Default mode network electrophysiological dynamics and causal role in creative thinking.

The default mode network (DMN) is a widely distributed, intrinsic brain network thought to play a crucial role in internally directed cognition. The present study employs stereo-EEG in 13 human patients, obtaining high resolution neural recordings across multiple canonical DMN regions during two processes that have been associated with creative thinking: spontaneous and divergent thought. We probe these two DMN-associated higher cognitive functions through mind wandering and alternate uses tasks, respectively. Our results reveal DMN recruitment during both tasks, as well as a task-specific dissociation in spatiotemporal response dynamics. When compared to the fronto-parietal network, DMN activity was characterized by a stronger increase in gamma band power (30-70 Hz) coupled with lower theta band power (4-8 Hz). The difference in activity between the two networks was especially strong during the mind wandering task. Within the DMN, we found that the tasks showed different dynamics, with the alternate uses task engaging the DMN more during the initial stage of the task, and mind wandering in the later stage. Gamma power changes were mainly driven by lateral DMN sites, while theta power displayed task-specific effects. During alternate uses task, theta changes did not show spatial differences within the DMN, while mind wandering was associated to an early lateral and late dorsomedial DMN engagement. Furthermore, causal manipulations of DMN regions using direct cortical stimulation preferentially decreased the originality of responses in the alternative uses task, without affecting fluency or mind wandering. Our results suggest that DMN activity is flexibly modulated as a function of specific cognitive processes and supports its causal role in divergent thinking. These findings shed light on the neural constructs supporting different forms of cognition and provide causal evidence for the role of DMN in the generation of original connections among concepts.

Intracranial stimulation and EEG feature analysis reveal affective salience network specialization.

Emotion is represented in limbic and prefrontal brain areas, herein termed the affective salience network (ASN). Within the ASN, there are substantial unknowns about how valence and emotional intensity are processed-specifically, which nodes are associated with affective bias (a phenomenon in which participants interpret emotions in a manner consistent with their own mood). A recently developed feature detection approach ('specparam') was used to select dominant spectral features from human intracranial electrophysiological data, revealing affective specialization within specific nodes of the ASN. Spectral analysis of dominant features at the channel level suggests that dorsal anterior cingulate (dACC), anterior insula and ventral-medial prefrontal cortex (vmPFC) are sensitive to valence and intensity, while the amygdala is primarily sensitive to intensity. Akaike information criterion model comparisons corroborated the spectral analysis findings, suggesting all four nodes are more sensitive to intensity compared to valence. The data also revealed that activity in dACC and vmPFC were predictive of the extent of affective bias in the ratings of facial expressions-a proxy measure of instantaneous mood. To examine causality of the dACC in affective experience, 130 Hz continuous stimulation was applied to dACC while patients viewed and rated emotional faces. Faces were rated significantly happier during stimulation, even after accounting for differences in baseline ratings. Together the data suggest a causal role for dACC during the processing of external affective stimuli.

Defining functional brain networks underlying obsessive-compulsive disorder (OCD) using treatment-induced neuroimaging changes: a systematic review of the literature.

Approximately 2%-3% of the population suffers from obsessive-compulsive disorder (OCD). Several brain regions have been implicated in the pathophysiology of OCD, but their various contributions remain unclear. We examined changes in structural and functional neuroimaging before and after a variety of therapeutic interventions as an index into identifying the underlying networks involved. We identified 64 studies from 1990 to 2020 comparing pretreatment and post-treatment imaging of patients with OCD, including metabolic and perfusion, neurochemical, structural, functional and connectivity-based modalities. Treatment class included pharmacotherapy, cognitive-behavioural therapy/exposure and response prevention, stereotactic lesions, deep brain stimulation and transcranial magnetic stimulation. Changes in several brain regions are consistent and correspond with treatment response despite the heterogeneity in treatments and neuroimaging modalities. Most notable are decreases in metabolism and perfusion of the caudate, anterior cingulate cortex, thalamus and regions of prefrontal cortex (PFC) including the orbitofrontal cortex (OFC), dorsolateral PFC (DLPFC), ventromedial PFC (VMPFC) and ventrolateral PFC (VLPFC). Modulating activity within regions of the cortico-striato-thalamo-cortical system may be a common therapeutic mechanism across treatments. We identify future needs and current knowledge gaps that can be mitigated by implementing integrative methods. Future studies should incorporate a systematic, analytical approach to testing objective correlates of treatment response to better understand neurophysiological mechanisms of dysfunction.

Direct electrical stimulation of the amygdala enhances declarative memory in humans.

Emotional events are often remembered better than neutral events, a benefit that many studies have hypothesized to depend on the amygdala's interactions with memory systems. These studies have indicated that the amygdala can modulate memory-consolidation processes in other brain regions such as the hippocampus and perirhinal cortex. Indeed, rodent studies have demonstrated that direct activation of the amygdala can enhance memory consolidation even during nonemotional events. However, the premise that the amygdala causally enhances declarative memory has not been directly tested in humans. Here we tested whether brief electrical stimulation to the amygdala could enhance declarative memory for specific images of neutral objects without eliciting a subjective emotional response. Fourteen epilepsy patients undergoing monitoring of seizures via intracranial depth electrodes viewed a series of neutral object images, half of which were immediately followed by brief, low-amplitude electrical stimulation to the amygdala. Amygdala stimulation elicited no subjective emotional response but led to reliably improved memory compared with control images when patients were given a recognition-memory test the next day. Neuronal oscillations in the amygdala, hippocampus, and perirhinal cortex during this next-day memory test indicated that a neural correlate of the memory enhancement was increased theta and gamma oscillatory interactions between these regions, consistent with the idea that the amygdala prioritizes consolidation by engaging other memory regions. These results show that the amygdala can initiate endogenous memory prioritization processes in the absence of emotional input, addressing a fundamental question and opening a path to future therapies.

Field potential 1/f activity in the subcallosal cingulate region as a candidate signal for monitoring deep brain stimulation for treatment-resistant depression.

Subcallosal cingulate cortex deep brain stimulation (SCC-DBS) is an experimental therapy for treatment-resistant depression (TRD). Refinement and optimization of SCC-DBS will benefit from increased study of SCC electrophysiology in context of ongoing high-frequency SCC-DBS therapy. The study objective was a 7-mo observation of frequency-domain 1/f slope in off-stimulation local field potentials (SCC-LFPs) alongside standardized measurements of depression severity in 4 patients undergoing SCC-DBS. SCC was implanted bilaterally with a combined neurostimulation-LFP recording system. Following a 1-mo off-stimulation postoperative phase with multiple daily recordings, patients received bilateral SCC-DBS therapy (130 Hz, 90 µs) and weekly resting-state SCC-LFP recordings over a 6-mo treatment phase. 1/f slopes for each time point were estimated via linear regression of log-transformed Welch periodograms. General linear mixed-effects models were constructed to estimate pretreatment sources of 1/f slope variance, and 95% bootstrap confidence intervals were constructed to estimate treatment phase 1/f slope association with treatment response (50% decrease in preimplantation symptom severity). Results show the time of recording was a prominent source of pretreatment 1/f slope variance bilaterally, with increased 1/f slope magnitude observed during night hours (2300-0659). Increase in right 1/f slope was observed in the setting of treatment response, with bootstrap analysis supporting this observation in 3 of 4 subjects. We conclude that 1/f slope can be measured longitudinally in a combined SCC-DBS/LFP recording system and likely conforms to known 1/f circadian variability. The preliminary evidence of 1/f slope increase during treatment response suggests a potential utility as a candidate biomarker for ongoing development of adaptive TRD-neuromodulation strategies.NEW & NOTEWORTHY In four patients with treatment-resistant depression undergoing therapeutic deep brain stimulation (DBS), we present the first longitudinal observations of local field potentials (LFP) from the subcallosal cingulate region outside the postoperative period. Specifically, our results demonstrate that frequency-domain 1/f activity is measurable in a combined DBS-LFP recording system and that right hemisphere recordings appear sensitive to mood state, thus suggesting a potential readout suitable for consideration in ongoing efforts to develop adaptive DBS delivery systems.

Test-retest reliability of a stimulation-locked evoked response to deep brain stimulation in subcallosal cingulate for treatment resistant depression.

Deep brain stimulation (DBS) to the subcallosal cingulate cortex (SCC) is an emerging therapy for treatment resistant depression. Precision targeting of specific white matter fibers is now central to the model of SCC DBS treatment efficacy. A method to confirm SCC DBS target engagement is needed to reduce procedural variance across treatment providers and to optimize DBS parameters for individual patients. We examined the reliability of a novel cortical evoked response that is time-locked to a 2 Hz DBS pulse and shows the propagation of signal from the DBS target. The evoked response was detected in four individuals as a stereotyped series of components within 150 ms of a 6 V DBS pulse, each showing coherent topography on the head surface. Test-retest reliability across four repeated measures over 14 months met or exceeded standards for valid test construction in three of four patients. Several observations in this pilot sample demonstrate the prospective utility of this method to confirm surgical target engagement and instruct parameter selection. The topography of an orbital frontal component on the head surface showed specificity for patterns of forceps minor activation, which may provide a means to confirm DBS location with respect to key white matter structures. A divergent cortical response to unilateral stimulation of left (vs. right) hemisphere underscores the need for feedback acuity on the level of a single electrode, despite bilateral presentation of therapeutic stimulation. Results demonstrate viability of this method to explore patient-specific cortical responsivity to DBS for brain-circuit pathologies.

Psychometric Properties of a Novel Affective Bias Task and Its Application in Clinical and Nonclinical Populations.

To mitigate limitations of self-reported mood assessments, we introduce a novel affective bias task. The task quantifies instantaneous emotional state by leveraging the phenomenon of affective bias, in which people interpret external emotional stimuli in a manner consistent with their current emotional state. This study establishes task stability in measuring and tracking depressive symptoms in clinical and nonclinical populations. Initial assessment in a large nonclinical sample established normative ratings. Depressive symptoms were measured and compared with task performance in a nonclinical sample, as well as in a clinical cohort of individuals who were undergoing surgical evaluation for severe epilepsy. In both cohorts, a stronger negative affective bias was associated with a higher Beck Depression Inventory-II score. The affective bias task exhibited high stability and interrater reliability as well as construct validity in predicting depression levels in both cohorts, suggesting that the task is a reliable proxy for mood and a diagnostic tool for detecting depressive symptoms.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

BACKGROUND: Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD: Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS: The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD: Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS: These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Proper reference selection and re-referencing to mitigate bias in single pulse electrical stimulation data.

Single pulse electrical stimulation experiments produce pulse-evoked potentials used to infer brain connectivity. The choice of recording reference for intracranial electrodes remains non-standardized and can significantly impact data interpretation. When the reference electrode is affected by stimulation or evoked brain activity, it can contaminate the pulse-evoked potentials recorded at all other electrodes and influence interpretation of findings. We highlight this specific issue in intracranial EEG datasets from two subjects recorded at separate institutions. We present several intuitive metrics to detect the presence of reference contamination and offer practical guidance on different mitigation strategies. Either switching the reference electrode or re-referencing to an adjusted common average effectively mitigated the reference contamination issue, as evidenced by increased variability in pulse-evoked potentials across the brain. Overall, we demonstrate the importance of clear quality checks and preprocessing steps that should be performed before analysis of single pulse electrical stimulation data. Highlights: A reference electrode close to active tissue can contaminate intracranial EEG signalsInterpretation of pulse-evoked potentials can be biased by reference contaminationLow response variability between channels is indicative of reference contaminationReference contamination can be resolved through data recollection or re-referencing.

Stereo-Electroencephalography-Guided Network Neuromodulation for Psychiatric Disorders: The Neurophysiology Monitoring Unit.

