Resting-state Functional Imaging Research Articles

Brain structural and functional abnormalities associated with acute post-traumatic headache: iron deposition and functional connectivity

BackgroundThe purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs, and time since most recent TBI.MethodsSixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 90 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons).ResultsThe acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* in the left posterior cingulate with SCAT symptom severity score (p = 0.05) and T2* in the left cuneus with headache frequency (p = 0.04).ConclusionsIron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Abnormal intrinsic functional hubs and connectivity in patients with post-stroke depression.

The present study aimed to investigate the specific alterations of brain networks in patients with post-stroke depression (PSD), and further assist in elucidating the brain mechanisms underlying the PSD which would provide supporting evidence for early diagnosis and interventions for the disease. Resting-state functional magnetic resonace imaging data were acquired from 82 nondepressed stroke patients (Stroke), 39 PSD patients, and 74 healthy controls (HC). Voxel-wise degree centrality (DC) conjoined with seed-based functional connectivity (FC) analyses were performed to investigate the PSD-related connectivity alterations. The relationship between these alterations and depression severity was further examined in PSD patients. Relative to both Stroke and HC groups, (1) PSD showed increased centrality in regions within the default mode network (DMN), including contralesional angular gyrus (ANG), posterior cingulate cortex (PCC), and hippocampus (HIP). DC values in contralesional ANG positively correlated with the Patient Health Questionnaire-9 (PHQ-9) scores in PSD group. (2) PSD exhibited increased connectivity between these three seeds showing altered DC and regions within the DMN: bilateral medial prefrontal cortex and middle temporal gyrus and ipsilesional superior parietal gyrus, and regions outside the DMN: bilateral calcarine, ipsilesional inferior occipital gyrus and contralesional lingual gyrus, while decreased connectivity between contralesional ANG and contralesional supramarginal gyrus. Moreover, these FC alterations could predict PHQ-9 scores in PSD group. These findings highlight that PSD was related with increased functional connectivity strength in some areas within the DMN, which might be attribute to the specific alterations of connectivity between within DMN and outside DMN regions in PSD.

Multimodal fusion of brain signals for robust prediction of psychosis transition

The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis

Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity “similarity index” for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.

A longitudinal evaluation of personalized intrinsic network topography and cognitive decline in Parkinson's disease.

Resting state functional magnetic resonance imaging (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both network-based statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison with the control group. Evaluation of mean connectivity between all regions of interest (ROIs) within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual and striatal networks and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and inter-network synchrony may support cognitive function in the face of PD-related network disruptions.

The influence of accelerated brain aging on coactivation pattern dynamics in Parkinson's disease.

Aging is widely acknowledged as the primary risk factor for brain degeneration, with Parkinson's disease (PD) tending to follow accelerated aging trajectories. We aim to investigate the impact of structural brain aging on the temporal dynamics of a large-scale functional network in PD. We enrolled 62 PD patients and 32 healthy controls (HCs). The level of brain aging was determined by calculating global and local brain age gap estimates (G-brainAGE and L-brainAGE) from structural images. The neural network activity of the whole brain was captured by identifying coactivation patterns (CAPs) from resting-state functional images. Intergroup differences were assessed using the general linear model. Subsequently, a spatial correlation analysis between the L-brainAGE difference map and CAPs was conducted to uncover the anatomical underpinnings of functional alterations. Compared to HCs (-3.73 years), G-brainAGE was significantly higher in PD patients (+1.93 years), who also exhibited widespread elevation in L-brainAGE. G-brainAGE was correlated with disease severity and duration. PD patients spent less time in CAPs involving activated default mode and the fronto-parietal network (DMN-FPN), as well as the sensorimotor and salience network (SMN-SN), and had a reduced transition frequency from other CAPs to the DMN-FPN and SMN-SN CAPs. Furthermore, the pattern of localized brain age acceleration showed spatial similarities with the SMN-SN CAP. Accelerated structural brain aging in PD adversely affects brain function, manifesting as dysregulated brain network dynamics. These findings provide insights into the neuropathological mechanisms underlying neurodegenerative diseases and imply the possibility of interventions for modifying PD progression by slowing the brain aging process.

Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease.

Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomicalmagnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (β = .837, r = 0.472, p < .001) and glucose covariance (β = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.

