as hypo-connectivity in the precentral, left superior frontal, left middle orbitofrontal, right amygdala, and left posterior cingulum. Our findings show that abnormal functional connectivity exists in patients with ASD. This study makes an important advancement in our understanding of the abnormal neurocircuits causing autism.

PMID:37040021 | DOI:10.1007/s12194-023-00717-2

Altered brain structural and functional connectivity in cannabis users

Mon, 04/10/2023 - 18:00

Sci Rep. 2023 Apr 10;13(1):5847. doi: 10.1038/s41598-023-32521-8.


Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22-36, 19 female) in comparison with 73 healthy controls (age 22-36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.

PMID:37037859 | PMC:PMC10086048 | DOI:10.1038/s41598-023-32521-8

Abnormal functional connectivity of the habenula in mild cognitive impairment patients with depression symptoms revealed by resting-state functional magnetic resonance imaging

Mon, 04/10/2023 - 18:00

Int J Geriatr Psychiatry. 2023 Apr;38(4):e5910. doi: 10.1002/gps.5910.


BACKGROUND: Recent research suggests that abnormalities in the habenula (HB), a core area of the brain that transmits reward information, may be a determinant of depression. However, it is not clear whether the functional connectivity (FC) pattern of the mild cognitive impairment (MCI) with and without depression symptoms is abnormal.

METHODS: In this study, we used resting-state functional magnetic resonance imaging (fMRI) to examine the FC pattern of the HB in MCI patients with depression symptoms (D-MCI). We acquired fMRI data from 54 subjects on a 3T MRI. Subjects collected included 16 patients with D-MCI, 18 patients with MCI with no depression, and 20 healthy controls. One way ANCOVA and post hoc t-test were used to compare the difference in FC strength between the three groups.

RESULTS: The D-MCI group had altered FC between the left HB and the right superior temporal gyrus, right inferior frontal gyrus/opercular part, and right middle frontal gyrus. The D-MCI group had increased FC between the right HB and precuneus.

CONCLUSIONS: These results suggest that the dysfunction of the HB-Default model network might be involved in the neural mechanism underlying depression in MCI.

PMID:37036361 | DOI:10.1002/gps.5910

Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches

Mon, 04/10/2023 - 18:00

Inform Med Unlocked. 2023;37:101176. doi: 10.1016/j.imu.2023.101176. Epub 2023 Jan 18.


The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of transparency are (1) confidence estimation and (2) explainability. Confidence estimation approaches indicate confidence in individual predictions. Explainability methods give insight into the importance of features to model predictions. In this study, we integrate confidence estimation and explainability approaches for the first time. We demonstrate their viability for schizophrenia diagnosis using resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data. We compare two confidence estimation approaches: Monte Carlo dropout (MCD) and MC batch normalization (MCBN). We combine them with two gradient-based explainability approaches, saliency and layer-wise relevance propagation (LRP), and examine their effects upon explanations. We find that MCD often adversely affects model gradients, making it ill-suited for integration with gradient-based explainability methods. In contrast, MCBN does not affect model gradients. Additionally, we find many participant-level differences between regular explanations and the distributions of explanations for combined explainability and confidence estimation approaches. This suggests that a similar confidence estimation approach used in a clinical context with explanations only output for the regular model would likely not yield adequate explanations. We hope that our findings will provide a starting point for the integration of the two fields, provide useful guidance for future studies, and accelerate the development of transparent neuroimaging clinical decision support systems.

PMID:37035832 | PMC:PMC10078989 | DOI:10.1016/j.imu.2023.101176

Basal forebrain activity predicts functional degeneration in the entorhinal cortex and decreases with Alzheimer's Disease progression

Mon, 04/10/2023 - 18:00

bioRxiv. 2023 Mar 28:2023.03.28.534523. doi: 10.1101/2023.03.28.534523. Preprint.


BACKGROUND AND OBJECTIVES: Recent models of Alzheimer's Disease (AD) suggest the nucleus basalis of Meynert (NbM) as the origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear.

METHODS: We analyzed resting-state (rs)fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=71) at baseline and two years later.

RESULTS: At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations (fALFF), differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio.

DISCUSSION: Our findings give novel insights into the pathogenesis of AD by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.

PMID:37034733 | PMC:PMC10081194 | DOI:10.1101/2023.03.28.534523

Sex-specific age-related changes in glymphatic function assessed by resting-state functional magnetic resonance imaging

Mon, 04/10/2023 - 18:00

bioRxiv. 2023 Apr 5:2023.04.02.535258. doi: 10.1101/2023.04.02.535258. Preprint.


The glymphatic system that clears out brain wastes, such as amyloid-β (Aβ) and tau, through cerebrospinal fluid (CSF) flow may play an important role in aging and dementias. However, a lack of non-invasive tools to assess the glymphatic function in humans hindered the understanding of the glymphatic changes in healthy aging. The global infra-slow (<0.1 Hz) brain activity measured by the global mean resting-state fMRI signal (gBOLD) was recently found to be coupled by large CSF movements. This coupling has been used to measure the glymphatic process and found to correlate with various pathologies of Alzheimer's disease (AD), including Aβ pathology. Using resting-state fMRI data from a large group of 719 healthy aging participants, we examined the sex-specific changes of the gBOLD-CSF coupling, as a measure of glymphatic function, over a wide age range between 36-100 years old. We found that this coupling index remains stable before around age 55 and then starts to decline afterward, particularly in females. Menopause may contribute to the accelerated decline in females.

PMID:37034667 | PMC:PMC10081329 | DOI:10.1101/2023.04.02.535258

Functional connectivity MRI quality control procedures in CONN

Mon, 04/10/2023 - 18:00

Front Neurosci. 2023 Mar 23;17:1092125. doi: 10.3389/fnins.2023.1092125. eCollection 2023.


Quality control (QC) for functional connectivity magnetic resonance imaging (FC-MRI) is critical to ensure the validity of neuroimaging studies. Noise confounds are common in MRI data and, if not accounted for, may introduce biases in functional measures affecting the validity, replicability, and interpretation of FC-MRI study results. Although FC-MRI analysis rests on the assumption of adequate data processing, QC is underutilized and not systematically reported. Here, we describe a quality control pipeline for the visual and automated evaluation of MRI data implemented as part of the CONN toolbox. We analyzed publicly available resting state MRI data (N = 139 from 7 MRI sites) from the FMRI Open QC Project. Preprocessing steps included realignment, unwarp, normalization, segmentation, outlier identification, and smoothing. Data denoising was performed based on the combination of scrubbing, motion regression, and aCompCor - a principal component characterization of noise from minimally eroded masks of white matter and of cerebrospinal fluid tissues. Participant-level QC procedures included visual inspection of raw-level data and of representative images after each preprocessing step for each run, as well as the computation of automated descriptive QC measures such as average framewise displacement, average global signal change, prevalence of outlier scans, MNI to anatomical and functional overlap, anatomical to functional overlap, residual BOLD timeseries variability, effective degrees of freedom, and global correlation strength. Dataset-level QC procedures included the evaluation of inter-subject variability in the distributions of edge connectivity in a 1,000-node graph (FC distribution displays), and the estimation of residual associations across participants between functional connectivity strength and potential noise indicators such as participant's head motion and prevalence of outlier scans (QC-FC analyses). QC procedures are demonstrated on the reference dataset with an emphasis on visualization, and general recommendations for best practices are discussed in the context of functional connectivity and other fMRI analysis. We hope this work contributes toward the dissemination and standardization of QC testing performance reporting among peers and in scientific journals.

PMID:37034165 | PMC:PMC10076563 | DOI:10.3389/fnins.2023.1092125

Social reappraisal of emotions is linked with the social presence effect in the default mode network

Mon, 04/10/2023 - 18:00

Front Psychiatry. 2023 Mar 23;14:1128916. doi: 10.3389/fpsyt.2023.1128916. eCollection 2023.


INTRODUCTION: Social reappraisal, during which one person deliberately tries to regulate another's emotions, is a powerful cognitive form of social emotion regulation, crucial for both daily life and psychotherapy. The neural underpinnings of social reappraisal include activity in the default mode network (DMN), but it is unclear how social processes influence the DMN and thereby social reappraisal functioning. We tested whether the mere presence of a supportive social regulator had an effect on the DMN during rest, and whether this effect in the DMN was linked with social reappraisal-related neural activations and effectiveness during negative emotions.

METHODS: A two-part fMRI experiment was performed, with a psychotherapist as the social regulator, involving two resting state (social, non-social) and two task-related (social reappraisal, social no-reappraisal) conditions.

RESULTS: The psychotherapist's presence enhanced intrinsic functional connectivity of the dorsal anterior cingulate (dACC) within the anterior medial DMN, with the effect positively related to participants' trust in psychotherapists. Secondly, the social presence-induced change in the dACC was related with (a) the social reappraisal-related activation in the bilateral dorsomedial/dorsolateral prefrontal cortex and the right temporoparietal junction and (b) social reappraisal success, with the latter relationship moderated by trust in psychotherapists.

CONCLUSION: Results demonstrate that a psychotherapist's supportive presence can change anterior medial DMN's intrinsic connectivity even in the absence of stimuli and that this DMN change during rest is linked with social reappraisal functioning during negative emotions. Data suggest that trust-dependent social presence effects on DMN states are relevant for social reappraisal-an idea important for daily-life and psychotherapy-related emotion regulation.