BACKGROUND AND OBJECTIVES: Recent advances in stereotactic and functional neurosurgery have brought forth the stereo-electroencephalography approach which allows deeper interrogation and characterization of the contributions of deep structures to neural and affective functioning. We argue that this approach can and should be brought to bear on the notoriously intractable issue of defining the pathophysiology of refractory psychiatric disorders and developing patient-specific optimized stimulation therapies. METHODS: We have developed a suite of methods for maximally leveraging the stereo-electroencephalography approach for an innovative application to understand affective disorders, with high translatability across the broader range of refractory neuropsychiatric conditions. RESULTS: This article provides a roadmap for determining desired electrode coverage, tracking high-resolution research recordings across a large number of electrodes, synchronizing intracranial signals with ongoing research tasks and other data streams, applying intracranial stimulation during recording, and design choices for patient comfort and safety. CONCLUSION: These methods can be implemented across other neuropsychiatric conditions needing intensive electrophysiological characterization to define biomarkers and more effectively guide therapeutic decision-making in cases of severe and treatment-refractory disease.

Beta activity in human anterior cingulate cortex mediates reward biases.

The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12-30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit.

Insula uses overlapping codes for emotion in self and others.

In daily life, we must recognize others' emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.

White matter fractional anisotropy is inversely related to anxious symptoms in older adults with atherosclerosis.

OBJECTIVE: Clinical anxiety disorders are associated with white matter hyperintensities and diffusion abnormalities measured using diffusion tensor imaging. However, it is not known if this association extends into individuals with mild anxious symptoms without formal diagnosis, in those who are older, or in those who have atherosclerosis. The current study explores whether white matter integrity and/or organization significantly associates with anxious symptoms in older adults with and without atherosclerosis. METHODS: We recruited older adults (ages 55-90 years); 35 with clinically diagnosed atherosclerotic vascular disease (AVD) and 22 without AVD. Anxious symptoms were measured using the validated Symptom Checklist-90-Revised. Fractional anisotropy (FA), a proxy for white matter organization and health, was measured in the white matter globally, by lobe, and in several smaller regions of interest suggested by the literature. Partial correlations between anxious symptoms and FA were calculated, controlling for significant covariates. RESULTS: Participants with and without AVD did not differ in severity of anxious symptom endorsement. There was a unique inverse relationship between white matter health and anxious symptoms in the AVD participants, but not in healthy comparisons. Significant relationships were observed in the superior longitudinal fasciculus (r = -0.476, df = 32, p = 0.004), as well as the cingulum bundle, the frontal lobes, and the parietal lobes. CONCLUSIONS: Anxiety symptoms uniquely correlated with low FA in older adults with atherosclerosis. These findings may have implications for future research on the topic of anxiety in aging and vascular disease and warrant replication.

Stereo-EEG-guided network modulation for psychiatric disorders: Interactive holographic planning.

BACKGROUND: Connectomic modeling studies are expanding understanding of the brain networks that are modulated by deep brain stimulation (DBS) therapies. However, explicit integration of these modeling results into prospective neurosurgical planning is only beginning to evolve. One challenge of employing connectomic models in patient-specific surgical planning is the inherent 3D nature of the results, which can make clinically useful data integration and visualization difficult. METHODS: We developed a holographic stereotactic neurosurgery research tool (HoloSNS) that integrates patient-specific brain models into a group-based visualization environment for interactive surgical planning using connectomic hypotheses. HoloSNS currently runs on the HoloLens 2 platform and it enables remote networking between headsets. This allowed us to perform surgical planning group meetings with study co-investigators distributed across the country. RESULTS: We used HoloSNS to plan stereo-EEG and DBS electrode placements for each patient participating in a clinical trial (NCT03437928) that is targeting both the subcallosal cingulate and ventral capsule for the treatment of depression. Each patient model consisted of multiple components of scientific data and anatomical reconstructions of the head and brain (both patient-specific and atlas-based), which far exceed the data integration capabilities of traditional neurosurgical planning workstations. This allowed us to prospectively discuss and evaluate the positioning of the electrodes based on novel connectomic hypotheses. CONCLUSIONS: The 3D nature of the surgical procedure, brain imaging data, and connectomic modeling results all highlighted the utility of employing holographic visualization to support the design of unique clinical experiments to explore brain network modulation with DBS.

Brain mechanisms underlying the emotion processing bias in treatment-resistant depression.

