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Burgher, B.

Publications and source records attributed to Burgher, B..

2 recordsLinked to original sources

Anterior default mode brain state dynamics predict depressive symptom severity before and during TMS treatment

Transcranial magnetic stimulation (TMS) targeting the left dorsolateral prefrontal cortex is known to progressively reduce symptoms of depression. However, the neural mechanisms supporting this effect are poorly understood. To address this gap, we analysed longitudinal EEG recordings from 70 people undergoing TMS therapy and fitted an established dynamic network model of resting-state activity. Greater baseline symptom severity was associated with reduced occupancy of and fewer transitions into an anterior default mode brain state, alongside increased activity in a posterior default mode state. During treatment, decreases in anterior default mode state engagement following TMS predicted symptom improvement in the latter half of the intervention. Brain state activity exhibited structured, cyclical dynamics, with slower cycles linked to greater baseline severity. These findings suggest that symptoms of depression are characterised by gradual alterations in brain state dynamics, highlighting a central and dissociable role of default mode brain states in the persistence and remission of symptoms.

neuroscience↗

Impact of Censoring on the Quality of Cortical Parcellations and Personalized TMS Targets

Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized "ground-truth" references from [≥]1 hour of low-motion data per participant. We then simulate 10-min or 20-min rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 min under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to - or even better than - those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.

neuroscience↗