Location matters: altered interhemispheric homotopic connectivity in post-stroke dyskinesia.

Motor impairment is the most prevalent consequence following a stroke. Interhemispheric homotopic connectivity, which varies regionally and hierarchically along the axis of the somatomotor-association cortex, plays a critical role in sustaining normal motor functions. However, the impact of strokes occurring in various locations on homotopic connectivity is not fully understood. This study aimed to explore how motor deficits resulting from acute strokes in different locations influence homotopic connectivity. Eighty-four acute ischemic stroke patients with dyskinesia were recruited and divided into four demographically-matched subgroups based on stroke locations: Group 1 (G1; frontoparietal, n = 15), Group 2 (G2; radiation coronal, n = 16), Group 3 (G3; basal ganglia, n = 30), and Group 4 (G4; brain stem, n = 23). An additional 37 demographically-matched healthy controls were also recruited in the study. Multimodal MRI data, motor function assessments, and cognitive tests were gathered for analysis. Interhemispheric homotopic functional and structural connectivity were measured using resting-state functional MRI and diffusion tensor imaging, respectively. These measurements were then correlated with motor function scores to investigate the relationships. Voxel-mirrored homotopic connectivity (VMHC) analysis showed that strokes in the frontoparietal and basal ganglia regions led to diminished homotopic connectivity in the somatosensory/motor cortex. In contrast, strokes in the radiation coronal and brainstem regions affected subcortical motor circuits. Structural homotopic connectivity analysis using diffusion tensor imaging showed that frontoparietal and basal ganglia strokes predominantly affected association fibers, while radiation coronal and brainstem strokes caused widespread disruption in the integrity of both cortical-cortical and cortical-subcortical white matter fibers. Correlation analyses demonstrated significant associations between the Fugl-Meyer Assessment (FMA), Modified Barthel Index (MBI), and National Institutes of Health Stroke Scale (NIHSS) scores with the VMHC in the inferior temporal gyrus for G1 (G1; r = 0.838, p < 0.001; r = 0.793, p < 0.001; and r = -0.834, p < 0.001, respectively). No statistically significant associations were observed in Groups 2, 3, and 4. Our results suggest that motor deficits following strokes in various regions involve distinct pathways from cortical to subcortical areas. Alterations in lesion topography and regional functional homotopy provide new insights into the understanding of neural underpinnings of post-stroke dyskinesia.

Systematic review and meta-analysis: multimodal functional and anatomical neural alterations in autism spectrum disorder

BackgroundThis meta-analysis aimed to explore the most robust findings across numerous existing resting-state functional imaging and voxel-based morphometry (VBM) studies on the functional and structural brain alterations in individuals with autism spectrum disorder (ASD).MethodsA whole-brain voxel-wise meta-analysis was conducted to compare the differences in the intrinsic functional activity and gray matter volume (GMV) between individuals with ASD and typically developing individuals (TDs) using Seed-based d Mapping software.ResultsA total of 23 functional imaging studies (786 ASD, 710 TDs) and 52 VBM studies (1728 ASD, 1747 TDs) were included. Compared with TDs, individuals with ASD displayed resting-state functional decreases in the left insula (extending to left superior temporal gyrus [STG]), bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), left angular gyrus and right inferior temporal gyrus, as well as increases in the right supplementary motor area and precuneus. For VBM meta-analysis, individuals with ASD displayed decreased GMV in the ACC/mPFC and left cerebellum, and increased GMV in the left middle temporal gyrus (extending to the left insula and STG), bilateral olfactory cortex, and right precentral gyrus. Further, individuals with ASD displayed decreased resting-state functional activity and increased GMV in the left insula after overlapping the functional and structural differences.ConclusionsThe present multimodal meta-analysis demonstrated that ASD exhibited similar alterations in both function and structure of the insula and ACC/mPFC, and functional or structural alterations in the default mode network (DMN), primary motor and sensory regions. These findings contribute to further understanding of the pathophysiology of ASD.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Association between abnormal default mode network homogeneity and sleep disturbances in major depressive disorder

BackgroundSleep disturbance is a common comorbidity of major depressive disorder (MDD). However, network homogeneity (NH) changes of the default mode network (DMN) in MDD with sleep disturbances are unclear.AimsThe purpose...

