Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Neuropsychopharmacology, Journal Year: 2024, Volume and Issue: 50(1), P. 230 - 245

Published: July 1, 2024

Language: Английский

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Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity

Journal of Magnetic Resonance Imaging, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Background Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting specificity individual level. Recently, there has been a growing interest in differences brain connectivity. Investigating individual‐specific connectivity is important for understanding mechanisms of major depressive disorder (MDD) and variations among individuals. Purpose To integrate individualized functional structural with machine learning techniques to distinguish people MDD healthy controls (HCs). Study Type Prospective. Subjects A total 182 patients 157 HCs verification cohort including 54 46 HCs. Field Strength/Sequence 3.0 T/T1‐weighted imaging, resting‐state MRI echo‐planar sequence, diffusion tensor imaging single‐shot spin echo. Assessment Functional networks from rs‐fMRI DTI data were constructed, respectively. Based on these networks, (IFC) (ISC) extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation combined joint independent component (mCCA + jICA) was conducted fusion identify unique components (ICs) across multiple modes. These ICs utilized generate features, support vector (SVM) model implemented classification MDD. Statistical Tests The between compared two‐sample t test, significance threshold set P < 0.05. established tested evaluated receiver operating characteristic (ROC) curve. Results performance constructed feature after multisequence increased 72.2% 90.3%. Furthermore, prediction showed significant predictive power assessing severity depression ( r = 0.544). Data Conclusion integration IFC ISC through enhances our capacity MDD, highlighting advantages approach underscoring its research. Level Evidence 1 Technical Efficacy Stage 2

Language: Английский

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Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging

Translational Psychiatry, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 7, 2024

People affected by psychotic, depressive and developmental disorders are at a higher risk for alcohol tobacco use. However, the further associations between alcohol/tobacco use symptoms/cognition in these remain unexplored. We identified multimodal brain networks involving (n = 707) 281) via supervised fusion evaluated if symptoms cognition people with psychotic (schizophrenia/schizoaffective disorder/bipolar, n 178/134/143), (major disorder, 260) (autism spectrum disorder/attention deficit hyperactivity 421/346) disorders. Alcohol scores were used as references to guide functional structural imaging identify associated patterns. Correlation analyses extracted features or performed evaluate relationships 6 psychiatric Results showed that (1) default mode network (DMN) salience (SN) use, whereas DMN fronto-limbic (FLN) use; (2) fronto-basal ganglia (FBG) related correlated symptom psychosis; (3) middle temporal cortex was depression; (4) symptom, SN limbic system (LB) In summary, abnormalities DMN, FLN had significant likely different networks. Further understanding of may assist clinicians development future approaches improve among

Language: Английский

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

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: April 26, 2023

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

Language: Английский

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

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 12, 2024

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages demographic groups. Unfortunately, current antidepressant psychotherapeutic treatments exhibit limited efficacy unsatisfactory response rates in substantial number patients. The development effective therapies MDD hindered by the insufficiently understood heterogeneity within its elusive underlying mechanisms. To address these challenges, we present target-oriented multimodal fusion framework robustly predicts integrating structural functional connectivity data (sertraline: R

Language: Английский

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Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2023, Volume and Issue: unknown

Published: July 6, 2023

By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, medical history, are combined via feature fusion the proposed technique. As result, model developed can accurately forecast that occur efficiency of suggested strategy is shown by experimental findings. performance greatly enhanced when characteristics opposed to just one source. findings have important ramifications increasing accuracy stroke diagnosis treatment and, eventually, bettering patient outcomes. results showed high level after was much combining using only these sources. This emphasizes value including pertinent management stroke.

Language: Английский

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Neural correlates of treatment response to ketamine for treatment-resistant depression: A systematic review of MRI-based studies

Psychiatry Research, Journal Year: 2024, Volume and Issue: 340, P. 116092 - 116092

Published: July 27, 2024

Treatment-resistant depression (TRD) is defined as patients diagnosed with having a history of failure different antidepressants an adequate dosage and treatment duration. The NMDA receptor antagonist ketamine rapidly reduces depressive symptoms in TRD. We examined neural correlates response to TRD through systematic review brain magnetic resonance imaging (MRI) studies. A comprehensive search PubMed was performed using "ketamine AND resonance." time span for the database queries "Start date: 2018/01/01; End 2024/05/31." Total 41 original articles comprising 1,396 587 healthy controls (HC) were included. Diagnosis made Structured Clinical Interview DSM Disorders (SCID), Mini-International Neuropsychiatric (MINI), and/or clinical assessment by psychiatrists. Patients affective psychotic disorders excluded. Most studies applied [0.5mg/kg racemic 0.25mg/kg S-ketamine] diluted 60cc normal saline via intravenous infusion over 40 min one time, four times, or six times spaced 2–3 days apart 2 weeks. outcome either remission, response, percentage changes symptoms. Brain MRI T2*-weighted (resting-state task performance), arterial spin labeling, diffusion weighted imaging, T1-weighted acquired at baseline mainly 1–3days after administration. Only study results replicated ≥ included default-mode, salience, fronto-parietal, subcortical, limbic networks regarded meaningful. Putative brain-based markers found structural/functional features (subgenual ACC, hippocampus, cingulum bundle-hippocampal portion; anhedonia/suicidal ideation), salience (dorsal insula, bundle-cingulate gyrus thought rumination/suicidal fronto-parietal (dorsolateral prefrontal cortex, superior longitudinal fasciculus; default-mode (posterior cingulate cortex; rumination), subcortical (striatum; anhedonia/thought rumination) networks. limbic, could be useful predicting better relief anhedonia, rumination, suicidal ideation.

Language: Английский

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

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

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

Language: Английский

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Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102723 - 102723

Published: Sept. 1, 2024

Language: Английский

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Characteristics of Functional Connections and Topographical Properties Distinguished the Healthy State and Obsessive-Compulsive Disorder

Archives of Neuroscience, Journal Year: 2024, Volume and Issue: 11(4)

Published: Oct. 22, 2024

Background: Obsessive-compulsive disorder (OCD) is characterized by alterations in brain connectivity, particularly within the default mode network (DMN) and salience (SN). Investigating these connectivity differences can provide a deeper understanding of neural mechanisms underlying OCD. Methods: This cross-sectional study involved 58 patients diagnosed with OCD 38 healthy control subjects, totaling 96 participants. Resting-state functional MRI (fMRI) data were acquired analyzed using CONN toolbox to examine intrinsic resting-state networks. Graph theory metrics applied evaluate node connections overall topology. Clinical symptoms assessed Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), their correlations patterns graph-theory parameters analyzed. Results: The controls matched terms age, gender, marital status, socioeconomic handedness. However, had significantly worse general health, quality life, higher levels depression anxiety. Network analyses revealed altered whole-brain patients, DMN frontoparietal network. most significant between-group observed between posterior parietal cortex (PPC) precuneus. Disruptions DMN, specifically medial prefrontal cingulate cortex, changes SN involving anterior insula correlated severity symptoms. Conclusions: findings suggest that associated distinct which may play critical role disorder's pathophysiology. These disruptions offer potential targets for therapeutic intervention. Further research needed explore larger cohorts at various stages better understand clinical significance.

Language: Английский

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