Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.

Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.

The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.

Dementia Subtypes Defined Through Neuropsychiatric Symptom-Associated Brain Connectivity Patterns.

Importance: Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia. Objective: To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes. Design, Setting, and Participants: Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited. Main Outcomes and Measures: A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined. Results: Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time × subtype 3 interaction item as the reference level: ß = 0.05; t = 2.6 for time × subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI. Conclusions and Relevance: These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.

Symptom dimensions of resting-state electroencephalographic functional connectivity in autism.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.

Early prediction of dementia using fMRI data with a graph convolutional network approach.

Objective. Alzheimer's disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at:https://github.com/Shuning-Han/FC-based-GCN.

Delineating Transdiagnostic Subtypes in Neurodevelopmental Disorders via Contrastive Graph Machine Learning of Brain Connectivity Patterns.

Neurodevelopmental disorders, such as Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are characterized by comorbidity and heterogeneity. Identifying distinct subtypes within these disorders can illuminate the underlying neurobiological and clinical characteristics, paving the way for more tailored treatments. We adopted a novel transdiagnostic approach across ADHD and ASD, using cutting-edge contrastive graph machine learning to determine subtypes based on brain network connectivity as revealed by resting-state functional magnetic resonance imaging. Our approach identified two generalizable subtypes characterized by robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the somatomotor network. These subtypes exhibited pronounced differences in major cognitive and behavioural measures. We further demonstrated the generalizability of these subtypes using data collected from independent study sites. Our data-driven approach provides a novel solution for parsing biological heterogeneity in neurodevelopmental disorders.

Multiband EEG signature decoded using machine learning for predicting rTMS treatment response in major depression.

Major depressive disorder (MDD) is a global health challenge with high prevalence. Further, many diagnosed with MDD are treatment resistant to traditional antidepressants. Repetitive transcranial magnetic stimulation (rTMS) offers promise as an alternative solution, but identifying objective biomarkers for predicting treatment response remains underexplored. Electroencephalographic (EEG) recordings are a cost-effective neuroimaging approach, but traditional EEG analysis methods often do not consider patient-specific variations and fail to capture complex neuronal dynamics. To address this, we propose a data-driven approach combining iterated masking empirical mode decomposition (itEMD) and sparse Bayesian learning (SBL). Our results demonstrated significant prediction of rTMS outcomes using this approach (Protocol 1: r=0.40, p<0.01; Protocol 2: r=0.26, p<0.05). From the decomposition, we obtained three key oscillations: IMF-Alpha, IMF-Beta, and the remaining residue. We also identified key spatial patterns associated with treatment outcomes for two rTMS protocols: for Protocol 1 (10Hz left DLPFC), important areas include the left frontal and parietal regions, while for Protocol 2 (1Hz right DLPFC), the left and frontal, left parietal regions are crucial. Additionally, our exploratory analysis found few significant correlations between oscillation specific predictive features and personality measures. This study highlights the potential of machine learning-driven EEG analysis for personalized MDD treatment prediction, offering a pathway for improved patient outcomes.

Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response.

Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

Elucidating the neuropathological and molecular heterogeneity of amyloid-beta and tau in Alzheimer's disease through machine learning and transcriptomic integration.

Discerning functional brain network variations related to neuropathological aggregates in Alzheimer's disease (AD), including amyloid-beta (Abeta) and phosphorylated tau (p-tau), is crucial for understanding their link to cognitive decline and underlying molecular mechanisms. However, these variations are often confounded by normal aging-related changes, complicating interpretation. To address this challenge, we first defined Alzheimer's continuum cases (Abeta positive (A+), n = 129) and normal elderly (Abeta negative (A-), n = 160) using cerebral spinal fluid amyloid levels, and then applied a novel deep learning approach to resting-state connectivity using functional magnetic resonance imaging (fMRI) of the 289 subjects to disentangle A+-specific dimensions in brain network alterations from those shared with A- individuals. The identified A+-specific dimensions were further refined to predict individual Abeta and p-tau levels separately. We observed that resulting brain signatures, defined from A+-specific dimensions for predicting these two CSF biomarkers, were both attributed to the right superior temporal and anterior cingulate cortices and associated with attention and memory domains. When linking the brain signatures to gene expression data from a public transcriptomic atlas, we found that the brain signatures were associated with molecular pathways involving synaptic dysfunction and disruptions in pathways containing activity of excitatory neurons, astrocytes, and microglia. For A--shared dimensions, the Aß-linked brain signature involved the left fusiform and right middle cingulate cortices, correlating with the language cognitive measurement and language-related molecular pathways. The p-tau-linked signature predominantly involved the right insula and inferior temporal cortices, correlating with the aging-related molecular pathways. Collectively, our findings provided new insights in understanding of Alzheimer's continuum pathological biomarkers.

Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns.

BACKGROUND: Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODS: Using large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes. RESULTS: We identified two reliable neuropsychiatric subsyndromes - behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction. CONCLUSIONS: Our findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients.

Dissecting Symptom-linked Dimensions of Resting-State Electroencephalographic Functional Connectivity in Autism with Contrastive Learning.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social interaction deficits, communication difficulties, and restricted/repetitive behaviors or fixated interests. Despite its high prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To collectively dissect the ASD heterogeneity in neurophysiology and symptoms, we develop a new analytical framework combining contrastive learning and sparse canonical correlation analysis to identify resting-state EEG connectivity dimensions linked to ASD behavioral symptoms within 392 ASD samples. Two dimensions are successfully identified, showing significant correlations with social/communication deficits (r = 0.70) and restricted/repetitive behaviors (r = 0.45), respectively. We confirm the robustness of these dimensions through cross-validation and further demonstrate their generalizability using an independent dataset of 223 ASD samples. Our results reveal that the right inferior parietal lobe is the core region displaying EEG activity associated with restricted/repetitive behaviors, and functional connectivity between the left angular gyrus and the right middle temporal gyrus is a promising biomarker of social/communication deficits. Overall, these findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for treatment development and precision medicine for ASD.

A generalizable functional connectivity signature characterizes brain dysfunction and links to rTMS treatment response in cocaine use disorder.

Cocaine use disorder (CUD) is a prevalent substance abuse disorder, and repetitive transcranial magnetic stimulation (rTMS) has shown potential in reducing cocaine cravings. However, a robust and replicable biomarker for CUD phenotyping is lacking, and the association between CUD brain phenotypes and treatment response remains unclear. Our study successfully established a cross-validated functional connectivity signature for accurate CUD phenotyping, using resting-state functional magnetic resonance imaging from a discovery cohort, and demonstrated its generalizability in an independent replication cohort. We identified phenotyping FCs involving increased connectivity between the visual network and dorsal attention network, and between the frontoparietal control network and ventral attention network, as well as decreased connectivity between the default mode network and limbic network in CUD patients compared to healthy controls. These abnormal connections correlated significantly with other drug use history and cognitive dysfunctions, e.g., non-planning impulsivity. We further confirmed the prognostic potential of the identified discriminative FCs for rTMS treatment response in CUD patients and found that the treatment-predictive FCs mainly involved the frontoparietal control and default mode networks. Our findings provide new insights into the neurobiological mechanisms of CUD and the association between CUD phenotypes and rTMS treatment response, offering promising targets for future therapeutic development.

Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.

Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.