Different patterns of local field potentials from limbic DBS targets in patients with major depressive and obsessive compulsive disorder

Unser Verständnis über die Regulation von Emotionen in limbischen Hirnarealen wurde bisher hauptsächlich aus nicht-invasiven bildgebenden Verfahren gewonnen. Die Wiederentdeckung der funktionellen Neurochirurgie als therapeutisches Verfahren in der Behandlung psychiatrischer Erkrankungen wie der therapierefraktären Depression (TRD) ermöglicht durch Implantation von tiefen Hirnelektroden die perioperative Ableitung subkortikaler Nervenzellpopulationsaktivität. In dieser Studie untersuchen wir die Hypothese, dass charakteristische oszillatorische Muster lokaler Feldpotentiale (LFP) im menschlichen limbischen System die Symptomschwere der Depression reflektieren. Über die zur tiefen Hirnstimulation implantierten Elektroden wurden LFPs aus dem subgenualem cingulären Kortex bei 7 Patienten mit Depression (Brodmann Area 25; CG25; 7 TRD Patienten) und bei weiteren 7 Patienten mit Depression aus dem Nucleus striae terminalis (BNST, 7 TRD Patienten) abgeleitet und mit LFPs aus dem BNST von 5 Patienten mit Zwangsstörung verglichen. Im direkten Vergleich zeigten die Patienten mit Depression signifikant höhere oszillatorische Alpha-Aktivität im BNST (8-14 Hz;p<0.01) als Patienten mit Zwangserkrankung. Die im CG25 Zielgebiet operierten Depressionspatienten zeigten ein ähnliches oszillatorisches Muster wie die TRD-BNST Patienten. Die mittlere Alpha-Aktivität korrelierte signifikant mit den depressiven Symptomen der Patienten (Beck Depressions Inventar; n=14, r=0.55, p = 0.042), nicht aber mit den Symptomen der Zwangsstörung. Unsere Ergebnisse deuten darauf hin, dass Alpha-Aktivität im limbischen System ein Korrelat der Symptomschwere bei Patienten mit Depression ist und als potentieller Biomarker für moderne Stimulationsverfahren wie der bedarfsgerechten (closed-loop) tiefen Hirnstimulation verwendet werden könnte.The role of distinct limbic areas in emotion regulation has been largely inferred from neuroimaging studies. Recently, the opportunity for intracranial recordings from limbic areas has arisen in patients undergoing deep brain stimulation (DBS) for neuropsychiatric disorders including major depressive disorder (MDD) and obsessive compulsive disorder (OCD). Here we test the hypothesis that distinct temporal patterns of local field potential (LFP) activity in the human limbic system reflect disease state and symptom severity in MDD and OCD patients. To this end, we recorded LFPs via implanted DBS electrodes from the bed nucleus of stria terminalis (BNST area) in 12 patients (5 OCD, 7 MDD) and from the subgenual cingulate cortex in 7 MDD patients (CG25 area). We found a distinct pattern of oscillatory activity with significantly higher α-power in MDD compared with OCD in the BNST area (broad α-band 8–14 Hz; P = 0.01) and a similar level of α-activity in the CG25 area as in the BNST area in MDD patients. The mean α-power correlated with severity of depressive symptoms as assessed by the Beck depression inventory in MDD (n = 14, r = 0.55, P = 0.042) but not with severity of obsessive compulsive symptoms in OCD. Here we show larger α-band activity in MDD patients compared with OCD recorded from intracranial DBS targets. Our results suggest that α-activity in the limbic system may be a signature of symptom severity in MDD and may serve as a potential state biomarker for closed loop DBS in MDD

Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation.

Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Assessment of myelination in infants and young children by T1 relaxation time measurements using the magnetization-prepared 2 rapid acquisition gradient echoes sequence

Background: Axonal myelination is an important maturation process in the developing brain. Increasing myelin content correlates with the longitudinal relaxation rate (R1=1/T1) in magnetic resonance imaging (MRI). Objective: By using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) on a 3-T MRI system, we provide R1 values and myelination rates for infants and young children. Materials and methods: Average R1 values in white and grey matter regions in 94 children without pathological MRI findings (age range: 3 months to 6 years) were measured and fitted by a saturating-exponential growth model. For comparison, R1 values of 36 children with different brain pathologies are presented. The findings were related to a qualitative evaluation using T2, magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and MP2RAGE. Results: R1 changes rapidly in the first 16 months of life, then much slower thereafter. R1 is highest in pre-myelinated structures in the youngest subjects, such as the posterior limb of the internal capsule (0.74-0.76 +/- 0.04 s(-1)) and lowest for the corpus callosum (0.37-0.44 +/- 0.03 s(-1)). The myelination rate is fastest in the corpus callosum and slowest in the deep grey matter. R1 is decreased in hypo- and dysmyelination disorders. Myelin maturation is clearly visible on MP2RAGE, especially in the first year of life. Conclusion: MP2RAGE permits a quantitative R1 mapping method with an examination time of approximately 6 min. The age-dependent R1 values for children without MRI-identified brain pathologies are well described by a saturating-exponential function with time constants depending on the investigated brain region. This model can serve as a reference for this age group and to search for indications of subtle pathologies. Moreover, the MP2RAGE sequence can also be used for the qualitative assessment of myelinated structures