Depression is associated with a cognitive bias towards negative information and away from positive information. This biased emotion processing may underlie core depression symptoms, including persistent feelings of sadness or low mood and a reduced capacity to experience pleasure. The neural mechanisms responsible for this biased emotion processing remain unknown. Here, we had a unique opportunity to record stereotactic electroencephalography (sEEG) signals in the amygdala and prefrontal cortex (PFC) from 5 treatment-resistant depression (TRD) patients and 12 epilepsy patients (as control) while they participated in an affective bias task in which happy and sad faces were rated. First, compared with the control group, patients with TRD showed increased amygdala responses to sad faces in the early stage (around 300 ms) and decreased amygdala responses to happy faces in the late stage (around 600 ms) following the onset of faces. Further, during the late stage of happy face processing, alpha-band activity in PFC as well as alpha-phase locking between the amygdala and PFC were significantly greater in TRD patients compared to the controls. Second, after deep brain stimulation (DBS) delivered to bilateral subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS), atypical amygdala and PFC processing of happy faces in TRD patients remitted toward the normative pattern. The increased amygdala activation during the early stage of sad face processing suggests an overactive bottom-up processing system in TRD. Meanwhile, the reduced amygdala response during the late stage of happy face processing could be attributed to inhibition by PFC through alpha-band oscillation, which can be released by DBS in SCC and VC/VS.

Tractography-Based Modeling Explains Treatment Outcomes in Patients Undergoing Deep Brain Stimulation for Obsessive-Compulsive Disorder.

BACKGROUND: Deep brain stimulation (DBS) is an established and expanding therapy for treatment-refractory obsessive-compulsive disorder. Previous work has suggested that a white matter circuit providing hyperdirect input from the dorsal cingulate and ventrolateral prefrontal regions to the subthalamic nucleus could be an effective neuromodulatory target. METHODS: We tested this concept by attempting to retrospectively explain through predictive modeling the ranks of clinical improvement as measured by the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) in 10 patients with obsessive-compulsive disorder who underwent DBS to the ventral anterior limb of internal capsule with subsequent programming uninformed by the putative target tract. RESULTS: Rank predictions were carried out using the tract model by a team that was completely uninvolved in DBS planning and programming. Predicted Y-BOCS improvement ranks significantly correlated with actual Y-BOCS improvement ranks at the 6-month follow-up (r = 0.75, p = .013). Predicted score improvements correlated with actual Y-BOCS score improvements (r = 0.72, p = .018). CONCLUSIONS: Here, we provide data in a first-of-its-kind report suggesting that normative tractography-based modeling can blindly predict treatment response in DBS for obsessive-compulsive disorder.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

Background: Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method: Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results: The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods: Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions: These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Prefrontal network engagement by deep brain stimulation in limbic hubs.

Prefrontal circuits in the human brain play an important role in cognitive and affective processing. Neuromodulation therapies delivered to certain key hubs within these circuits are being used with increasing frequency to treat a host of neuropsychiatric disorders. However, the detailed neurophysiological effects of stimulation to these hubs are largely unknown. Here, we performed intracranial recordings across prefrontal networks while delivering electrical stimulation to two well-established white matter hubs involved in cognitive regulation and depression: the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS). We demonstrate a shared frontotemporal circuit consisting of the ventromedial prefrontal cortex, amygdala, and lateral orbitofrontal cortex where gamma oscillations are differentially modulated by stimulation target. Additionally, we found participant-specific responses to stimulation in the dorsal anterior cingulate cortex and demonstrate the capacity for further tuning of neural activity using current-steered stimulation. Our findings indicate a potential neurophysiological mechanism for the dissociable therapeutic effects seen across the SCC and VC/VS targets for psychiatric neuromodulation and our results lay the groundwork for personalized, network-guided neurostimulation therapy.

Decoding Depression Severity From Intracranial Neural Activity.

BACKGROUND: Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. METHODS: We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. RESULTS: Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. CONCLUSIONS: The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.

Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets.

Objective.Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians' experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms.Approach.This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described.Main results.Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P= 0.002).Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.