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Biomedical subjects

Adi Maron-Katz

Publications and source records attributed to Adi Maron-Katz.

4 recordsLinked to original sources

Neurophysiological signatures of Stanford Neuromodulation Therapy in treatment resistant depression.

Treatment-resistant depression (TRD) affects approximately 30% of patients with major depressive disorder. Stanford Neuromodulation Therapy (SNT), a high-dose intermittent theta-burst transcranial magnetic stimulation protocol, produces rapid antidepressant effects, but its neurophysiological mechanisms remain unclear. Here, we used longitudinal TMS-EEG to characterize the progressive neurophysiological changes induced by SNT, assess their site-specificity, and explore whether baseline neural markers are associated with clinical response. We conducted a double-blind, randomized, sham-controlled trial at Stanford University (2017-2018; analysis August 2024-October 2025) in 24 TMS-na&#xef;ve participants with TRD (Montgomery-&#xc5;sberg Depression Rating Scale &#x2265;20; &#x2265;1 failed antidepressant trial). Participants were randomized to active (n&#x2009;=&#x2009;12) or sham (n&#x2009;=&#x2009;12) SNT, consisting of 10 sessions per day over 5 consecutive days targeting the left dorsolateral prefrontal cortex (90,000 pulses). TMS-EEG was acquired at two baseline sessions, before and after each treatment session, and at 1-month follow-up (14 TMS-EEG sessions in total). Active SNT progressively reduced cortical excitability at the treatment site, with significant decreases by day 3 in the early window component (-27.9%; P&#x2009;<&#x2009;0.01), while no changes were observed at the vertex control site. Site-specific comparisons confirmed early window reductions only at the left dorsolateral prefrontal cortex (t&#x2082;&#x2082; = -3.82; P&#x2009;<&#x2009;0.001). SNT also selectively decreased estimated medial prefrontal source activity consistent with the subgenual anterior cingulate cortex (sgACC) across sessions (F&#x2081;&#x2083;,&#x2082;&#x2082;&#x2082; = 4.93; P&#x2009;<&#x2009;0.001), with effects persisting at 1-month follow-up. In an exploratory analysis in the active group (n&#x2009;=&#x2009;12), higher baseline estimated sgACC source activity was associated with greater clinical improvement (r = -0.67; P&#x2009;=&#x2009;0.023); although promising, the latter preliminary finding requires replication in larger, adequately powered samples before predictive utility can be established. These findings indicate that SNT induces progressive, site-specific cortical modulation and selective downstream effects on estimated sgACC source activity. Early cortical excitability changes represent candidate neurophysiological markers of SNT response, while the observed association between baseline sgACC activity and clinical outcome, while preliminary, motivates prospective investigation of subcortical source activity as a potential predictor of treatment response in larger trials. ClinicalTrials.gov Identifier: NCT03068715.

Journal Article↗

A whole-brain voxel-based analysis of structural abnormalities in PTSD: An ENIGMA-PGC study.

BACKGROUND: Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group. METHODS: T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n&#xa0;=&#xa0;1309; controls n&#xa0;=&#xa0;2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9). RESULTS: PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges' g&#xa0;=&#xa0;0.22, pcorrected&#xa0;=&#xa0;.001), and smaller cerebellar WM volume (peak Hedges' g&#xa0;=&#xa0;0.14, pcorrected&#xa0;=&#xa0;.008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (p corrected &#xa0;=&#xa0;.003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (p corrected &#xa0;=&#xa0;.001). CONCLUSIONS: PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

Humans↗

EXPANDER--an integrative program suite for microarray data analysis.

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at http://www.cs.tau.ac.il/~rshamir/expander.

Algorithms↗

CLICK and EXPANDER: a system for clustering and visualizing gene expression data.

MOTIVATION: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY: http://www.cs.tau.ac.il/~rshamir/expander/expander.html

Algorithms↗