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Subthalamic beta band suppression reflects effective neuromodulation in chronic recordings

Background and purpose: Biomarkers for future adaptive deep brain stimulation still need evaluation in clinical routine. Here, we aimed to assess stimulation-induced modulation of beta-band activity and clinical symptoms in a Parkinson's disease patient during chronic neuronal sensing using a novel implantable pulse generator. Methods: Subthalamic activity was recorded OFF and ON medication during a stepwise increase of stimulation amplitude. Off-line fast fourier transfom -based analysis of beta-band activity was correlated with motor performance rated from blinded videos. Results: The stepwise increase of stimulation amplitude resulted in decreased beta oscillatory activity and improvement of bradykinesia. Mean low beta-band (13-20 Hz) activity correlated significantly with bradykinesia (ρ = 0.662, p < 0.01). Conclusions: Motor improvement is reflected in reduced subthalamic beta-band activity in Parkinson's disease, supporting beta activity as a reliable biomarker. The novel PERCEPT neurostimulator enables chronic neuronal sensing in clinical routine. Our findings pave the way for a personalized precision-medicine approach to neurostimulation

The Phenomenon of Exquisite Motor Control in Tic Disorders and its Pathophysiological Implications

The unifying characteristic of movement disorders is the phenotypic presentation of abnormal motor outputs, either as isolated phenomena or in association with further clinical, often neuropsychiatric, features. However, the possibility of a movement disorder also characterized by supranormal or enhanced volitional motor control has not received attention. Based on clinical observations and cases collected over a number of years, we here describe the intriguing clinical phenomenon that people with tic disorders are often able to control specific muscle contractions as part of their tic behaviors to a degree that most humans typically cannot. Examples are given in accompanying video documentation. We explore medical literature on this topic and draw analogies with early research of fine motor control physiology in healthy humans. By systematically analyzing the probable sources of this unusual capacity, and focusing on neuroscientific accounts of voluntary motor control, sensory feedback, and the role of motor learning in tic disorders, we provide a novel pathophysiological account explaining both the presence of exquisite control over motor output and that of overall tic behaviors. We finally comment on key questions for future research on the topic and provide concluding remarks on the complex movement disorder of tic behaviors

Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson's disease

INTRODUCTION Subthalamic DBS in Parkinson's disease has been associated with cognitive decline in few cases. Volume reduction of the nucleus basalis of Meynert (NBM) seems to precede cognitive impairment in Parkinson's disease. In this retrospective study, we evaluated NBM volume as a predictor of cognitive outcome 1 year after subthalamic DBS. METHODS NBM volumes were calculated from preoperative MRIs using voxel-based morphometry. Cognitive outcome was defined as the relative change of MMSE or DemTect scores from pre-to 1 year postoperatively. A multiple linear regression analysis adjusted for the number of cognitive domains affected in the preoperative neuropsychological testing and UPDRS III was conducted. To account for other variables and potential non-linear effects, an additional machine learning analysis using random forests was applied. RESULTS 55 patients with Parkinson's disease (39 male, age 61.4 ± 7.5 years, disease duration 10.8 ± 4.7 years) who received bilateral subthalamic DBS electrodes at our center were included. Although overall cognition did not change significantly, individual change in cognitive abilities was variable. Cognitive outcome could be predicted based on NBM size (B = 208.98, p = 0.022*) in the regression model (F(3,49) = 2.869; R2 of 0.149; p = 0.046*). Using random forests with more variables, cognitive outcome could also be predicted (average root mean squared error between predicted and true cognitive change 11.28 ± 9.51, p = 0.039*). Also in this model, NBM volume was the most predictive variable. CONCLUSION NBM volume can be used as a simple non-invasive predictor for cognitive outcome after DBS in Parkinson's disease, especially when combined with other clinical parameters that are prognostically relevant

Dopamine-dependent scaling of subthalamic gamma bursts with movement velocity in patients with Parkinson’s disease

Gamma synchronization increases during movement and scales with kinematic parameters. Here, disease-specific characteristics of this synchronization and the dopamine-dependence of its scaling in Parkinson’s disease are investigated. In 16 patients undergoing deep brain stimulation surgery, movements of different velocities revealed that subthalamic gamma power peaked in the sensorimotor part of the subthalamic nucleus, correlated positively with maximal velocity and negatively with symptom severity. These effects relied on movement-related bursts of transient synchrony in the gamma band. The gamma burst rate highly correlated with averaged power, increased gradually with larger movements and correlated with symptom severity. In the dopamine-depleted state, gamma power and burst rate significantly decreased, particularly when peak velocity was slower than ON medication. Burst amplitude and duration were unaffected by the medication state. We propose that insufficient recruitment of fast gamma bursts during movement may underlie bradykinesia as one of the cardinal symptoms in Parkinson’s disease

Lead-OR: A multimodal platform for deep brain stimulation surgery

Background: Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods: Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS