Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder

Cognition-related connectome gradient dysfunctions of thalamus and basal ganglia in drug-naïve first-episode major depressive disorder

BACKGROUND

Subcortical functional abnormalities are believed to contribute to clinical symptoms and cognitive impairments in major depressive disorder (MDD). By introducing functional gradient mapping, the present study evaluated subcortical gradients in MDD patients and their association with cognitive features.

METHODS

Organization patterns and between-group differences in the principal subcortical gradient were investigated in 145 never-treated first-episode MDD patients and 145 healthy controls (HCs) across limbic, thalamic, and basal ganglia (BG) systems and their structural and functional subregions. We also assessed the associations between significant gradient alterations and clinical characteristics and neuropsychological functioning.

RESULTS

Overall, MDD patients showed a relatively compressed and disturbed gradient organization than HCs, with limbic and BG regions located at the two extreme ends of the principal gradient. Specifically, MDD patients had lower principal gradient values in thalamus and limbic system but higher values in BG than HCs. These gradient alterations, associated with intrinsic Euclidian distance and functional connectivity patterns, manifested as spatial rearrangements of gradient values within each respective subregion. Lower gradient values in thalamic subregion projecting to default mode network were associated with higher principal gradient values in BG subregion projecting to ventral attention network, and these gradient alterations were correlated with poorer episodic memory performance in MDD patients.

LIMITATIONS

The specific neuropathological mechanisms driving the gradient alterations still require further investigation.

CONCLUSIONS

Opposing gradient alterations in the thalamic and BG regions synergistically impact episodic memory performance in MDD, revealing an internally differentiated and cognition related pattern of subcortical gradient dysfunction in MDD.

Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients

Background

Antipsychotic medications offer limited long-term benefit to about 30% of patients with schizophrenia. We aimed to explore the individual-specific imaging markers to predict 1-year treatment response of schizophrenia.

Methods

Structural morphology and functional topological features related to treatment response were identified using an individualized parcellation analysis in conjunction with machine learning (ML). We performed dimensionality reductions using the Pearson correlation coefficient and three feature selection analyses and classifications using 10 ML classifiers. The results were assessed through a 5-fold cross-validation (training and validation cohorts, n = 51) and validated using the external test cohort (n = 17).

Results

ML algorithms based on individual-specific brain network proved more effective than those based on group-level brain network in predicting outcomes. The most predictive features based on individual-specific parcellation involved the GMV of the default network and the degree of the control, limbic, and default networks. The AUCs for the training, validation, and test cohorts were 0.947, 0.939, and 0.883, respectively. Additionally, the prediction performance of the models constructed by the different feature selection methods and classifiers showed no significant differences.

Conclusion

Our study highlighted the potential of individual-specific network parcellation in treatment resistant schizophrenia prediction and underscored the crucial role of feature attributes in predictive model accuracy.

Compensatory increase in ipsilesional supplementary motor area and premotor connectivity is associated with greater gait impairments: a personalized fMRI analysis in chronic stroke

Background

Balance and mobility impairments are prevalent post-stroke and a large number of survivors require walking assistance at 6 months post-stroke which diminishes their overall quality of life. Personalized interventions for gait and balance rehabilitation are crucial. Recent evidence indicates that stroke lesions in primary motor pathways, such as corticoreticular pathways (CRP) and corticospinal tract (CST), may lead to reliance on alternate motor pathways as compensation, but the current evidence lacks comprehensive knowledge about the underlying neural mechanisms.

Methods

In this study, we investigate the functional connectivity (FC) changes within the motor network derived from an individualized cortical parcellation approach in 33 participants with chronic stroke compared to 17 healthy controls. The correlations between altered motor FC and gait deficits (i.e., walking speed and walking balance) were then estimated in the stroke population to understand the compensation mechanism of the motor network in motor function rehabilitation post-stroke.

Results

Our results demonstrated significant FC increases between ipsilesional medial supplementary motor area (SMA) and premotor in stroke compared to healthy controls. Furthermore, we also revealed a negative correlation between ipsilesional SMA-premotor FC and self-selected walking speed, as well as the Functional Gait Assessment (FGA) scores.

Conclusion

The increased FC between the ipsilesional SMA and premotor regions could be a compensatory mechanism within the motor network following a stroke when the individual can presumably no longer rely on the more precise CST modulation of movements to produce a healthy walking pattern. These findings enhance our understanding of individualized motor network FC changes and their connection to gait and walking balance impairments post-stroke, improving stroke rehabilitation interventions.

Recent advances in psychoradiology

Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brainin vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.