PMID:37032933 | PMC:PMC10076786 | DOI:10.3389/fpsyt.2023.1128916

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

Mon, 04/10/2023 - 18:00

Front Psychiatry. 2023 Mar 23;14:1125339. doi: 10.3389/fpsyt.2023.1125339. eCollection 2023.


Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

PMID:37032921 | PMC:PMC10077869 | DOI:10.3389/fpsyt.2023.1125339

Erratum: Investigating the association between polygenic risk scores for Alzheimer's disease with cognitive performance and intrinsic functional connectivity in healthy adults

Mon, 04/10/2023 - 18:00

Front Aging Neurosci. 2023 Mar 23;15:1186490. doi: 10.3389/fnagi.2023.1186490. eCollection 2023.


[This corrects the article DOI: 10.3389/fnagi.2022.837284.].

PMID:37032825 | PMC:PMC10078343 | DOI:10.3389/fnagi.2023.1186490

Aberrant voxel-based degree centrality and functional connectivity in Parkinson's disease patients with fatigue

Mon, 04/10/2023 - 18:00

CNS Neurosci Ther. 2023 Apr 10. doi: 10.1111/cns.14212. Online ahead of print.


AIMS: The study aimed to investigate alterations in the inherent connectivity pattern of global functional networks in Parkinson's disease (PD) patients with fatigue.

METHODS: Eighteen PD patients with fatigue (PD-F), 20 PD patients without fatigue (PD-NF), and 23 healthy controls (HCs) were recruited and analyzed by the voxel-wise degree centrality (DC) and the seed-based functional connectivity (FC) analysis. Meanwhile, the surface-based morphometry (SBM) analysis was also commanded to explore the structural alternations among groups.

RESULTS: PD-F patients displayed reduced DC values in the left postcentral gyrus relative to PD-NF and HCs groups, while increased DC values in the bilateral precuneus compared to HCs. Simultaneously, altered DC value in the left postcentral gyrus negatively corresponded to the mean fatigue severity scale (FSS) in PD-F patients. Additionally, the receiver operating characteristic (ROC) curves uncovered that the reduced DC value of the left postcentral gyrus could discriminate PD-F from PD-NF and HCs groups. Our FC analysis further revealed that altered FC was located predominantly in the sensorimotor network in the PD-F group. Moreover, we discovered no statistically significant differences between the three groups concerning cortical thickness.

CONCLUSION: Our findings indicated that the altered functional connectivity in the sensorimotor network centering on the left postcentral gyrus and the bilateral precuneus might be the potential pathogenesis of PD with fatigue.

PMID:37032641 | DOI:10.1111/cns.14212

Power spectral analysis can determine language laterality from resting-state functional MRI data in healthy controls

Mon, 04/10/2023 - 18:00

J Neuroimaging. 2023 Apr 9. doi: 10.1111/jon.13105. Online ahead of print.


BACKGROUND AND PURPOSE: Resting-state functional magnetic resonance imaging (rsfMRI) has been proposed as an alternative to task-based fMRI including clinical situations such as preoperative brain tumor planning, due to advantages including ease of performance and time savings. However, one of its drawbacks is the limited ability to accurately lateralize language function.

METHODS: Using the rsfMRI data of healthy controls, we carried out a power spectra analysis on three regions of interest (ROIs): Broca's area (BA) in the frontal cortex for language, hand motor (HM) area in the primary motor cortex, and the primary visual cortex (V1). Spike removal, motion correction, linear trend removal, and spatial smoothing were applied. Spontaneous low-frequency fluctuations (0.01-0.1 Hz) were filtered to enable functional integration.

RESULTS: BA showed greater power on the left hemisphere relative to the right (p = .0055), while HM (p = .1563) and V1 (p = .4681) were not statistically significant. A novel index, termed the power laterality index (PLI), computed to estimate the degree of power lateralization for each brain region, revealed a statistically significant difference between BA and V1 (p < .00001), where V1 was used as a control since the primary visual cortex does not lateralize. Validation studies used to compare PLI to a laterality index computed using phonemic fluency, a task-based, language fMRI paradigm, demonstrated good correlation.

CONCLUSIONS: The power spectra for BA revealed left language lateralization, which was not replicated in HM or V1. This work demonstrates the feasibility and validity of an ROI-based power spectra analysis on rsfMRI data for language lateralization.

PMID:37032593 | DOI:10.1111/jon.13105

Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data

Fri, 04/07/2023 - 18:00

IEEE Trans Neural Netw Learn Syst. 2023 Feb 17;PP. doi: 10.1109/TNNLS.2023.3243000. Online ahead of print.


Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.

PMID:37027556 | DOI:10.1109/TNNLS.2023.3243000