Insula-cortico-subcortical networks predict interoceptive awareness and stress resilience

BackgroundInteroception, the neural sensing of visceral signals, and interoceptive awareness (IA), the conscious perception of interoception, are crucial for life survival functions and mental health. Resilience, the capacity to overcome adversity, has been associated with reduced interoceptive disturbances. Here, we sought evidence for our Insula Modular Active Control (IMAC) model that suggest that the insula, a brain region specialized in the processing of interoceptive information, realizes IA and contributes to resilience and mental health via cortico-subcortical connections. Methods64 healthy participants (32 females; ages 18–34 years) answered questionnaires that assess IA and resilience. Mental health was evaluated with the Beck Depression Inventory II that assesses depressive mood. Participants also underwent a 15 minute resting-state functional resonance imaging session. Pearson correlations and mediation analyses were used to investigate the relationship between IA and resilience and their contributions to depressive mood. We then performed insula seed-based functional connectivity analyzes to identify insula networks involved in IA, resilience and depressive mood. ResultsWe first demonstrated that resilience mediates the relationship between IA and depressive mood. Second, shared and distinct intra-insula, insula-cortical and insula-subcortical networks were associated with IA, resilience and also predicted the degree of experienced depressive mood. Third, while resilience was associated with stronger insula-precuneus, insula-cerebellum and insula-prefrontal networks, IA was linked with stronger intra-insula, insula-striatum and insula-motor networks. ConclusionsOur findings help understand the roles of insula-cortico-subcortical networks in IA and resilience. These results also highlight the potential use of insula networks as biomarkers for depression prediction.

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

Resting-state functional imaging predictors of cognitive impairment in multiple sclerosis

Resting-state functional imaging predictors of cognitive impairment in multiple sclerosis

The Role of the Human Cerebellum for Learning from and Processing of External Feedback in Non-Motor Learning: A Systematic Review

This review aimed to systematically identify and comprehensively review the role of the cerebellum in performance monitoring, focusing on learning from and on processing of external feedback in non-motor learning. While 1078 articles were screened for eligibility, ultimately 36 studies were included in which external feedback was delivered in cognitive tasks and which referenced the cerebellum. These included studies in patient populations with cerebellar damage and studies in healthy subjects applying neuroimaging. Learning performance in patients with different cerebellar diseases was heterogeneous, with only about half of all patients showing alterations. One patient study using EEG demonstrated that damage to the cerebellum was associated with altered neural processing of external feedback. Studies assessing brain activity with task-based fMRI or PET and one resting-state functional imaging study that investigated connectivity changes following feedback-based learning in healthy participants revealed involvement particularly of lateral and posterior cerebellar regions in processing of and learning from external feedback. Cerebellar involvement was found at different stages, e.g., during feedback anticipation and following the onset of the feedback stimuli, substantiating the cerebellum’s relevance for different aspects of performance monitoring such as feedback prediction. Future research will need to further elucidate precisely how, where, and when the cerebellum modulates the prediction and processing of external feedback information, which cerebellar subregions are particularly relevant, and to what extent cerebellar diseases alter these processes.

In patients with mild disability NMOSD: is the alteration in the cortical morphological or functional network topological properties more significant.

To assess the alteration of individual brain morphological and functional network topological properties and their clinical significance in patients with neuromyelitis optica spectrum disorder (NMOSD). Eighteen patients with NMOSD and twenty-two healthy controls (HCs) were included. The clinical assessment of NMOSD patients involved evaluations of disability status, cognitive function, and fatigue impact. For each participant, brain images, including high-resolution T1-weighted images for individual morphological brain networks (MBNs) and resting-state functional MR images for functional brain networks (FBNs) were obtained. Topological properties were calculated and compared for both MBNs and FBNs. Then, partial correlation analysis was performed to investigate the relationships between the altered network properties and clinical variables. Finally, the altered network topological properties were used to classify NMOSD patients from HCs and to analyses time- to-progression of the patients. The average Expanded Disability Status Scale score of NMOSD patients was 1.05 (range from 0 to 2), indicating mild disability. Compared to HCs, NMOSD patients exhibited a higher normalized characteristic path length (λ) in their MBNs (P = 0.0118, FDR corrected) but showed no significant differences in the global properties of FBNs (p: 0.405-0.488). Network-based statistical analysis revealed that MBNs had more significantly altered connections (P< 0.01, NBS corrected) than FBNs. Altered nodal properties of MBNs were correlated with disease duration or fatigue scores (P< 0.05/6 with Bonferroni correction). Using the altered nodal properties of MBNs, the accuracy of classification of NMOSD patients versus HCs was 96.4%, with a sensitivity of 93.3% and a specificity of 100%. This accuracy was better than that achieved using the altered nodal properties of FBNs. Nodal properties of MBN significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD. The results indicated that patients with mild disability NMOSD exhibited compensatory increases in local network properties to maintain overall stability. Furthermore, the alterations in the morphological network nodal properties of NMOSD patients not only had better relevance for clinical assessments compared with functional network nodal properties, but also exhibited predictive values of EDSS worsening.

Correlation between the changes of brain amplitude of low-frequency fluctuation and cognitive impairment in patients with neuropsychiatric lupus.

To explore the relationship between brain function changes and clinical serological indicators and behavioral cognitive assessment in patients with neuropsychiatric systemic lupus erythematosus (NPSLE), and understand the pathogenesis of NPSLE from the perspective of imaging. The resting-state functional imaging data, clinical serological, and behavioral cognitive assessment scores of 28 patients with NPSLE and 22 healthy controls (HC) were prospectively collected. The resting-state amplitude of low-frequency fluctuation (ALFF) values obtained from the analysis and processing were correlated with the serological data and behavioral cognitive assessment scores to determine the relationship between these data. The average age of the patients of the NPSLE group was older than that of the HC group; significant differences in education level, Auditory Verbal Learning Test Hua Shan Version (AVLT-H), and Trail Making Test scores were observed between the two groups. The NPSLE group demonstrated increased brain activity in the insula, precentral gyrus, and superior temporal gyrus, and decreased brain activity in the superior parietal gyrus. The ALFF value of the insula positively correlated with the Anti-β2gp1 antibody and negatively correlated with the anti-nucleosome antibody and the AVL-recall (RC) score. The ALFF of the precentral gyrus negatively correlated with the AVL-immediate recall (I). The ALFF value of the superior temporal gyrus negatively correlated with the AVL-RC score. The left superior parietal gyrus positively correlated with the c-reactive protein. The right superior parietal gyrus positively correlated with the System Lupus Erythematosus Disease Activity Index and negatively correlated with the AVL-I score. Patients with NPSLE show different brain activity changes in different brain regions, and the abnormal brain regions are correlated with certain lupus antibodies, inflammatory factors, and cognitive assessment, thereby suggesting that the correlation between the three could provide novel insights into the pathogenesis of NPSLE.

Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging.

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79-91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings.

Amygdala structure and function and its associations with social-emotional outcomes in a low-risk preterm sample

Amygdala atypical volume development and functional connectivity (FC) at small gestational ages (GA) have been found across childhood. This adult-oriented study assesses whether altered amygdala structure and function is present following low-risk preterm birth. T1-weighted and resting-state functional MRI images of 33 low-risk preterm (30–36 weeks’ GA) and 29 full-term (37–42 weeks’ GA) young adults of both sexes, aged between 20 and 32 years old, were analyzed using FreeSurfer (v6.0.0) and Coon Toolbox (v21.a). The social-emotional assessment included Happé’s Strange Stories Test, the Moral Judgment Test, Delay-Discounting Test, Adult Self Report, and Emotion Regulation Questionnaire. No differences were found in social-emotional outcomes or amygdala volumes between the groups. Low-risk preterm young adults showed increased FC between the left amygdala, right amygdala and medial frontal cortex (MedFC) (F = 9.89, p-FWE = 0.009) at cluster level compared to their full-term peers. However, significant results at connection level were not observed between left and right amygdala. Lastly, increased FC at cluster level between the right amygdala and MedFC, and left amygdala and MedFC, was related to better social-emotional outcomes only in low-risk preterm young adults (F = 6.60, p-FWE = 0.036) at cluster level. At connection level, in contrast, only right amygdala–MedFC increased FC was significantly associated with better social-emotional outcomes. This study reveals that low-risk prematurity does not have an effect on social-emotional outcomes or structural amygdala volumes during young adulthood. However, individuals who were considered to be at a lower risk of exhibiting neurodevelopmental alterations following preterm birth demonstrated increased FC between the left and right amygdala and MedFC.