Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project) DOI Creative Commons
Mauro Pettorruso, Giorgio Di Lorenzo, Beatrice Benatti

et al.

Frontiers in Psychiatry, Journal Year: 2024, Volume and Issue: 15

Published: July 17, 2024

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD’s clinical manifestations neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide choices TRD, herein we introduce SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) conducting preliminary validation 2/WP2) computational tool (SelecTool) that integrates data, neurophysiological (EEG) peripheral (blood sample) through machine-learning framework designed optimize TRD protocols. project aims enhance decision-making by enabling selection personalized It leverages multi-modal data analysis navigate towards two validated therapeutic options TRD: esketamine nasal spray (ESK-NS) accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with will be randomized receive either ESK-NS or arTMS, comprehensive evaluations encompassing (EEG), (psychometric scales), samples) assessments both at baseline (T0) one month post-treatment initiation (T1). WP2 utilize collected WP1 train algorithm, followed its application second, out-of-sample cohort 20 subjects, assigning treatments based tool’s recommendations. Ultimately, this seeks revolutionize employing advanced machine learning strategies thorough analysis, aimed unraveling complex landscape depression. effort is expected provide pivotal insights promote development more individually tailored strategies, thus addressing significant void current management potentially reducing profound societal burdens.

Language: Английский

Anhedonia relates to reduced striatal reward anticipation in depression but not in schizophrenia or bipolar disorder: A transdiagnostic study DOI Creative Commons
Anna Daniels, Sarah A. Wellan, Anne Beck

et al.

Cognitive Affective & Behavioral Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

Citations

1
Mild motor signs and depression: more than just medication side effects? DOI Creative Commons
Antonina Luca, Maria Luca, Siegfried Kasper

et al.

European Archives of Psychiatry and Clinical Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

The relationship between major depressive disorder (MDD) and mild motor signs (MMS) remains to be elucidated. present study aims assess the association neurological symptoms medications treatment response. Neurological in 790 patients with MDD were correlated outcome. Three hundred ten (39.2%) responders 480 (60.8%) non-responders. 342 (43.3%) presented signs. In whole sample negative associations dystonia rigidity various was observed. Non-response associated dystonia, rigidity, hypokinesia independent from age medications. This highlighted an MMS specific Moreover, non-response treatment, regardless of medication use. may suggest that a subgroup respond less therapy because underlying still undetected disorder.

Language: Английский

Citations

0
Neural Functioning in Late-Life Depression: An Activation Likelihood Estimation Meta-Analysis DOI Creative Commons
Antonio Del Casale,

Serena Mancino,

Jan Francesco Arena

et al.

Geriatrics, Journal Year: 2024, Volume and Issue: 9(4), P. 87 - 87

Published: June 25, 2024

Late-life depression (LLD) is a relatively common and debilitating mental disorder, also associated with cognitive dysfunctions an increased risk of mortality. Considering the growing elderly population worldwide, LLD increasingly emerging as significant public health issue, due to rise in direct indirect costs borne by healthcare systems. Understanding neuroanatomical neurofunctional correlates crucial for developing more targeted effective interventions, both from preventive therapeutic standpoint. This ALE meta-analysis aims evaluate involvement specific changes neurophysiopathology analysing functional neuroimaging studies conducted on patients compared healthy subjects (HCs). We included 19 844 subjects, divided into 439 405 HCs. Patients LLD, HCs, showed hypoactivation right superior medial frontal gyri (Brodmann areas (Bas) 8, 9), left cingulate cortex (BA 24), putamen, caudate body. The same exhibited hyperactivation temporal gyrus 42), inferior 45), anterior cerebellar culmen, declive. In summary, we found activation patterns brain functioning encompassed cortico–limbic–striatal network LLD. Furthermore, our results suggest potential role within cortico–striatal–cerebellar

Language: Английский

Citations

1
Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project) DOI Creative Commons
Mauro Pettorruso, Giorgio Di Lorenzo, Beatrice Benatti

et al.

Frontiers in Psychiatry, Journal Year: 2024, Volume and Issue: 15

Published: July 17, 2024

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD’s clinical manifestations neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide choices TRD, herein we introduce SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) conducting preliminary validation 2/WP2) computational tool (SelecTool) that integrates data, neurophysiological (EEG) peripheral (blood sample) through machine-learning framework designed optimize TRD protocols. project aims enhance decision-making by enabling selection personalized It leverages multi-modal data analysis navigate towards two validated therapeutic options TRD: esketamine nasal spray (ESK-NS) accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with will be randomized receive either ESK-NS or arTMS, comprehensive evaluations encompassing (EEG), (psychometric scales), samples) assessments both at baseline (T0) one month post-treatment initiation (T1). WP2 utilize collected WP1 train algorithm, followed its application second, out-of-sample cohort 20 subjects, assigning treatments based tool’s recommendations. Ultimately, this seeks revolutionize employing advanced machine learning strategies thorough analysis, aimed unraveling complex landscape depression. effort is expected provide pivotal insights promote development more individually tailored strategies, thus addressing significant void current management potentially reducing profound societal burdens.

Language: Английский

Citations

1