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Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD) 1 . However, achieving stable recovery is unpredictable 2 , typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting 3 . We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment., (© 2023. The Author(s).)

Aberrant pauses are characteristic of schizophrenia and are robustly associated with its negative symptoms. Here, we found that pause behavior was associated with negative symptoms in individuals at clinical high risk (CHR) for psychosis, and with measures of syntactic complexity-phrase length and usage of determiners that introduce clauses-that we previously showed in this same CHR cohort to help comprise a classifier that predicted psychosis. These findings suggest a common impairment in discourse planning and verbal self-monitoring that affects both speech and language, and which is detected in clinical ratings of negative symptoms.

People with Parkinson's (PWP) disease are under constant tension with respect to their dopamine replacement therapy (DRT) regimen. Waiting too long between doses results in more prominent symptoms, loss of motor function, and greater risk of falling per step. Shortened pill cycles can lead to accelerated habituation and faster development of disabling dyskinesias. The Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard for monitoring Parkinson's disease progression but requires a neurologist to administer and therefore is not an ideal instrument to continuously evaluate short-term disease fluctuations. We investigated the feasibility of using speech to detect changes in medication states, based on expectations of subtle changes in voice and content related to dopaminergic levels. We calculated acoustic and prosodic features for three speech tasks (picture description, reverse counting, and diadochokinetic rate) for 25 PWP, each evaluated "ON" and "OFF" DRT. Additionally, we generated semantic features for the picture description task. Classification of ON/OFF medication states using features generated from picture description, reverse counting and diadochokinetic rate tasks resulted in cross-validated accuracy rates of 0.89, 0.84, and 0.60, respectively. The most discriminating task was picture description which provided evidence that participants are more likely to use action words in ON than in OFF state. We also found that speech tempo was modified by DRT. Our results suggest that automatic speech assessment can capture changes associated with the DRT cycle. Given the ease of acquiring speech data, this method shows promise to remotely monitor DRT effects., Competing Interests: Competing interestsR. Norel, C. Agurto, S. Heisig, J.J. Rice, R. Ostrand, G.A. Cecchi disclose that their employer, IBM Research, is the research branch of IBM Corporation. R. Norel, G.A. Cecchi, S. Heisig own stock in IBM Corporation. H. Zhang, P.W. Wacnik, V.L. Ramos disclose that their employer, Pfizer Digital Medicine & Translational Imaging: Early Clinical Development, is a research branch of Pfizer Corporation. B.K. Ho is employed by the Department of Neurology, Tufts University School of Medicine and Tufts Medical Center., (© The Author(s) 2020.)

Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.